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Colour measurement

© Woodhead Publishing Limited, 2010

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The Textile Institute and Woodhead Publishing

The Textile Institute is a unique organisation in textiles, clothing and footwear. Incorporated in England by a Royal Charter granted in 1925, the Institute has individual and corporate members in over 90 countries. The aim of the Institute is to facilitate learning, recognise achievement, reward excellence and disseminate information within the global textiles, clothing and footwear industries.

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A list of Woodhead books on textile science and technology, most of which have been published in collaboration with The Textile Institute, can be found on pages xv–xxi.


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Woodhead Publishing Series in Textiles: Number 103

Colour measurementPrinciples, advances

and industrial applications

Edited byM. L. Gulrajani

Oxford Cambridge Philadelphia New Delhi

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Published by Woodhead Publishing Limited in association with The Textile InstituteWoodhead Publishing Limited, Abington Hall, Granta Park, Great AbingtonCambridge CB21 6AH, UKwww.woodheadpublishing.com

Woodhead Publishing, 525 South 4th Street #241, Philadelphia, PA 19147, USA

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Contents

Contributor contact details xiWoodhead Publishing Series in Textiles xv

Part I Theories, principles and methods of measuring colour 1

1 Colour vision: theories and principles 3 V. V. PÉREZ, D. DE FEZ SAIZ and F. MARTINEZ VERDÚ, University of Alicante, Spain

1.1 Introduction 31.2 Human colour vision 61.3 Chromatic perception 101.4 Defective colour vision 121.5 Colour constancy 151.6 Bibliography 17

2 Scales for communicating colours 19 A. K. ROY CHOUDHURY, Government College of Engineering and

Textile Technology, India

2.1 Introduction 192.2 Systematic arrangements of colours 222.3 Colour order systems 232.4 Various colour order systems 312.5 Comparison and interrelation of various systems 512.6 Accuracy of colour order systems 542.7 Computer-based systems 542.8 Universal colour language (UCL) 612.9 Future trends 632.10 References 65

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3 Expressing colours numerically 70 V. C. GUPTE, Advanced Graphic Systems, India

3.1 Introduction 703.2 Colour specifi cations 703.3 The Commission Internationale de l’Eclairage (CIE) system 723.4 The CIE standard light sources/illuminants 723.5 The CIE Standard Observer and unreal primaries 743.6 Computation of tristimulus values 773.7 Refl ectance measurement 793.8 Chromaticity coordinates and chromaticity diagram 803.9 Usefulness of the CIE XYZ system 813.10 Limitations of the CIE system 823.11 Transformation and improvement of the CIE system 823.12 Future trends 863.13 References 86

4 Visual and instrumental evaluation of whiteness and yellowness 88 R. HIRSCHLER, SENAI/CETIQT Colour Institute, Brazil

4.1 Introduction: whiteness and yellowness 884.2 Visual assessment of whiteness 904.3 Measuring techniques and instruments 954.4 Indices for whiteness and yellowness 1004.5 Applications in industry, cosmetics and dentistry 1114.6 Future trends 1154.7 Sources of further information and advice 1174.8 References 119

5 Use of artifi cial neural networks (ANNs) in colour measurement 125

M. SENTHILKUMAR, PSG College of Technology, India

5.1 Introduction 1255.2 Artifi cial neural networks (ANNs): basic principles 1265.3 Architecture of an artifi cial neural network 1275.4 Learning process 1295.5 Feed-forward neural network 1305.6 Training of an artifi cial neural network using back

propagation algorithm 1305.7 Application of artifi cial neural networks to colour measurement 1325.8 Recipe prediction 1355.9 Evaluation of the ANN method 1405.10 Case studies 140

Contents vii

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5.11 Future trends 1415.12 Sources of further information and advice 1445.13 References 144

6 Camera-based colour measurement 147 F. MARTÍNEZ-VERDÚ, E. CHORRO and E. PERALES, University of

Alicante, Spain, M. VILASECA and J. PUJOL, Technical University of Catalonia, Spain

6.1 Introduction 1476.2 Principles of camera-based colour measurement 1496.3 Procedures of camera-based colour measurement 1516.4 Strengths and weaknesses 1546.5 Case studies 1586.6 Future trends 1626.7 Conclusions 1636.8 Sources of further information and advice 1636.9 References 163

7 Colour shade sorting 167 M. L. GULRAJANI, India Institute of Technology, India

7.1 Introduction 1677.2 (555) Fixed-grid shade sorting system 1687.3 Clemson Colour Clustering 1747.4 K-means clustering 1787.5 Modifi ed CCC shade sorting method 1807.6 Shade sequencing and clustering 1807.7 References 182

8 Determining uncertainty and improving the accuracy of color measurement 184

J. A. LADSON, Color Science Consultancy, USA

8.1 Introduction to determining uncertainty 1848.2 Uncertainty 1858.3 Defi nitions 1868.4 Tables of results 1878.5 Conclusions: determining uncertainty 1898.6 Improving accuracy: the absolute correction of

instrumentally generated spectrometer values 1908.7 Introduction to improving accuracy 1908.8 Experimental modeling 1918.9 Applications 1938.10 Conclusions: improving accuracy 1948.11 References 195

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9 Colour measurement and fastness assessment 196 M. BIDE, Department of Textiles, Fashion Merchandising and

Design, University of Rhode Island, USA

9.1 Introduction: colour and colourfastness 1969.2 The use and usefulness of colourfastness testing 1979.3 Colourfastness test method development 1999.4 Colourfastness test standard setting organizations 2009.5 Standard colourfastness test format 2019.6 Testing for colourfastness: specifi c tests 2039.7 Colourfastness testing: assessment of results

(colour measurement) 2079.8 Conclusions 2169.9 References 216

Part II Colour measurement and its applications 219

10 Colour measurement methods for textiles 221 N. S. GANGAKHEDKAR, Compute Spectra Color Pvt. Ltd., India

10.1 Introduction 22110.2 Colour as numbers 22210.3 Colour specifi cation 22410.4 Metamerism 22610.5 Reasons why colours do not match 23110.6 Visual versus numerical pass/fail 23210.7 Colour measurement techniques for textiles 23610.8 On-line colour measurement 24210.9 Colour of dry and wet fabrics 24710.10 Inspection of colour of fi nished fabrics: a case study 24810.11 Future trends 25010.12 Conclusions 25010.13 Sources of further information and advice 25110.14 References 251

11 Grading of cotton by color measurement 253 B. XU, The University of Texas, USA

11.1 History of cotton color grading 25311.2 USDA cotton color grades 25311.3 HVI colorimeter 25411.4 Factors affecting cotton color grade 25511.5 Color measurement using color image analysis 25911.6 Using neural networks 26311.7 Using fuzzy logic 26811.8 Conclusions 27611.9 References 277

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12 Colour measurement of paint fi lms and coatings 279 N. S. GANGAKHEDKAR, Compute Spectra Color Pvt. Ltd., India

12.1 Introduction 27912.2 Quality control of paints 28012.3 Sample preparation for colour measurement 29112.4 Pigment quality control 29212.5 Problems in match prediction: paint applications 29512.6 Computer colour matching for paints 29512.7 Colour control system 29712.8 Measuring colour properties of wet paints 29912.9 Instant colour matching at the paint shop 30012.10 Colour matching of automotive paints 30712.11 Future trends 30912.12 Conclusions 30912.13 Sources of further information and advice 31012.14 References 310

13 Colour measurement of food: principles and practice 312 D. B. MACDOUGALL, Formerly of the University of Reading, UK

13.1 Introduction 31213.2 Colour vision: trichromatic detection 31313.3 The infl uence of ambient light and food structure 31613.4 Appearance 31713.5 Absorption and scatter 31813.6 Colour description: the CIE system 31913.7 Colour description: uniform colour space 32013.8 Instrumentation 32513.9 Food colour appearance measurement in practice 32713.10 Illuminant spectra and uniform colour 33613.11 Conclusions and future trends 33713.12 References 339

14 Colorimetric evaluation of tooth colour 343 A. JOINER, Unilever Oral Care, UK

14.1 Introduction 34314.2 The human dentition and its environment 34414.3 Optical properties of teeth 34514.4 The colour of teeth 34714.5 Factors that impact tooth colour and its perception 34814.6 Tooth whiteness 35114.7 Measurement of tooth colour 35214.8 Measurement of extrinsic stain 35614.9 Methods to improve tooth colour 35714.10 Future trends 361

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14.11 Sources of further information and advice 36214.12 References 363

15 Hair color measurement 371 D. J. TOBIN, University of Bradford, UK

15.1 Introduction 37115.2 Background 37115.3 Natural hair color 37315.4 Gray hair and age 37915.5 Effect of environment 38215.6 Artifi cial hair coloring shades 38315.7 Color measurement methods and instruments 38615.8 Future trends 38815.9 Sources of further information and advice 38815.10 References 388

Index 393


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Contributor contact details

(* = main contact)

Chapter 1Valentín Viqueira Pérez,* Dolores de

Fez Saiz and Dr Francisco Martínez-Verdú

Department of Optics, Pharmacology and Anatomy

Faculty of SciencesUniversity of AlicanteAlicanteSpain

E-mail: [email protected]

Chapter 2Professor (Dr) A. K. Roy

ChoudhuryGovt College of Engineering and

Textile TechnologySerampore-712201Hooghly (W.B.)India


Chapter 3V. C. GupteAdvanced Graphic Systems601/602, Trade WorldB Wing, Kamla CitySenapati Bapat Marg, Lower ParelMumbai-400013India


Chapter 4Dr Robert HirschlerSENAI/CETIQT Colour InstituteRua Dr. Manuel Cotrim195 Rio de JaneiroBrazil, 20961-040


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xii Contributor contact details

Chapter 5M. SenthilkumarDepartment of Textile TechnologyPSG College of TechnologyPeelameduCoimbatore-641004Tamil Nadu, India


Chapter 6Francisco Martínez-Verdú*, Elisabet

Chorro, Esther PeralesDepartment of Optics,

Pharmacology and AnatomyUniversity of AlicanteCarretera de San Vicente del

Raspeig s/n, 03690 – Alicante (Spain)

E-mail: [email protected]; [email protected]; [email protected]

Meritxell Vilaseca, Jaume PujolCenter for Sensors, Instruments

and Systems Development (CD6)

Technical University of CataloniaRambla de Sant Nebridi 10, 08222

– Terrassa (Spain)

E-mail: [email protected]; [email protected]

Chapter 7Dr M. L. GulrajaniDepartment of Textile TechnologyIndia Institute of TechnologyNew Delhi-110016India


Chapter 8Jack A. LadsonColor Science Consultancy1000 Plowshare RoadYardley, PA 19067USA


Chapter 9Dr Martin BideDepartment of Textiles, Fashion

Merchandising and DesignUniversity of Rhode IslandRI 02881USA


Chapter 10Dr N. S. GangakhedkarCompute Spectra Color Pvt. Ltd.,

India306, So Lucky CornerChakala, Andheri EastMumbai-400 099India


Chapter 11Dr B. Xu, ProfessorThe University of TexasSchool of Human EcologyUniversity of Texas at AustinGearing Hall 225Austin, TX 78712USA


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Contributor contact details xiii

Chapter 12Dr N. S. GangakhedkarCompute Spectra Color Pvt. Ltd.,

India306, So Lucky CornerChakala, Andheri EastMumbai-400 099India


Chapter 13Dr D. B. MacDougallFormerly of the University of

ReadingWhiteknightsPO Box 217Reading RG6 6AHUK


Chapter 14Dr Andrew JoinerUnilever Oral CareQuarry Road East, BebingtonWirral CH63 3JWUK


Chapter 15Dr D. J. TobinCentre for Skin SciencesSchool of Life Sciences, University

of BradfordRichmond Road, BradfordWest Yorkshire BD7 1DPUK


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Woodhead Publishing Series in Textiles

1 Watson’s textile design and colour Seventh edition Edited by Z. Grosicki

2 Watson’s advanced textile design Edited by Z. Grosicki

3 Weaving Second edition P. R. Lord and M. H. Mohamed

4 Handbook of textile fi bres Vol 1: Natural fi bres J. Gordon Cook

5 Handbook of textile fi bres Vol 2: Man-made fi bres J. Gordon Cook

6 Recycling textile and plastic waste Edited by A. R. Horrocks

7 New fi bers Second edition T. Hongu and G. O. Phillips

8 Atlas of fi bre fracture and damage to textiles Second edition J. W. S. Hearle, B. Lomas and W. D. Cooke

9 Ecotextile ’98 Edited by A. R. Horrocks

10 Physical testing of textiles B. P. Saville

11 Geometric symmetry in patterns and tilings C. E. Horne

12 Handbook of technical textiles Edited by A. R. Horrocks and S. C. Anand

13 Textiles in automotive engineering W. Fung and J. M. Hardcastle

14 Handbook of textile design J. Wilson

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xvi Woodhead Publishing Series in Textiles

15 High-performance fi bres Edited by J. W. S. Hearle

16 Knitting technology Third edition D. J. Spencer

17 Medical textiles Edited by S. C. Anand

18 Regenerated cellulose fi bres Edited by C. Woodings

19 Silk, mohair, cashmere and other luxury fi bres Edited by R. R. Franck

20 Smart fi bres, fabrics and clothing Edited by X. M. Tao

21 Yarn texturing technology J. W. S. Hearle, L. Hollick and D. K. Wilson

22 Encyclopedia of textile fi nishing H-K. Rouette

23 Coated and laminated textiles W. Fung

24 Fancy yarns R. H. Gong and R. M. Wright

25 Wool: Science and technology Edited by W. S. Simpson and G. Crawshaw

26 Dictionary of textile fi nishing H-K. Rouette

27 Environmental impact of textiles K. Slater

28 Handbook of yarn production P. R. Lord

29 Textile processing with enzymes Edited by A. Cavaco-Paulo and G. Gübitz

30 The China and Hong Kong denim industry Y. Li, L. Yao and K. W. Yeung

31 The World Trade Organization and international denim trading Y. Li, Y. Shen, L. Yao and E. Newton

32 Chemical fi nishing of textiles W. D. Schindler and P. J. Hauser

33 Clothing appearance and fi t J. Fan, W. Yu and L. Hunter

34 Handbook of fi bre rope technology H. A. McKenna, J. W. S. Hearle and N. O’Hear

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Woodhead Publishing Series in Textiles xvii

35 Structure and mechanics of woven fabrics J. Hu

36 Synthetic fi bres: Nylon, polyester, acrylic, polyolefi n Edited by J. E. McIntyre

37 Woollen and worsted woven fabric design E. G. Gilligan

38 Analytical electrochemistry in textiles P. Westbroek, G. Priniotakis and P. Kiekens

39 Bast and other plant fi bres R. R. Franck

40 Chemical testing of textiles Edited by Q. Fan

41 Design and manufacture of textile composites Edited by A. C. Long

42 Effect of mechanical and physical properties on fabric hand

Edited by Hassan M. Behery

43 New millennium fi bers T. Hongu, M. Takigami and G. O. Phillips

44 Textiles for protection Edited by R. A. Scott

45 Textiles in sport Edited by R. Shishoo

46 Wearable electronics and photonics Edited by X. M. Tao

47 Biodegradable and sustainable fi bres Edited by R. S. Blackburn

48 Medical textiles and biomaterials for healthcare Edited by S. C. Anand, M. Miraftab, S. Rajendran and

J. F. Kennedy

49 Total colour management in textiles Edited by J. Xin

50 Recycling in textiles Edited by Y. Wang

51 Clothing biosensory engineering Y. Li and A. S. W. Wong

52 Biomechanical engineering of textiles and clothing Edited by Y. Li and D. X-Q. Dai

53 Digital printing of textiles Edited by H. Ujiie

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xviii Woodhead Publishing Series in Textiles

54 Intelligent textiles and clothing Edited by H. Mattila

55 Innovation and technology of women’s intimate apparel W. Yu, J. Fan, S. C. Harlock and S. P. Ng

56 Thermal and moisture transport in fi brous materials Edited by N. Pan and P. Gibson

57 Geosynthetics in civil engineering Edited by R. W. Sarsby

58 Handbook of nonwovens Edited by S. Russell

59 Cotton: Science and technology Edited by S. Gordon and Y-L. Hsieh

60 Ecotextiles Edited by M. Miraftab and A. R. Horrocks

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62 Plasma technology for textiles Edited by R. Shishoo

63 Smart textiles for medicine and healthcare Edited by L. Van Langenhove

64 Sizing in clothing Edited by S. Ashdown

65 Shape memory polymers and textiles J. Hu

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67 Nanofi bers and nanotechnology in textiles Edited by P. Brown and K. Stevens

68 Physical properties of textile fi bres Fourth edition W. E. Morton and J. W. S. Hearle

69 Advances in apparel production Edited by C. Fairhurst

70 Advances in fi re retardant materials Edited by A. R. Horrocks and D. Price

71 Polyesters and polyamides Edited by B. L. Deopura, R. Alagirusamy, M. Joshi and B. S. Gupta

72 Advances in wool technology Edited by N. A. G. Johnson and I. Russell

73 Military textiles Edited by E. Wilusz

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Woodhead Publishing Series in Textiles xix

74 3D fi brous assemblies: Properties, applications and modelling of three-dimensional textile structures

J. Hu

75 Medical and healthcare textiles Edited by S. C. Anand, J. F. Kennedy, M. Miraftab and S. Rajendran

76 Fabric testing Edited by J. Hu

77 Biologically inspired textiles Edited by A. Abbott and M. Ellison

78 Friction in textile materials Edited by B. S. Gupta

79 Textile advances in the automotive industry Edited by R. Shishoo

80 Structure and mechanics of textile fi bre assemblies Edited by P. Schwartz

81 Engineering textiles: Integrating the design and manufacture of textile products

Edited by Y.E. El-Mogahzy

82 Polyolefi n fi bres: Industrial and medical applications Edited by S. C. O. Ugbolue

83 Smart clothes and wearable technology Edited by J. McCann and D. Bryson

84 Identifi cation of textile fi bres Edited by M. Houck

85 Advanced textiles for wound care Edited by S. Rajendran

86 Fatigue failure of textile fi bres Edited by M. Miraftab

87 Advances in carpet technology Edited by K. Goswami

88 Handbook of textile fi bre structure Volume 1 and Volume 2 Edited by S. J. Eichhorn, J. W. S. Hearle, M. Jaffe and T. Kikutani

89 Advances in knitting technology Edited by K-F. Au

90 Smart textile coatings and laminates Edited by W. C. Smith

91 Handbook of tensile properties of textile and technical fi bres Edited by A. R. Bunsell

92 Interior textiles: Design and developments Edited by T. Rowe

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xx Woodhead Publishing Series in Textiles

93 Textiles for cold weather apparel Edited by J. T. Williams

94 Modelling and predicting textile behaviour Edited by X. Chen

95 Textiles, polymers and composites for buildings Edited by G. Pohl

96 Engineering apparel fabrics and garments J. Fan and L. Hunter

97 Surface modifi cation of textiles Edited by Q. Wei

98 Sustainable textiles Edited by R. S. Blackburn

99 Advances in textile technology Edited by C. A. Lawrence

100 Handbook of medical textiles Edited by V. T. Bartels

101 Technical textile yarns Edited by R. Alagirusamy and A. Das

102 Applications of nonwovens in technical textiles Edited by R. A. Chapman

103 Colour measurement: Principles, advances and industrial applications

Edited by M. L. Gulrajani

104 Textiles for civil engineering Edited by R. Fangueiro

105 New product development in textiles Edited by B. Mills

106 Improving comfort in clothing Edited by G. Song

107 Advances in textile biotechnology Edited by V. A. Nierstrasz and A. Cavaco-Paulo

108 Textiles for hygiene and infection control Edited by B. McCarthy

109 Nanofunctional textiles Edited by Y. Li

110 Joining textiles: Principles and applications Edited by I. Jones and G. Stylios

111 Soft computing in textiles Edited by A. Majumdar

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Woodhead Publishing Series in Textiles xxi

112 Textile design Edited by A. Briggs-Goode and K. Townsend

113 Biotextiles as medical implants Edited by M. King and B. Gupta

114 Textile thermal bioengineering Edited by Y. Li

115 Woven textile structure B. K. Behera and P. K. Hari

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Part I

Theories, principles and methods of measuring colour

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2 Colour measurement


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1Colour vision: theories and principles

V. VIQUEIRA PÉREZ, D. DE FEZ SAIZ and F. MARTINEZ VERDÚ, University of Alicante, Spain

Abstract: When viewing any scene, the human visual system is able to extract information regarding light wavelength, which is why we see in colour. This chapter discusses the mechanisms of human colour vision. The chapter fi rst reviews the anatomy and the physiology of the visual system, and then describes the generic ATD models of colour vision. From these models, the chapter discusses the topics of colour appearance, colour constancy, and defective colour vision.

Key words: ATD models, colour appearance, defective colour vision, colour constancy, mechanisms of chromatic adaptation.

1.1 Introduction

1.1.1 Advantages of colour vision

When viewing any scene, the human visual system is able to extract information regarding light wavelength, which is why we see in colour. But what advantages does this ability give to human vision? In evolutionary terms, the advantages for our ancestors are clear: seeing in colour makes it easier to detect food, such as the colour of fruit against the green of a leafy background, and the ability to detect animals hidden from view, be they predators or possible prey.

Today, considering the fact that people with chromatic anomalies or defi ciencies are able to lead a normal life, we would tend to think that colour vision is not a relevant factor. But consider the sheer amount of information that surrounds us, and how much of it is based on colour – is it not surprising to realise that most information is actually colour-coded? Traffi c signs, advertising, graphic design, the internet. … Not only will people who have diffi culties seeing in colour not be qualifi ed for certain jobs, it could also mean that they do not properly recognise information surrounding them in their normal life.

1.1.2 Anatomy and physiology of the human visual system (rods; L, M and S cones LGN and cortical areas)

The visual system is the part of the brain that makes us see. It does this by interpreting information from available light to build a representation of the outside world. The process begins when light passes through the eye’s transparent elements and hits the retina. All the elements involved before the retina form the ocular optic system (optically, the eye consists of a series of refractive surfaces

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defi ned by transitions between air, fl uid and solid tissues, the study of which is not the purpose of this book).

The human eye is roughly spherical in shape (Fig. 1.1), and is made up of three distinct layers of tissue: sclerotic coat, choroid coat and retina. The sclerotic coat is the outer layer. It is white and extremely tough, except in the front where it forms the transparent cornea which contributes to the image-forming process by refracting light entering the eye. The surface of the cornea is kept moist and dust-free by the secretion from the tear glands. The intermediate layer is the choroid coat. This layer is deeply pigmented with melanin that reduces refl ection of stray light within the eye. The choroid coat forms the iris, a diaphragm of variable size whose function is to adjust the size of the pupil to regulate the amount of light admitted into the eye. The pupil contraction is under the control of the autonomic nervous system: in dim light, the pupil opens wider letting more light into the eye; in bright light the pupil closes down. The retina is the inner layer of the eye. It contains the light receptors, the rods and cones.

Inside the eye, the cavity between the lens and cornea – called the anterior chamber – is fi lled with a gel-like fl uid called aqueous. The lens is located just behind the iris. The lens is a fl exible unit that consists of layers of tissue enclosed in a tough capsule. It is suspended from the ciliary muscles by the zonule fi bres. Behind the lens is the vitreous: a thick, transparent substance that fi lls the back of the eye. It is composed mainly of water and comprises about two-thirds of the eye’s volume, giving it form and shape. The next element is the retina, a neurosensory layer that initiates the neural processes of vision.

An inverted image of the outside world is projected onto the retina, and the visual system then has the complex task of rebuilding a three-dimensional image

1.1 Human eye structure.

Sclera Choroid

Retina

Fovea

Optic nerve

Ciliary body

Iris

Lens

Pupil

Cornea

Colour vision: theories and principles 5

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from this two-dimensional projection. The end result of this process is visual perception.

The retina is in fact a prolongation of the brain. Anatomically, it is structured in ten layers for different types of neurones: photoreceptors (cones and rods), which are in fact modifi ed neurones, and horizontal, bipolar, amacrine and ganglion cells.

However, the photoreceptor’s mosaic is not evenly distributed along the retina. In physiological terms, it has a central zone and an outer area. The central zone includes the fovea, a small pit that ensures best visual acuity. The fovea is located in an area known as the macula lutea (Latin for yellow spot), which takes its name from the yellow pigment that covers it. The central retina includes another region, the optic disc, at the exit of the optic nerve, formed by the ganglion cell axons leaving the eyeball to form the optic nerve. This elongated pink disc is located in the nasal area and usually measures around 1.5 mm2. It has no photoreceptors, and as a result forms a blind spot in our vision, where we cannot see (since there are no cells to detect light, this part of the fi eld of vision is not perceived). The rest, named the peripheral retina, has a low concentration of photoreceptors.

The retina is inverted, in such a way that light passes through its entire structure before hitting the photosensitive layer. A photoreceptor is a specialist neurone capable of performing phototransduction, by which light is converted into electrical signals, which are then transmitted from neurone to neurone. Cones and rods are anatomically and physiologically different. Cones are much more abundant in the central retina, so the fovea contains only cones and, more importantly, the cones connect with the bipolar and ganglion cells at a proportion of 1:1:1, whereas in the peripheral retina area, several hundred or thousands of cones converge to a single bipolar cell. This explains why visual acuity is so much sharper in the fovea than at any other point on the retina.

In terms of light sensitivity, rods can function in low light, whereas cones need much higher levels of light. So, rods are responsible for night vision, and cones are used for daytime vision, seeing colours and visual acuity.

When light hits a photoreceptor, it sends a proportional synaptic response to a bipolar cell, which in turn sends the signal to a ganglion cell. Furthermore, there are two substrata at a synaptic level, with horizontal cells and amacrine cells (see Plate I in colour section between pages 42 and 43). These modify the initial signal to an extent that, after two or three synapses, the signal contains much more complex information than a simple point-to-point visual representation. From the ganglion cells, the signal reaches the appropriate left and right lateral geniculate nuclei (LGNs). The function of the LGNs is complex. We know that there is a three-system separation of fi bres (PC cells, MC cells and KC cells), relating to Magno, Parvo and Konio systems. The LGNs have six layers, numbered from 1 (innermost layer) to 6 (outermost layer). Layers 1 and 2 contain the largest cell bodies and constitute the magnocellular system, whereas layers 3, 4, 5 and 6, which have smaller neurones, are part of the parvocellular system. Layers 2, 3


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and 5 receive fi bres from the homolateral eye, and layers 1, 4 and 6 from the contralateral eye. Between each layer are the KC cells, which belong to the koniocellular system. Eighty per cent of the cells form part of the parvo system, ten per cent belong to the magno system, and ten per cent to the konio system.

The LGN signals are sent to the primary visual cortex (V1), located at the back of the brain. The V1 fi bres are then projected to area V2, and from V2 to V3, V4 and V5. Another important fact is that more than half of the LGN and V1 neurones process information from the fovea. A process therefore exists that gives priority to the part of the scene that is projected onto the fovea (the fi xation point).

1.2 Human colour vision

1.2.1 Chromatic stimulus and perceived colour

When we observe a scene, our visual system creates a perception of the outside world from the radiating energy that reaches our eyes from the objects within that scene. One of the elements of that perception is colour. But where is the colour? Is it external, or is it inside our visual system? The answer is clear: there are different chromatic stimuli in any scene that we view, and our visual system is able to capture that information, relating to the wavelength of light, which is how colour ‘emerges’. Colour, therefore, is something internal. Colour is perception.

The chromatic stimulus is electromagnetic radiation from sources and objects that hits the optic system and triggers the visual process. Perceived colour, therefore, is a sensation produced by the chromatic stimulus that makes it possible to differentiate that stimulus from others with the same area, duration, shape and texture.

As we shall see, chromatic stimuli have three variables, which are directly related to three perceptual variables.

1.2.2 Models of chromatic vision

The trichromatic theory (Young–Helmholtz–Maxwell)

In the early nineteenth century, Thomas Young (1801) suggested that the retina contained three types of nerve fi bres that can be stimulated to a greater or lesser extent by the different wavelengths that correspond to red, green and violet colours. Several years later, James C. Maxwell would demonstrate that any colour in the spectrum could be matched with three monochromatic primary colours: red, green and blue. These two ideas were concurrent, and gave a clear, simple explanation of colour vision. Thus, a colour that stimulates the red and green particles in equal measure will be perceived as yellow; if it stimulates green and violet, we see blue. Chromatic vision is simply a matter of additive mixing. At around the same time, the German physicist Hermann V. Helmholtz also suggested that subjects with defi cient colour vision (dichromats) had reduced


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forms of normal vision, which lacked one of the three receptor types. This explains the fact that dichromats accept as equal the same kind of matchings as do subjects with normal vision.

However, there were weak points in the theory in terms of colour appearance: if everything can be explained through mixes of colours, why are there no reddish greens or bluish yellow colours?

Theory of chromatic opponency

Ewald Hering (1872–4) was more concerned with the appearance of colours. In his reports to the Imperial Academy of Sciences in Vienna, Hering proposed the existence of three opponent processes generated at some point in the visual process: red-green, blue-yellow and black-white mechanisms.

The idea of opponency is that the red-green categories (and, similarly, the blue-yellow and light-shadow categories) are opposed and represent two extremes of variations in a continuum. More red necessarily implies less green. Despite having no experimental proof, Hering challenged both the established trichromatic theory and the prevailing physiological theory (Müller’s doctrine of specifi c nerve energies) head-on: ‘a nerve fi bre is capable of conducting only one type of qualitative information’. A nerve fi bre, therefore, could not transmit both red and green information, as they are qualitatively different.

These two theories, which in principle seemed to take opposing views, are in fact compatible; the three retina cone types would in fact correspond to trichromacy, and the sensor responses by these cones would then be combined in the three red-green, blue-yellow and light-shadow mechanisms (although, as we shall see, dark-light is an additive rather than an opposing mechanism). It was not until 1957, a hundred years later, that D. Jameson and L. M. Hurvich would prove the existence of the opposing mechanisms. Paradoxically, Hering himself had suggested that these two theories did not have to be necessarily incompatible.

Today we know that the parvo cells are responsible for the red-green mechanisms (L – M cells and M – L cells), and the konio cells are responsible for the blue-yellow mechanisms S – (L + M) cells. Magno cells, on the other hand, are not opposing cells, but rather add the inputs from the L, M and S cells. Therefore it is an additive mechanism.

ATD models (two-stage models)

Each of these previous theories can explain a series of phenomena: the trichromatic theory reproduces many psychophysical experiments, but it does not predict appearance, which the colour opponency theory does. Combining the two theories leads to models that combine an initial trichromatic stage at the receptor level (fi rst stage), with a stage of neural processing ruled by colour opponency mechanisms (second stage), comprising a non-opposing luminance


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mechanism (A) and the two opposing chromatic mechanisms: green-red (T) and blue-yellow (D). To a certain extent, the CIELAB values, which are very useful for industrial colorimetry, represent these concepts as Lightness (L) and can be linked to the achromatic channel (A); the chromatic coordinate a* to the greenness-redness channel (T) and the chromatic coordinate b* to the yellowness-blueness channel (D).

The model as a whole is summed up in an ATD matrix resulting from the combination of the L, M and S cone signals, as shown in equation 1.1. The three independent channels are listed as A (achromatic), T (tritan) and D (deutan):

A a11 a12 a13 L

T = a21 a22 a23 * M [1.1]

D a31 a32 a33 S

The Boynton model (1986) is a good representation of these two-stage models. It proposes three cone types, the spectral response curves of which are the Smith and Pokorny fundamentals. The coeffi cients for the resulting matrix are shown in equation 1.2, and the general functioning for this model is summarised in colour Plate II. The illustration also shows how the S cones contribute to the achromatic channel, though this value is very low and in the matrix is worth zero.

A 1 1 0 L

T = 1 −2 0 * M [1.2]

D 1 1 −1 S

Why does the matrix have these coeffi cients? If we look at the single 575 nm yellow in the Smith and Pokorny fundamentals (Fig. 1.2), (L – M) would be a positive value, i.e. red would predominate over green, and we would see a reddish orange colour. However, the M cones’ response becomes balanced by applying a factor of two, so that in 575 nm they are cancelled out (T = 0) and a pure yellow colour is perceived. The red-green channel gives no signal (T = 0), and the blue-yellow channel gives a response tending towards yellow.

Another justifi cation for this factor of two is in the proportion of cones in the retina: L:M = 10:5. According to this model, there is also a small intrusion of the S cones in the RG channel, though generally it is not taken into consideration.

The oponnent process yellow-blue receives an amplifi ed signal with a negative sign from relatively small values of the S cone, and a positive signal from the L and M cones: (L + M) – S. If S > L + M, the channel’s response is negative and is interpreted in upper states of the visual process as blue. But if S < L + M, the channel’s response is positive and the colour is interpreted as yellow. For lengths above 520 nm, the S cones give no signal; the channel signal will always be positive and interpreted as yellow. Finally, we can state that the L + M signal is transmitted by different nerve fi bres to those that transmit the L – 2M signal.


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Almost all chromatic vision models largely coincide in this general structure that we have just seen for the Boynton model: there are two opposing channels (channels T and D), and one additive channel (no opponent) for luminance (channel A). The differences are in the matrix coeffi cients. However, there are certain points that have not been fully clarifi ed:

Channel T: There is L – M opponency, but there are doubts over contribution from the S cones. If there is a contribution, it would be of the (L + S) – M type.

Channel D: There is L – S opponency, but there are doubts over contribution from the M cones to this channel.

Channel D: The achromatic channel is L + M, but there are doubts over contribution from the S cones to this channel. If there is a contribution, it would be of the L + M + S additive type. Nevertheless, most of the experimental results point to such a contribution not existing.

Other more complex models, also exist, such as the De Valois-De Valois (1993) model. This considers a second opposing stage of cones (ATDinterm) that generates three colour opponent signals (rather than two) at the LGN cell level, corresponding approximately to L – M, M – L and S – (L + M). A third perceptual (linear) opposing stage would then occur, generating one achromatic and two chromatic channels. Finally, there are other non-linear models that succeed in explaining effects resulting from adaptation to a light source, and the infl uence of the background and the environment surrounding the stimulus, etc. In this case, non-linearities need to

40

30

20

10

0

–10

–20

Log

sens

itivi

ty

Wavelength (nm)

S conesM conesL cones

–30

–40350 400 450 500 550 600 650 700 750

1.2 Smith and Pokorny fundamentals.


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be introduced into the ATD response calculation. The 1990 Guth model, and subsequent modifi cations up to 1995, are a good example of these models.

The general outline for these models is as follows (equation 1.3): from LMS an initial non-linearity is introduced, obtaining LMSs that are adapted to those conditions. Following this, the ATD exchange matrix and a second non-linearity are applied to obtain the adapted ATDs.

L L A A

M M T T [1.3]

S S D D

The non-linearities are normally of the following type: —xn— .

As always, the objective with this model is to explain chromatic vision by reproducing the behaviour of the physiological A, T and D mechanisms.

1.3 Chromatic perception

1.3.1 Chromatic discrimination

Colour differences are of enormous importance in industry. Understanding the human visual system’s degree of tolerance to colour differences is fundamental in knowing the maximum tolerable error in formulating a paint or printing a fabric. The most important aspect is not in fact the differences of the various components of colour (hue, colourfulness and lightness), but rather the perceptive differences of colour as a whole.

In 1934, Wright evaluated colour differences with constant illumination by means of a 2° bipartite fi eld, with 100 td retinal illumination throughout the whole spectrum and for fi ve directions of the chromaticity diagram. In this way, he determined intervals or segments with edges that represent a constant difference in chromaticity. As a result of this experiment, it was deduced that the CIE1931xy space had very little uniformity, as two very distant points in the area of greens differ in colour just as two very close points in the area of blues and purples do. In 1942, MacAdam took these studies further. Using a bipartite fi eld, a fi xed reference stimulus was placed in one of the two fi elds, and colour matching was carried out in the other; the test was for 2° and was surrounded by an adaptation fi eld of white light of the same luminance (200 td). After 50 matchings, two symmetrical points were defi ned in the same direction. By repeating this same operation for various directions and joining the points (within the standard deviations), an ellipse was generated. MacAdam showed the results in the CIE1931xy space for 25 reference colours.

The result of joining the experimental colour discrimination points is an ellipse. These ellipses are of different sizes, orientation and shape throughout the chromaticity diagram. The ellipses are smaller in the area of blues, of medium size

NL M NL

adap adap

a + xn


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in the reds and larger in the greens. By considering the distance from the centre of the ellipse to the edge as the unit of colour difference, it can be confi rmed that this distance is not the same from one ellipse to another; it can thus be deduced that the CIE1931xy space is not uniform. In a space of uniform representation, representing colour differences would obtain circumferences of equal radius, regardless of the centre colour chosen, rather than ellipses of different sizes and orientation. As a result of these works, attempts have been made to fi nd a more uniform colour representation space. The most commonly used are CIELAB, CIELUV, Guth’s ATD and SVF, among others.

1.3.2 Appearance of colour: chromatic effects

Colour is trivariant, which means that in an isolated colour stimulus, we can distinguish three separate qualities: hue, brightness and saturation. When one studies a related colour, i.e. one that forms part of a scene, the descriptors refer to the environment, and one thus speaks of hue, lightness and colourfulness. Similarly, any colour is defi ned by means of three physical variables that correspond to these three perceptive variables: wavelength to hue, luminance to brightness, and colorimetric purity to saturation (see Table 1.1). However, these relationships do not display absolute independence, but rather there is interference between parameters. A variation in a single parameter (L, Pc, λ) can affect not only the perceptual attribute to which it is associated, but the other two as well. On the other hand, environmental modifi cations also affect colour. These effects cannot be explained by a linear model, as occurs with characterisation by three-way stimulus values or by means of opponency. We thus know that there must be some subsequent process, and that it cannot be linear.

Studies of these chromatic effects are many and varied, and are beyond the scope of this work; we can merely provide a brief explanation of some of the better known and more widely considered of these chromatic effects.

• Bezold-Brücke effect: ‘A variation in luminance can alter the tone, thus changing its colour appearance.’ Bezold and Brücke (1873 and 1878) discovered this effect independently, and both indicated that at high luminance levels, reds and yellowish greens tended towards yellow, whereas greenish blues and violets became blue. However, they also confi rmed that there are

Table 1.1 Physical and perceptual colour descriptors for isolated and related colours

Chromatic stimuli Isolated colour Related colour

Wavelength (nm) Hue HueLuminance (cd/m2) Brightness LightnessColorimetric purity Colourfulness Chroma


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three hues that do not vary: yellow (571 nm), green (506 nm) and blue (474 nm). This effect shows that tone and luminosity attributes are not completely independent.

• Aubert-Abney effect: ‘By adding white to a purple or a monochromatic colour, not only is saturation reduced, but hue changes also occur.’ This effect can be seen in Munsell’s Atlas colour samples, with equal tone and different chroma in the xy diagram. Theoretically, they should be straight lines of constant λd, but instead a curve is obtained that becomes greater the nearer the samples are to the locus. Again, there are some exceptions to this behaviour: for 572 nm yellow and for a purple (0.240,0.035) (λc = 559 nm).

• Helmholtz-Kohlrausch effect: ‘In heterochromatic Matching and with identical luminance, Brightness varies with the chromaticity of the stimulus.’ With identical luminance levels, achromatic colours display lower luminosity than chromatic colours. And equally, the other way round, with identical luminosity levels, achromatic colours display greater luminance than chromatic colours. In any matching of a white with any colour, then (Lwhite /Lcolour) > 1 applies. This ratio nears 1 when the colour is more desaturated, and reaches values higher than 1.6 for monochromatic colours.

• Simultaneous contrast: An increase in the brightness of a stimulus with the simultaneous decrease of background luminance. A dark environment makes one grey seem lighter than another with a white environment.

• Crispening effect: An increase in the difference between two stimuli occurs if the background is similar to the stimuli; in this case the brightness differential threshold is lower.

• Apparent mix: When the spatial frequency of an object is high, a chromatic mix of background and stimulus is produced. This colour quality is applied in artistic painting, with its maximum expression in the pointillist movement. George Seurat used this technique; his brushstrokes are tiny points, with no merging between them on the canvas, yet when viewed from a certain distance they create the desired combinations on the retina. The same occurs with the pixels of a television screen.

1.4 Defective colour vision

1.4.1 Anomalies and defi ciencies in colour vision

Most people see colour in the same way, which we can call normal chromatic vision. However, other people’s sight behaves abnormally. In most of these cases, they are able to differentiate colours, but their chromatic vision is much poorer than that of someone with normal sight. There are many colours which for a normal observer are clearly different and which the person with chromatic defi ciency views as the same.

The reason can be explained with any ATD model, by considering the hypothesis that chromatic defi ciency is due to the lack of one of the three cone


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types. This lack of a cone type means a failure in either the T or the D chromatic mechanisms.

Consider the Boynton model. The chromatic channels are T = L – 2M and D = (L + M) – S. If one of the two terminals is cancelled out, the channel response will always be of the same sign. What happens if the L cone is missing? Channel D would function, but in an anomalous way (reduced to D = M – S), and its response would differ to that of the normal channel, but a positive or negative response would still be possible; however, channel T (T = – 2M) would always respond in the same direction. Clearly, chromatic vision would thus be greatly reduced. In the hypothetical case of two cone types being missing (L and M), neither chromatic channel could function, and the subject would have no chromatic vision of any kind. A person lacking one cone type is known as a dichromat. Given that there are three types (L, M and S), there can be three separate categories, known as protanopia (lacking L cones), deuteranopia (lacking M cones) and tritanopia (lacking S cones). Individuals with these defi ciencies have sight that differs between the three types. Table 1.2 includes all types of reduced colour vision currently reported.

As well as chromatic defi ciencies, another anomaly type also exists: people who have all three cone types, one of which has a slightly displaced curve response for that pigment. In this case, the person will also suffer from poor colour discrimination. These cases are known as protanomalous, deuteranomalous and tritanomalous.

In people with protanomaly, maximum pigment L absorption is not at 560 nm, but instead is displaced slightly to shorter wavelengths and is thus closer to the M pigment. If we analyse the response to a red 650 nm wavelength, for example, sensitivity for this colour is reduced when compared with someone with normal sight, and as a result, in a yellow match with a sum of red and green, people with protanomaly have to add more red than a normal observer.

Table 1.2 Types of colour vision depending on the cones

Type of cones Type of chromatic vision

Normal vision L M S Normal

Defective visionProtanopia – M S Defective (R/G confusion)Deuteranopia L – S Defective (R/G confusion)Tritanopia L M – Defective (B/Y confusion)Monochromatism – – S No chromatic visionAchromatism – – – No chromatic vision

Anomalous visionProtanomaly L’ M S Irregular (R/G confusion)Deuteranomaly L M’ S Irregular (R/G confusion)Tritanomaly L M S’ Irregular (B/Y confusion)


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People with deuteranomaly suffer a similar problem, but in their case it is the M pigment that is abnormally displaced towards red. Maximum absorption is no longer 540 nm, but rather is displaced towards a high wavelength and is thus closer to the L pigment.

This hypothesis of abnormal displacement in maximum pigment absorption has been confi rmed in works by Piantanida, Sperlig and Rushton, who identifi ed the existence of abnormal pigments in people with protanomaly and deuteranomaly. Tritanomaly has been observed in a very few cases, though the cause is always pathological due to lesions in the optic nerve or retina, rather than from an abnormal pigment. Despite the fact that the vast majority of chromatic vision abnormalities are due to the absence or alteration of a visual pigment, there are other (very few) cases that are the result of pathological disorders.

1.4.2 Confusion lines and colour discrimination tests

Imagine two colours, blue and yellow, with the same L and M inputs (Fig. 1.3). If we place these samples, C1 and C2, in the same scene, a subject without S cones will not notice any chromatic difference between the two. As there is no contribution from the S cones, these two colours will show the same response for the T channel and the D channel, and as such the subject will not see any chromatic difference between them.

In the CIE31xy chromaticity diagram, the colours that meet this condition are spread along a straight line (there is only one degree of variation). The colours that are indistinguishable to dichromats are located along lines that converge at a single point. This point of convergence is known as the confusion point, and is characteristic for each defi ciency type (identifying the chromatic co-ordinates of the missing response mechanism, although in practice there would be an infl uence from absorption of the pre-receptor media). Figure 1.4 shows the

L1

M1

S1

A1

T1

D1

111

1–21

00–1

362

C1 =L2

M2

S2

C2 =367

360

=

= *

A2

T2

D2

111

1–21

00–1

360

= *

A1 ≡ A2 T1 ≡ T2 D1 ≡ D2

TRITANOPIC =

=

1.3 Two different colours C1 and C2d will be similar for a tritanopic subject. T and D responses are equal.


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confusion lines for deuteranopia. As can be seen, this anomaly confuses all monochromatic colours from 540 nm to 700 nm. In protanopia, the confusion point is at the co-ordinates (0.747, 0.253), in deuteranopia it is at (1.080, – 0.080), and in tritanopia it is at (0.171, 0.000).

Of all these lines, there is always one that passes through the equienergetic white, and all these colours will be confused with white, including the corresponding cut-off point in the locus, which can be called the neutral point (494 nm for protanopia, 499 nm for deuteranopia and 570 nm for tritanopia). The neutral point is important because it characterises the type of defi ciency, and this fact is used to design detection tests and to categorise chromatic anomalies and defi ciencies (see colour Plate III).

1.5 Colour constancy

1.5.1 Mechanisms of adaption

Adaptation is the process by which the visual system changes its sensitivity, depending on luminance level in the visual fi eld, so as to adapt to existing

0.8

510

500

490

480

470460

520 530

540

550

560

570

580

590

600

610620

0.7

0.7

0.6

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0.5

0.5

0.4

y

x0.4

0.3

0.3

0.2

0.2

0.1

0.10.0

0.0

1.4 Protanopic confusion lines.


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light levels. The process can be explained (at least in part) because each photoreceptor on the retina can change its own sensitivity curve depending on the amount of light it receives. It is a slow process that can take several minutes (Fig. 1.5).

The visual system uses various mechanisms to adapt to high and low light levels. The iris contracts very quickly, followed by a much slower reaction from the retina’s photoreceptors as they adapt their sensitivity. The change in response from the photoreceptors is a very effective method of adaptation, whereas the contraction of the pupil is thought to be more of an initial defence mechanism to protect the retina against sudden changes in light.

1.5.2 Chromatic adaptation and gain control mechanisms

The human visual system is able to adapt in such a way that the colour of an object remains unchanged, despite any changes in the light. Thus, chromatic adaptation is defi ned as the ability of the visual system to deduct the light spectrum so as to preserve the chromatic appearance of that object.

A sheet of paper seen with daylight or under a light bulb will always seem white, even though sunlight is much more blue than the light from a tungsten bulb, and if we measure the colour of that piece of paper with a photometer in both situations, the results are very different.

All colour adaptation models are based on the Von Kries coeffi cient law: ‘The individual components present in the eye are completely independent of one another and each is adapted exclusively according to its own function.’ According to modern interpretation, the Von Kries law means that each

1,2

1,0

0,8

Sen

sitiv

ity

Low luminance responseHigh luminance response

Wavelength

0,6

0,4

0,2

0,0

400 450 500 550 600 650 700

1.5 Von Kries law: each photoreceptor can change its own sensitivity.


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photoreceptor has its own gain control mechanism, so that sensibility is adapted according to equation 1.3.

La = KL * L

Ma = KM * M [1.3]

Sa = KS * S

La, Ma and Sa are the post-adaptation cone signals, L, M and S are the initial cone signals, and Kn is the gain control mechanisms.

Gain control mechanisms can be explained by changes in photoreceptor responses. A high luminance level will split a large number of the cones’ pigment molecules, and as such the number of molecules available to produce a visual response is much lower. This explanation of the gain control mechanisms only refers to cones and rods, and an explanation of chromatic adaptation in its entirety involves understanding other subsequent mechanisms of adaptation – probably neural gain control mechanisms at a horizontal, bipolar and ganglion cell level.

1.5.3 Illuminant discount

As we have seen in the previous section, chromatic adaptation is a change in the sensitivity of the chromatic mechanisms, which makes it possible for the colour appearance to remain unchanged. Colour constancy can be seen as an extreme case of chromatic adaptation that associates a colour to an object regardless of the light in which the object is seen. Thus, many well-known objects (our car or our coat, for example) seem to be always the same colour, whether we see them in daylight or under fl uorescent lighting. In colorimetric terms, the colours will be different, but the visual system interprets them as being the same. However, failures in this colour constancy also occur. These failures are explained by a marked colour variation in lighting that the visual system is unable to adapt to, or because the system produces an inappropriate adaptation response.

1.6 Bibliography

Chalupa, L.M. & Werner, J., The Visual Neurosciences, Cambridge: The MIT Press, 2003.Fairchild, M.D., Colour Appearance Models, Chichester, West Sussex (UK): John Wiley &

Sons, 2000.Foster, D.H., ‘Inherited and acquired colour vision defi ciencies: fundamental aspects and

clinical studies’. In Vision and Visual Dysfunctions, Vol. VII, Ed. J.R. Cronly-Dillon London: Macmillan Press Ltd., 1991.

Gegenfurtner, K.R. & Sharpe, L.T., Colour Vision. From Genes to Perception, Cambridge: Cambridge University Press, 1999.

Kaiser, P.K. and Boynton, R.M., Human Color Vision, 2nd edition, Washington, DC: Optical Society of America, 1996.


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Marr, D., Vision, New York: W.H. Freeman and Company, 1982.Schwartz, S.H., Visual Perception: A Clinical Orientation, 3rd edition, New York:

McGraw-Hill, 2004.Spillmann, L. & Werner, J.S., Visual Perception: The Neurophysiological Foundations,

New York: Academic Press, 1990.Wandell, B.A., Foundations of Vision, Sunderland: Sinauer Associates, 1995.

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++

L M S

L M S

+

–

–

2

+

Plate I Photograph of a section of vertebrate retina (cones, bipolar, ganglion and amacrine cells). Courtesy of Dr Nicolas Cuenca, Department of Physiology, Genetics and Microbiology, University of Alicante, Spain.

Plate II Boynton’s model representation.


Plate III Colour vision simulation for protanopic, deuteranopic and tritanopic subjects.

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19

2Scales for communicating colours

A. K. ROY CHOUDHURY, Government College of Engineering and Textile Technology, Serampore, India

Abstract: In colorant production and application industries, colour order systems or colour notations are required for effective communication, comparison, recording and formulation of colours. This chapter discusses merits-demerits and accuracy of various important colour order systems, namely Munsell, Natural colour system, Ostwald, DIN, OSA-UCS, Coloroid, etc. in the material form as well as digitised form. The comparison and interrelations between various systems are also discussed.

Key words: colour order systems, colour notation, colour atlas, colour naming, Munsell system.

2.1 Introduction

While communicating or talking about colour, a language which is understandable by all parties must be followed. A logical scheme for ordering and specifying colours on the basis of some clearly defi ned attributes is known as the ‘colour notation system’. The attributes are generally three in number, as our vision is trichromatic, and they constitute the coordinates of the resultant ‘colour space’. Colour notation systems also encompass ‘colour order systems’ which typically comprise material standards in the form of a colour atlas. Due to constraints of the colorant gamut, the atlases may depict only a physically realisable subset of a colour order system.

Colour notations can be classifi ed into three categories (Rhodes, 2002):

1 Device dependent systems – the most common imaging devices used for reproducing colour are the computer-controlled CRT displays and the colour printers. The associated colour order system and colour spaces are hardware-oriented and they lack perceptually based attributes.

2 Mathematical systems – uniform colour spaces based on mathematical transformation of CIE tristimulus values such as CIELUV and CIELAB belong to this category.

3 Systems based on database of aim points – colour order systems existing principally in physical form, the colour samples of which can be measured to establish a database of aim points. Using interpolation techniques among limited available samples, many more colours can be defi ned.

Humans with normal colour vision can distinguish among some two million colours when viewed against a mid-grey background and perhaps double this

19


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when the background is widely varied (Kuehni, 2005). The orderly and meaningful arrangement has been a matter of concern for the last 2000 years as a colour system which will manage to meet all requirements needs to be based on many years of physical and psychological research and experience.

Until the seventeenth century, the prevailing opinion of the great Greek philosopher, Aristotle was that colour is generated from the interaction of darkness and light and that there are seven simple colours out of which all others are obtained by mixture. These seven are white (pure white), yellow, red, purple, green, blue and black (pure darkness). The history of colour order shows that the relationships between colours are rather complex and took two millennia to unravel. Originally, colour order systems consisted of lists of colours, such as those by Aristotle or Alberti.

It was only at the beginning of the seventeenth century that the fi rst graphical representation of colour order appeared in the work of Forsius. A different style of graphical representation of colour order was developed by the Belgian Jesuit and scholar François d’Aguilon (1567–1617). In his graphic representation, d’Aguilon showed tonal mixtures of the three chromatic simple colours with white and black as well as intermediates between white and black (a grey scale), with arcs above the line of the simple colours. Below the line, he represented with other arcs the hue mixtures of the three chromatic simple colours (Kuehni, 2003).

The modern concept of colour was founded in 1704 by Isaac Newton. Before this, all colour order systems were one dimensional or linear but Newton recognised three colour attributes and drew an incomplete (spectral colours only) chromatic diagram in the form of spectral colours on the circumference and white in the centre. The saturation lines were drawn as radial lines from the white centre to the spectral periphery. Newton was also an alchemist believing in universal harmony. In analogy to musical tones, he chose seven hues in the spectrum: red, orange, yellow, green, blue, indigo and violet (ROYGBIV). However, the choice of seven is always controversial – repeated tests have shown about 120 discernible colours in the spectrum (Kuehni, 2005).

LeBlon (1756) was fi rst to make a clear distinction between mixing pigment colours and mixing colours of light. He stated that all visible objects can be represented by three colours, yellow, red and blue, and mixing these three colours makes black or all other colours. He named them as material colours or those used by painters. He further added that for a mixture of spectral colours those proposed by Sir Isaac Newton could not produce black, but the very contrary, white. Moreover, purple is perceivable in object colours only.

The fi rst proposal for a three-dimensional double tetrahedron system was made by the German mapmaker and astronomer Tobias Mayer in 1758. A French silk merchant, Gaspard Grégoire, fi rst proposed a three-dimensional object colour order system based on the perceptual attributes hue, (relative) chroma and lightness and an atlas with 1350 samples was introduced before 1813 (Kuehni, 2008a). Matthias Klotz (1748–1821), a German painter, also proposed a three-dimensional

Scales for communicating colours 21

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colour order system based on independent perceptual colour attributes. He proposed the cylindrical colour order system that consisted of a well-defi ned lightness scale (Kuehni, 2008b). About 100 years later Albert Munsell introduced a system based on intensive scientifi c studies very similar to the above systems.

Four-dimensional Riemannian colour space was fi rst proposed by Helmholtz with the help of a linear element which is diffi cult to defi ne precisely and hence, the conceptualisation remained unclear. Recent studies (Leonev and Sokolov, 2008) showed that perceived colours can be represented on a spherical colour space of unit radius (hyper-sphere) in 4D Riemannian space. The model devotes a dimension to the stimulus parameter ‘darkness’ recognising the separate signals conveyed by light and dark neuronal channels. The advantage claimed is that the model defi nes mathematically the relation between the perception of large colour differences and the physical characteristics of luminous stimuli more consistently. However, a four-dimensional space is diffi cult to visualise.

It is not very clear how colour names developed historically. One of the two prevailing opinions is that people of all societies became aware of different colours or colour categories and then named them in the same sequence: white and black, red, green, yellow, blue, brown, purple, pink, orange, grey (Berlin and Kay, 1969). Others think that all colour names are group cultural achievements and there is little common thread.

Many colour words are related to materials, such as orange, ultramarine, olive, malachite green, bottle-green, peanut-green, sea-green, etc. These common names refer to the colour of various common objects which can be quickly recognised and memorised by most people. Some names refl ect poetic invention, such as Cuban Sand, Ashes of Rose, Blue Fox and so on. But such colour names are very approximate, unreliable and temporary. Their meaning also changes with observer, time, place, style, technology, language, culture, etc.

It is common practice to describe colour in terms of hues like red or yellow along with tone or secondary hue such as greenish or bluish and the amount of light refl ected such as dark or pale. However when we describe a colour as ‘dark greenish blue’, the description is very inadequate as there may be many thousands of such colours. This problem was realised long ago.

The accurate description of colour is essential for communication and for accurate reproduction of colour across a wide range of products. The colour of any object is commonly registered or recorded in two ways, namely:

• preserving coloured physical samples• recording in terms of common colour names.

Physical samples of dyed/printed fabrics, yarns or fi bre paint panels, patches of printing inks, coloured papers, etc. are frequently used to express colours in the trade. Collections of such colour samples are very useful as examples of coloured product if the number of colours required is fairly limited. A good example of such collections is the dye-manufacturers’ ‘shade cards’. The shade cards carry


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numerous coloured objects on specifi c substrates (e.g. pieces of paper or various textile materials) along with procedures and names of the colorants to be used. The paint and printing ink manufacturers also publish shade cards for their products (colours) with names very specifi c to the concerned industry. However the exemplifi cations are very limited. They are restricted to the specifi c type of colorant or substrate and cannot be used for general reference.

In the modern age, the celebrated German scientist A. G. Werner (1750–1817) was probably fi rst to standardise colours by developing a method of describing minerals by their external characteristics like colours. In 1814 Werner’s system was bought in book form (entitled Werner’s Nomenclature of Colours, containing 110 samples of colours) by a fl ower painter of Edinburgh, Patrick Syme. In 1905 a French work, Répertoire de Couleurs, was published containing 365 plates consisting of 1 356 colours as in horticulture, traditional and textile use, described by colour names in various languages.

2.2 Systematic arrangements of colours

When we deal with a reasonable number of specimens, say a few thousand, to cover the whole range of possible colours (one million or more), the specimen must be selected according to a system or plan. The colour naming systems were popular for a long time, but they were not very systematic – hence the accuracy of specifi cation was limited. It is absolutely necessary to arrange the colours in a systematic manner to tackle the enormous number of colours we can perceive.

It is well known that the colours are three-dimensional. However, the dimensions of colour are expressed in various ways in different fi elds. For systematic arrangements, the dimensions should be independent of each other. The question is, therefore, which dimensions are to be chosen to arrange colours in a three-dimensional space?

The most natural and logical approach can be illustrated by Judd’s ‘desert island’ experiment (Billmeyer, 1981). A person sitting idly on a desert island may decide to arrange systematically the large number of pebbles surrounding him according to colour. First he sorts out coloured (i.e. chromatic) and colourless (i.e. achromatic) pebbles. Then he arranges colourless pebbles – black, dark grey, medium grey, light grey and white (Plate IV, step 1, in the colour section between pages 42 and 43). This classifi cation is based on a property called ‘lightness’ or ‘value’.

Then chromatic pebbles are classifi ed according to their common colour names. All surface or object colours can be classifi ed broadly into fi ve principal colours namely red, yellow, green, blue and purple (Plate IV, step 2). While the former four can be seen as spectral colours, purple is perceivable in object colours only. If the variation of colour in the pebbles is great we may have reds admixed with yellow, yellow admixed with green and so on. The hues intermediate of the fi ve principal hues can be named as red-yellow, yellow-green, green-blue, blue-purple


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and purple-red (Plate IV, step 3). Now there can be red colours having varying degrees of yellowness, and each may be considered as a separate hue. Hence the above ten hues can be further classifi ed into any number of intermediate hues. For assembling into a decimal system, each of the above ten hues may be classifi ed into a further ten intermediate hues. Hence we can have 100 hues; the number is for convenience and can be changed into any other number.

After classifying the coloured pebbles into separate hues, further classifi cation may be done according to lightness or value. For example, pink, light bluish-red, medium bluish-red, dark bluish-red – all may be of the same hue, only varying in lightness (colour Plate IV, step 4). Lightness is actually a measure of the total amount of light refl ected in the visible region of wavelength.

After classifying the pebbles according to hue and lightness, all pebbles in a group may not be equally colourful – some are very vivid and colourful and the others are dirty and less colourful or pale. This is due to the varying degree of hue or colour content. Two objects of equal lightness means both refl ect equal amounts of light. But a colour may be admixed with grey or black and a portion of the refl ected light may be due to this grey component. This grey content, or conversely the colour content, is the third dimension of colour called ‘chroma’ or ‘saturation’. So the pebbles of equal hue and lightness can be further classifi ed according to chroma or saturation (colour Plate IV, step 5). Clearly, chroma and saturation have different meanings; the former is the hue content in relation to the brightness of a reference white, while the later is the hue content in relation to its own brightness. Every colour sensation unites three distinct qualities and one quality can be varied without disturbing the other. A colour may be weakened or strengthened in chroma without changing its value or hue.

Wright (1984) identifi ed two sets of visual attributes, namely:

Group A attributes are lightness, hue and chroma.Group B attributes are whiteness, blackness and chromaticness.

According to Wright (1984) and Nayatani (1984), group B attributes are more useful because it is most easily understood and is more fundamental for observers to represent colour appearance. However they are less studied in psychometric (equal perception) terms. The colours of the outermost Munsell (group A) hue circle are close to full colours which is a term for group B attributes.

2.3 Colour order systems

A colour order system is a systematic and rational method of arranging all possible colours or subsets by means of material samples. Once the colours are arranged systematically they are named in some descriptive terms and/or are numbered (Graham, 1985). It is also desirable that the samples included in any colour order system are to be properly specifi ed in terms of any standard colorimetric specifi cation, the most common being the CIE colorimetric system.


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The colour order systems are constrained by the following facts (Fairchild, 2006):

• Orderly (and continuous) arrangement of colours.• A logical system of denotation.• Perceptually meaningful dimensions.• Embodied with stable, accurate and precise samples.

Colour specifi ers or atlases are convenient physical forms of any colour order system. Colour order systems are three-dimensional, but atlases are two-dimensional so that they can be presented in the form of a book or fl at form (Lewis and Park, 1989). They have multiple functions such as:

• Stand-alone design tool for colour ideas.• Quick communication of colour ideas over distance.• The larger swatches provide master standards.• Basis for specifying colours during colour formulations and colour ideas.• Supporting role for instrumental response or visual perception of instrumentally

measured colours.

An atlas should fulfi l certain criteria such as:

1 The ideal design should be based on colours uniformly distributed throughout the colour solid.

2 Selection of the substrate for an atlas is very important. Colours illustrated on cotton are readily matched on other substrates using the appropriate class of dyes (Park, 2008). To facilitate accurate assessments, however, some atlases have been prepared on multiple substrates. Moreover, different applications require different colour ranges. The full range of requirements for textiles, paint, plastics and ceramics are quite different.

3 The ideal atlas should be highly stable and should have good fastness properties, particularly to light.

4 It should be simple and easy to understand. The samples are to be reproducible and replacement pieces should be available.

5 It should be cheap, portable and globally used.

However, no atlas is expected to represent visually millions of colours that can be detected by our eyes. There is no ideal colour order system and hence no ideal atlas.

2.3.1 The necessity of a colour order system

It is a diffi cult task to deal with the millions of colours which our eyes can distinguish. We can feel the problem instantly if we try to describe a colour variation from memory or when we try to describe a colour to a man at a distance via communication channels (Roy Choudhury, 1996). The problem was known in


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ancient times and several people have tried to solve it in their own way: Nobel laureate W. Ostwald, American artist A. H. Munsell and many others studied the problem in great detail.

In colorant production and application industries, colours are to be communicated, compared, recorded and formulated on a regular basis. This necessitates the systematic classifi cation of colours, which can be done in various ways. The classifi cation may be based on visually or instrumentally assessed colour parameters as various colour order systems were developed originally on the basis of visual attributes, but later supported and modifi ed by instrumental assessment. The main reasons for the widespread interest of colour order systems are for communication about colour over distance and time as well as for analysis and defi nition of the aesthetic relations among colours (Härd and Sivik, 1983–4).

2.3.2 Classifi cation

The colour order systems are of three types (Wyszecki, 1986):

1 Colorant-mixture system based on subtractive mixture of colorants.2 Colour-mixture system based on additive mixture of colour stimuli, for

example the Ostwald system.3 Colour appearance system based on the principles of colour perception or

colour appearance.

However, Derefeldt (1991) suggests that colour appearance systems are the only systems which are appropriate for general use because these are defi ned by perceptual colour coordinates with uniform or equal visual spacing of colours.

Colorant-mixture systems

These systems display the range of colours which can be achieved with declared quantities of colorants. The desired colours are developed by compounding a limited number of pigments or dyes in systematically varied proportions. The main purpose of these systems is to illustrate the range and other properties of a set of colorants.

The principle of colorant mixing is subtractive and the colour gamut is restricted by the choice of primary colorants. This is helpful in the reproduction of shades for a particular coloration industry. However the method of application should be identical to that of the atlas. The dyes obtained from different manufacturers vary in colour considerably, but for the paint and printing ink industry the problem is less severe and the manufacturers’ recommended mixed shades are fairly reproducible.

Examples of colorant-mixture systems are the colour atlases developed by different dye manufacturers. The ICI Colour Atlas (1969) was a collection of 1 379 original colours and 27,580 variations printed on papers. Similarly other


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atlases were also developed by different dye-makers such as the ‘Tootal Atlas’ (2 200 fabrics, 1970), ‘Hoechst Atlas’, ‘Ciba-Geigy Colour Atlas’ (625 fabrics, 1982) and the ‘BASF Colourthek III’ (2 580 fabrics, 1970). Examples also include ‘Piochere Colour System’ and ‘Martin-Senour Nu-Hue System’ (Wyszecki and Stiles, 1982).

The Pantone colour system is basically a colour mixture system. The Pantone system (www.pantone.co.uk) began life in 1963 in the USA for defi ning colours for printers, but expanded into other fi elds later, e.g. textiles in 1984, plastics in 1993, and architecture and interiors (1 925 colours) in 2002, each of which has a six digit numerical notation (e.g. # 19-1764) and an ‘inspirational’ colour name. This is widely used in graphic art and also in the textile industry mainly because of its low cost, though the colours are not equally spaced and the shades are prepared on paper using printing inks. It is not a colour order system since it does not include a continuous scale. It is more appropriately considered a colour naming system. The Pantone system is loosely based on a three-dimensional scale using a six digit reference number, two each of which indicate colour strength, hue and tone successively, but CIE specifi cations are not available. Recently, a textile version of the Pantone atlas (having 1 001 reactive dyed cotton fabrics) has been introduced in the market.

Colour-mixture systems

The basic idea of a colour-mixture system is to show, in the form of material standards, the sequence of colours related either to manipulation of the controls of a tristimulus colorimeter or to variation in simple ways of the proportions of sector areas on a Maxwell disk. So the basic principle of generating colour is additive colour mixing. The tristimulus colorimeter aims to tie in colours with the CIE system of colorimetry, or more specifi cally with the chromaticity diagram. However, long before the development of the CIE system of colorimetry, the Maxwell disk was used to develop colours of constant dominant wavelength by varying the proportion of the chromatic sector and the achromatic sector (white, grey or black) on the disk. Judd and Wyszecki (1963) preferred an additive colour-mixture based colour order system due to its resemblance to our everyday experience of colour perception. The most popular in this category is the Ostwald colour system. A few other examples of colour-mixture systems are as follows.

The Colour Standards and Colour Nomenclature atlas of Robert Ridways, a bird curator in the United States National Museum, was published in 1912 containing 1 113 named coloured samples. Each sample is 1" by ½" rectangular matte printed paper. In a page, the lightest sample is at the top followed by seven steps of increasing grey content. Each column represents constant dominant wavelength obtained by rotary mixing of white, black and a chromatic pigment. The system represents 35 dominant wavelengths maintaining approximately uniform hue-spacing. The representation of near-whites is poor, but of near-greys


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is excellent. The system was popular among naturalists for colour specifi cation of plants, fl owers, birds, insects, rocks etc.

The Colour Harmony Manual is one of the most important colour-mixture systems, published by the Container Corporation of America during 1942–72 (Jacobson, 1972). It consists of a set of 12 hand-books, each showing a pair of complementary hues. Each colour chip is specifi ed by the Ostwald method on a 24-step hue scale, i.e. 12 pairs of complementary hues of constant dominant wavelength. The number 24 was chosen because it is divisible into equal intervals of 2, 3, 4, 6, 8 and 12 for selecting multi-hue harmonies. Each hue-chart shows samples having varying black, white and full-colour content represented by double-letter names such as na, ga, ca, etc. The vertical series in the triangle were called ‘shadow series’ because they have the same dominant wavelength and chromaticity and differ only in refl ectance. The fi rst and fourth (last) editions of the manual contain 680 and 949 chips respectively. Light colours and near-whites were not included in the manual and the system cannot readily translate the attributes into useful textile terms. The publication of the manual was discontinued after 1972 mainly due to poor standard of reproduction (Greenville, 1994).

The Dictionary of Colour, published in book form by Mearz and Paul (1950), shows a collection of over seven thousand colours (precisely 7 056 numbers) classifi ed into seven hue groups. Considerable effort has been made to describe the colours by commonly used colour names. The names are displayed on the left-hand pages, while the corresponding colours are shown opposite. The names have been gathered from the sources mentioned above and other reliable and established sources and no name has been originated by the authors of the book. The various adjectives (e.g., light, pale, etc.), trade or commercial names have been excluded and emphasis has been solely based on the names’ colour perception.

The above system is an intermediate between subtractive colorant-mixture and additive colour-mixture systems. The colours are created by variable-density overprints of inks of different colours. Wherever there is overprint, there is subtraction, while in remaining areas the colours are additively mixed. The system shows a collection of over seven thousand colours printed in the form of a book, but colour variations from copy to copy are reported as these are printed on paper. Near-blacks and light-saturated colours are not included, while the colours used are remarkably permanent.

The order of seven hues in The Dictionary of Colour follows the spectrum – red to orange, orange to yellow, yellow to green, green to blue-green, blue-green to blue, blue to red, and purple to red. The plates are divided into twelve rows (A to L) and twelve columns (1 to 12). The rows start with ‘no hue’ at one end and ‘full hue’ at the other end. The columns represent hues as a mixture of two hues in varying quantities. Each of the seven hues is presented in eight successive plates with an increasing grey content. The perfect scale of reduction should be the geometric series, based on the Weber-Fechner law, having a percentage of refl ection in the order of 0, 12, 22, 32, 42, 52, 62, 72, 82, 92, 102. However, some of


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the steps like 0 or 100% are impractical and, moreover, smaller intervals were given for lighter colours so that they are in suffi cient numbers. In this system, effort has been made to arrange colours in some defi nite order, however the spacing of the samples is somewhat arbitrary and not equally spaced. The system describes a very small portion of the colours visualised by us. Intermediate colours can be interpolated, but such interpolation cannot be communicated to others because the samples are not spaced equally as per the visual colour perception.

Colour appearance systems

These systems are based on the perception of colours by an observer with normal colour vision and the scales are chosen to represent attributes of perceived colours. However, attributes represented in various systems are different. The spacing of colours along the scales also varies from one system to another, even when the same attribute is used in both systems.

Most of the earlier atlases were in favour of inclusion of colours of long traditional usage, thereby emphasising tighter spacing of colours in some hue regions. Such systems sample the colour solid non-uniformly. In other words, there is no uniform placement of the colour samples throughout the total colour space. Some areas of colour space are over-emphasised, while some areas are poorly presented or not presented at all. A universal urge to arrange the colour chips on the basis of constant hue is strongly felt by the designer of colour order systems. However, the mixture of chromatic colour, black and white is only an approximation to constant hue.

The main emphasis of appearance-based systems is the uniform visual spacing. The systems thus allow easy interpolation between the samples represented and extrapolation of colours not illustrated in a given collection. The collections of samples are generally represented in pages of constant hue. The most popular appearance-based colour order system is the Munsell system.

Psychometric scales provide a way of assigning numbers to physical stimuli according to the psychological attributes that the stimuli evoke. The relationships between perceptual magnitudes and physical measures of stimulus intensity are assessed by scaling experiments. The types of scales may be as follows (Wyszecki and Stiles, 1982; Kuehni, 2003).

‘Nominal scales’ merely determine whether or not things are equal. The same name or symbol is assigned if they have the same value for the attributes. The colours, for example, can be grouped into yellows, reds, greens, blues, etc.

‘Ordinal scales’ assign numbers in such a way that the order of the numbers corresponds to the order of the magnitudes of the attribute being scaled. The stimulus with higher scale value will be perceived as having more of the attribute. An ordinal scale is subject to logical operations: equal to, greater/less than, etc.

‘Interval scales’ have all the properties of ordinal scales and, in addition, the differences (intervals) between the numbers characterise the sizes of the


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corresponding perceived difference of the attribute (e.g. Celsius and Fahrenheit scales). Colour scales are usually interval scales.

‘Ratio scales’ are interval scales with a natural origin. Examples are length in metres, duration in seconds and temperature in degrees Kelvin. The zero point of the scale corresponds to a stimulus for which the attribute has zero magnitude. As a result, the numbers on the scale are proportional to the perceived magnitudes of the attribute being scaled. Many colour order systems consist of ratio scales.

A variety of techniques have been devised for psychometric scaling (Wyszecki and Stiles, 1982; Fairchild, 2006) such as:

• Rank order• Graphical rating• Category scaling• Paired comparisons• Partition scaling• Magnitude estimation• Ratio estimation.

2.3.3 Merits–demerits

The advantages of material-based colour order systems (Hunt, 1987) are listed below.

1 As represented by physical samples, the systems are realistic and easy to understand. It is easy for the eye to specify object colours by comparison with reference to physical samples, rather than by matching with colours in memory (Hunter and Harold, 1978: 300).

2 The atlases are easy to use. In most cases, side-by-side comparisons are made under standard viewing conditions and, as such, no instrument is required.

3 The systems based on perceptual scaling like Munsell and NCS can be used to evaluate mathematical colour appearance models.

4 At present, most of the colour order systems are calibrated in terms of tristimulus values; hence reference can be made to the colour order systems for colour control or for colorant formulations by computer, even in the absence of reference samples.

5 Visually uniform colour spaces, such as Munsell and OSA, can prove a useful way of organising the colours of a digitally controlled colour television monitor. Future uniform colour spaces will probably be defi ned with the aid of these monitors but with a higher fl exibility and wider colour gamut than the complex pigment technology presently in use (Durrett, 1987).

6 Colour order systems can be used as sources for test targets for imaging systems or other measurement devices. The Macbeth Colour Checker chart, commonly used as a test target for imaging systems, is partially based on the Munsell system.


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Some of their limitations are:

• A number of colour order systems are used globally and they are not mutually convertible.

• The actual colour of the physical samples in the atlas may be quite different from the intended aim colour. The error may be of several CIELAB colour difference units and may differ from batch to batch, i.e. poor reproducibility.

• It is not possible to include all perceivable colours in any colour order system. The Chroma cosmos 5000 is the largest, with 5 000 uniquely dyed samples, whereas most of the atlases consist of around two thousand samples. Any colour atlas is a serious abridgement of the colour world as there are gaps between the available physical samples. Interpolation or extrapolation is, therefore, frequently necessary for colour specifi cations, the accuracy of which largely depends on the colour discrimination effi ciency and experience of the observer.

• As colour order atlases are composed of a limited number of physical samples, future inclusion of newer samples may be a problem. Though most of the systems keep provision for addition of newer samples, it may occasionally be necessary to alter the spacing.

• The perceptual scales of colour appearance in a colour order system have been established for a specifi c viewing condition. No data have been provided with respect to change in viewing conditions and the visual spacing of the samples is valid only if standard illuminating and viewing conditions are maintained. The errors are not likely to be very high if typical indoor daylight is used, but viewing under other artifi cial lights may result in serious errors.

• The visual interpolation between atlas samples to determine the notation of colours not represented in the atlas is subjective and may differ between individuals. The phenomenon is known as ‘observer metamerism’ (Roy Choudhury and Chatterjee, 1992).

• As the system uses physical samples, there are chances of deterioration of the standards due to limited stability of the colorants, extensive use or long exposure to light. High chroma colours may require fl uorescent dye or pigment, the use of which is restricted due to limited stability. The manufacturer takes proper care for good performance, but still after a certain interval of time, the genuineness of the sample may be questioned. Moreover the user will be completely unaware of such changes.

• Most of the colour order systems cannot be used for self-luminous colours such as light sources unless ancillary apparatus is used.

• Though colour order systems are used for a variety of applications in colour appearance, they are not a substitute for a colour appearance model.

• The relation between perceptual coordinates of the colour order systems and colorimetric coordinates are complex and cannot be expressed by accurate equations. Approximate transformation equations have been derived by statistic fi tting and neural network modelling and look-up table interpolation techniques are used for transformation from the CIE colorimetry to colour order coordinates.


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2.4 Various colour order systems

The idea of using a three-dimensional colour solid to represent all colours was developed during the eighteenth and nineteenth centuries. Several different shapes for such a solid were proposed, including a double triangular pyramid by Tobias Mayer in 1758, a single triangular pyramid by Johann Heinrich Lambert in 1772, a sphere by Philipp Otto Runge in 1810, a hemisphere by Michel Eugène Chevreul in 1839, a cone by Hermann von Helmholtz in 1860, a tilted cube by William Benson in 1868 and a slanted double cone by August Kirschmann in 1895 (Kuehni, 2002). These systems became progressively more sophisticated, with Kirschmann’s even recognising the difference between coloured lights and object colours. But all of them remained either purely theoretical or encountered practical problems in accommodating all colours. Furthermore, none was based on any rigorous scientifi c measurement of human vision; before Munsell, the relationship between hue, value and chroma was not understood. Munsell replaced all historical approaches with the proposal of a balanced colour sphere, later replaced by an irregular solid.

Subsequently a large number of colour order systems have been developed in different parts of the globe at different times. However, there is no internationally agreed colour order system to date. Some systems are very popular, some are used occasionally while some are obsolete.

Six popular colour order systems and their respective colour attributes are as follows:

1 Munsell – hue, value and chroma2 Natural colour system – hue, blackness and chromaticness3 Ostwald system – hue, lightness and saturation4 DIN system – hue, saturation degree and darkness degree5 OSA-UCS – no separate scaling of three attributes6 Coloroid system – hue, saturation and lightness.

In addition, there are a few less known and newly developed colour order systems such as: Swiss Colour Atlas 2541, Chevreul, Colourcurve, Eurocolour system, Acoat system, Pope colour system (Heila, 1988). A one-dimensional colour order system for dental shade guides has been proposed by O’Brien, Groh and Boenke (1989) by visual ranking of translucent porcelain bioform shade guide teeth of the American Dental Association.

2.4.1 Munsell colour order system

Professor Munsell (1905), an artist, wanted to create a ‘rational way to describe colour’ that would use decimal notation instead of colour names (which he felt were ‘foolish’ and ‘misleading’) and so developed the oldest and by far the most popular colour order system to fi ll the gap between art and science. The Munsell atlas was released in 1915, commercialised in 1929 and the system has been extensively studied by Billmeyer (1987). The Association Internationale de la


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Couleur (AIC) study group on colour order systems prepared a computer-based annotated bibliography containing about 400 entries out of which 115 references are on the Munsell system (Billmeyer, 1985).

Prior to 1943, the Munsell system was defi ned by the physical samples composing the 1929 MBC colour chips and thus the basic specifi cation of the Munsell system was the spectral refl ectance function of each colour chip. The spacing of the chips was intensively studied by the Colorimetry Committee of the Optical Society of America and in 1943 the CIE tristimulus values of ideally spaced chips were published as the Munsell renotation system (Newhall et al., 1943). Obviously there are many important physical and psycho-physical differences between earlier refl ectance based systems and the present tristimulus based systems (Berns and Billmeyer, 1985).

The system (colour Plate V) consists of the following three independent dimensions which can be represented cylindrically in three dimensions as an irregular colour solid.

1 Hue (H), measured along circumference of the horizontal circles.2 Chroma (C) or purity of colour, measured radially outward from the neutral

(grey) vertical axis.3 Value (V), measured vertically from 0 (black) to 10 (white).

Munsell determined the spacing of colours along these dimensions by taking measurements of human visual responses. In each dimension, Munsell colours are as close to perceptually uniform as he could make them, which makes the resulting shape quite irregular. The perceptual uniformity of the system is only valid under illuminant C, a uniform middle grey (N5) background with a suffi ciently high illumination level (greater than 500 lux).

The Munsell system divides each horizontal hue circle into fi ve unique or principal hues: Red (5R), Yellow (5Y), Green (5G), Blue (5B), and Purple (5P), along with fi ve intermediate hues (5YR, 5GY, 5BG, 5PB, 5RP) halfway between adjacent principal hues. In the original Munsell book, each hue sector (H) is divided further into four fi ner categories, namely, 2.5H, 5H, 7.5H and 10H (0 for the next H). However, each of these ten steps may also be broken into further ten sub-steps, so that 100 hues are obtained with integer values (though Munsell originally sampled only 20 hues, later 40). The naming of these hues starts from the mid-point between major hues and numbered from 0 to 10, e.g. 5R, 6R-9R, 10R or 0YR, then 1YR, 2YR and so on. For the modern Munsell hue scale to be visually uniform, Kuehni (2005) observed that different numbers of Munsell hue steps are required between average unique hues namely:

Unique hue sector → red to yellow yellow to green green to blue blue to redMunsell hue steps → 20 23 26 31

The open-ended chroma increases from 0 for a neutral colour to colours with stronger hue content. There is no maximum for the chroma. The highest chroma


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depends on the hue and value of the samples and the colorant used to produce them. With the available colorants, chroma is generally restricted to a maximum of 16 or 18. A colour is fully specifi ed by three numbers for hue, value and chroma, e.g. H V/C. The Munsell atlas is usually available on painted paper in glossy (1 488 chips) and matt forms (1 277 chips).

As the Munsell system is based on polar coordinates, smaller perceptual differences occur in the near-neutral grey region of colour space than in the outermost saturated regions. Physical distance between neighbouring hue increases with increase in chroma, i.e. increase in distance from the neutral axis. In the Munsell system, therefore, the evaluation of near-neutral samples is problematic – hence the ‘Nearly Neutral Collection’ came onto the market in 1990. The atlas contains a range of light and near grey samples, an important colour region for various fi elds of design and architecture such as wall and house colour, colour of furniture and offi ce equipment, building materials, cosmetics, etc. Each page contains a grey scale running in half steps of lightness from Munsell value 6/ to 9/ and Munsell chroma /0.5 to /4 for 20 hues, two for each of Munsell’s ten principal hues. However it is diffi cult to combine the two atlases during evaluation.

Indow and Watanabe (1980) demonstrated that human observers can memorise the scheme of Munsell notation and, without comparing with the Munsell standard chips, they can specify colour samples in terms of (H V/C).

It is not guaranteed that one step in ‘V’, one step in ‘C’ and one step in ‘H’ represent the same size of perceptual difference. The scaling perceptual differences δjk between two Munsell standard chips j and k have shown that chips are embeddable as a confi guration of points (Pj) in a 3D space locally Euclidean metric. However, in order to accumulate through this approach a tremendous amount of experimentation is necessary to collect suffi cient information necessary to provide the Munsell colour solid with a unifi ed distance scale (Indow and Romney, 2008).

The relations between Munsell and CIE variables are very complex. In the CIE chromaticity diagram, lines of constant Munsell hue are curved and the location changes with change of Munsell value. The Munsell value (V) scale is related to CIE luminance factor (Y) by a complex fi fth degree polynomial equation called Judd’s polynomial as follows (Newhall, 1940):

Y = 1.2219V – 0.23111V2 + 0.23951V3 – 0.021009V4 + 0.0008404V5 [2.1]

The equation was devised by Judd with measurements based on the use of magnesium oxide, and assigned a value of absolute refl ectance of 1.026 for 45°/0° illumination and viewing.

This can be inverted through iterative methods to obtain the approximation (Rhodes, 2002) as follows:

V = 0.01612Y + 2.5649Y1/6 + 1.3455Y1/3 + 0.08797Y–1 – (2.685 × 10–7)Y3 – 3.116 [2.2]


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Land and Pinney (1955) proposed a simpler equation:

V = 2.468Y1/3 – 1.636 [2.3]

However, no simple relation has been reported so far for Munsell hue or chroma with respective CIE parameters. The NBS computer program (Rheinboldt and Menard, 1960) utilises a look-up table followed by interpolation but a simple and faster program has been proposed by Simon and Frost (1987). Artifi cial intelligence computer programs such as ‘artifi cial neural network’ (ANN) has been utilised to convert Munsell coordinates into CIE coordinates. Neural network models to imitate some functions of the human brain are described by Tominaga (1993).

If we set aside the small problem that a uniform colour space in three dimensions is impossible, then CIELAB does remarkably well. A standard basis for comparison is the distribution of Munsell target colours at Munsell values 4, 6 and 8, out to the limits of surface colour chroma. Ideally the diagram would look like radial spokes within concentric circles. However, Munsell hue loci showed (Hunt, 1978) to depart from straight radial lines having equal angular spacing and departures of the Munsell chroma loci from equally spaced concentric circles. The exaggerated spacing of Munsell chroma into the yellow and yellow-green hues, the displacement in the lines of constant hue as lightness increases (especially in the blue greens), the curving lines of constant violet and green hues and the wide gap in the hue spacing of green colours, are all primarily or partly due to irregularities in the CIELAB system although some are actually problems in the Munsell system.

As a rule of thumb, ten units of CIELAB lightness exactly match one unit of Munsell value, and ten units of CIELAB chroma approximately match two units of Munsell chroma. It may be noted that all CIE systems reverse the ordering of Munsell hues. The clockwise movement along the Munsell hue circle results in the change of hue from red to yellow, while in CIELAB the same change requires anticlockwise movement.

The chromaticities of the Munsell renotation data set were applied to eight colour-appearance models namely CIELAB, Hunt, Nayatani, RLAB, LLAB, CIECAM97s, ZLAB and IPT by Wyble and Fairchild (2000). In general, the models derived from the Munsell system performed well except for some deviations in hue spacing and linearity.

Limitations of the Munsell system

Munsell and his successors worked hard to produce colour samples that were ‘perceptually equidistant’ from neighbouring colours on the individual dimensions of value, chroma and hue. The relationship between Munsell step size and perceived colour is not constant across the three dimensions. The equality of visual spacing is such that one value step (on a scale of 10 between white and


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black) is equal to two steps in chroma and 0.3 major hue step (3 step on a 100 step hue scale) at chroma 5 (Nickerson, 1936). In a series of articles, Indow and Aoki (1983) applied multidimensional scaling to the Munsell chips and pointed out a number of minor deviations from perfect equal spacing, both locally and globally. Despite these known minor deviations from the goal of local equality of visual spacing, the Munsell is often considered, because of extensive documentation (some 3,000,000 colour judgements by 40 observers), as the standard against which other colour order systems can be compared.

Munsell realised that the natural colour space is highly irregular when it is represented without geometrical preconceptions. Thus, Munsell always conceived of his colour model as a sphere, but allowed for unequal dimensions of chroma at different levels of lightness and across different hues. The range of colours represented in a Munsell atlas is limited by the gamut of paints or inks used to create the colour samples. As a result, no simple geometrical form accurately represents perceptual colour space. All other colour models based on triangles, circles, squares, pyramids, cones, spheres, cubes or cylinders must (and do) grossly distort perceived colour relationships.

The Munsell system has a few inherent problems.

• A variety of discrepancies were found in the perceptual spacing of colours, depending on their location in the colour space.

• The quantitative difference between colours could only be defi ned on a single colour attribute (lightness, chroma or hue) at a time.

• Complementary colours are not on opposite sides, so that one cannot predict the results of colour mixing very well.

As the intervals of hue, value and chroma are not perceptually equal, it is desirable to have diagonal dimensions added to the Cartesian space for assessment of perceived colour differences in all directions of the colour space using identical psychometric tasks. In plain language, it is impossible to represent uniform colour differences in a three-dimensional colour model. The human colour space is non-Euclidean.

SCOTDIC colour atlas

SCOTDIC, a textile version of Munsell created by fusion of two quite different systems – Standard Colour of Textile (Japan) and Dictionnaire Internationale de la Couleur (France), is adopted by over 8 000 companies worldwide. Textile standard colours of the SCOTDIC colour system are widely used as colour tools by fashion colour professionals. The system has three versions – glossy (2 468 colours on polyester crepe fabric), matt (2 038 colours on cotton poplin fabric) and yarn (1 100 colours on wool yarns). It has incorporated many bright colours and the number on the constant hue chart has been increased to 54 (20 for wool).


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The SCOTDIC system uses a six digit code for each standard colour – the fi rst two digits for hue, the second two digits for value and the third two digits for chroma. The prefi x corresponds to the material of textile bases – P for polyester, C for cotton, W for wool. Therefore, the notation C-155010 means cotton standard sample having hue = 15, value = 50 and chroma = 10.

In a recent study (Roy Choudhury, 2008) of locations of SCOTDIC cotton samples in CIELAB colour space, the following observations were made.

• For most of the Munsell hues, hue angles vary between 10° and 20°, but for a good number of Munsell hues, the hue angles vary within much broader ranges. For a few borderline Munsell hues, the hue angles vary from 0° to 360°. But if we consider that hues belonging to hue angles 0° and 360° are identical, the range of variation is quite narrow.

• Figure 2.1 shows constant SCOTDIC loci in CIELAB space at a constant value level of 60. As expected, constant SCOTDIC hue loci form radial lines emerging from origin and for many hues the lines are curved, especially at high chroma. The hues show unequal angular spacing and in many cases the spacing has changed at high chroma.

• The loci of constant chroma represent near-circles with diameter increasing with increase of chroma. Theoretically all constant chroma points should have been located at constant radial distance from the centre of an a*−b*

1311141517192535374143495569738587939597

80

70

60

50

40

30

20

–60 60–50 50–40

–40

40–30

–30

30–20

–20

20–10–10

100

0

10

2.1 Constant SCOTDIC hue loci in CIELAB space.


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diagram. In reality, the points are somewhat scattered and the radial distance from the centre is higher for yellow hues, i.e. the near-circles are tilted towards a+b* direction. In other words, for all levels of SCOTDIC chroma, the yellow hues have higher b* values as compared to other hues of identical chroma.

The a*−b* values of identical SCOTDIC chroma but of different SCOTDIC values have not overlapped. Close observation shows at higher value levels, the near-circles tilted more towards a+b* direction as compared to near-circles of lower value.

• The study further showed that the actual SCOTDIC notations are quite different from Munsell notations predicted from refl ectance data.

2.4.2 Practical colour coordinate system (PCCS)

The Japanese Colour Research Institute (JCRI) developed two colour order systems – one of the largest collections globally, the ‘Chroma Cosmos 5000 atlas’ (1978) and a smaller collection, ‘Chromatogen 707’ (Birren, 1983). The numbers denote the number of chips in the respective atlas.

The chips in Chroma Cosmos 5000 are represented in usual Munsell notation of Munsell hue, chroma and value. The chips are arranged in the planes of constant chroma, on which value (0.5 to 9.5 in steps of 0.5) is represented against hue (40 equally spaced hues for all levels of chroma except for chroma = 1, where 20 hues are considered). Later eight hues were supplemented and a few high chroma blue and green hues were dropped.

Chromatogen 707 is based on the less known Japanese colour order system, PCCS. The system is a modifi cation of the Munsell system in which chroma is replaced by PCCS saturation which is constant for the purest colour available for each hue. The chips are arranged in seven planes of constant PCCS saturation, on which Munsell value and hue are variables.

Of the 5 000 chips in Chroma Cosmos 5000, only 1 325 have similar chips in the Munsell book of colour. Billmeyer and Loppnow (1988) reported that the chips are denoted by Munsell notations, but accuracy of many chips are poor, 31% of the chips had CIELAB colour difference greater than 3 units, 62% between 1 and 3 units and only 7% less than 1 unit against the corresponding Munsell chip. The maximum colour difference observed increases with increasing chroma. Of the 451 pairs having colour difference greater than 3 units, a large number of samples (60%) showed three or more cross-over of refl ectance curves against respective Munsell samples. So different sets of colorants were used for JCRI and Munsell chips and some degree of metamerism is inevitable.

Of the 83 chips in Chromatogen 707 having Munsell equivalence, only one had colour difference of 3 CIELAB units, 46 chips had the difference between 1 and 3 units and 36 less than 1 unit against the corresponding Munsell chip. So the latter system is more reliable.


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2.4.3 Natural colour system

The natural colour system (NCS) was developed in Sweden (Härd and Sivik, 1981). It is accepted as the national standard of Sweden and many other European countries. By 1611 the scientist A. S. Forsius published the embryo of the NCS in his book Physica. The system is based on six primary colours suggested by Leonardo da Vinci (Hesselgren, 1984) and an opponent colour scale proposed by Hering (1872–4). Johansson (1937) fi rst defi ned the concept of a ‘natural colour system’ and the Hesselgren (1952) colour atlas having 507 samples came onto the market in 1952. After thorough research at the Swedish Colour Centre Foundation, the fi rst commercial atlas containing 1 412 samples was brought into the market by the Swedish National Building Research Foundation in 1979. A second revision was made to improve the accuracy of the samples and to exclude pigment containing harmful lead and cadmium in 1995 and the number of samples was raised to 1 741 with corrected notation of boundary samples and inclusion of low saturated samples.

In this system six elementary colours, namely white (W), black (S), yellow (Y), red (R), blue (B) and green (G), are perceived as pure colours and cannot be described by other than themselves. All other colours can be described on the basis of their resemblance to these six elementary colours. The colour names in capital letters indicate pure or full colour and the colour names in small letters indicate the colour content.

The three fundamental variables used by NCS are hue, blackness and chromaticness, i.e. the intensity of the colour sensation. Colour Plate VI (a) shows the NCS constant hue triangle with three corners, namely white (W), black (S) and pure chromatic colour (C). The distance of the location of the test colour from the corners indicate the whiteness, blackness and chromatic content respectively. The NCS colour triangle is a radial plane, normal to the hue circle, which shows samples with the same hue.

NCS hue, ϕ, is defi ned as degree of resemblance of the test colour to the nearest two chromatic elementary colours. Y80R indicates 80% resemblance to red and 20% to yellow. The NCS hue circle, shown in colour Plate VI (b), is a horizontal plane showing samples with the same whiteness (or blackness). In the atlas, the samples are at every 20th hue step, starting at Y10R – namely Y10R, Y30R, Y50R, Y70R, Y90R, R10B, etc., ending with G90Y. The colours are placed in a polar coordinate system as in the Munsell system. While the 20 hue steps are acceptable for less chromatic colours, the perceived total difference or interval between the hues will be too large for strongly chromatic colours. Hence for chromaticness, c ≥ 40, the number of hues has doubled by adding extra hues as Y20R, Y40R, etc. In the second edition of the atlas, about a hundred more samples have been included having high whiteness and chromaticness, c ≥ 40. The acceptable tolerance is ±2 units for all NCS attributes.

The sequence of NCS hues is similar to the CIELAB arrangement and opposite to the Munsell system. The hue circle sequence is Y → R → B → G → Y. It follows that Y50R is an NCS hue code, but R50Y is not.


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The opponent colour theory suggests that we can perceive the following two chromatic attributes simultaneously:

• Yellowness and redness (e.g. Y80R)• Redness and blueness (e.g. R20B)• Blueness and greenness (e.g. B70G) or• Greenness and yellowness (e.g. G50Y).

In a single colour element, it is not possible to perceive the two chromatic attributes, yellowness and blueness or redness and greenness in combination as described by opponent colour theory.

Saturation (m) is expressed with a number between 0 for the achromatic colour and 1 for colour devoid of whiteness and is symbolised by the line S-C. The natural colour system is based on unique psychological perception. The hue-difference between the neighbouring colours is not the same. For example, if at constant lightness and chroma there are 10 equal hue steps between unique yellow and unique red, there may be 20 to 50 hue steps of the same size between unique red and unique blue.

The colours of different hues, but having equal NCS blackness and chromaticness are described by the colour designers as having a certain equivalence called equality of nuance or weight. From the NCS notations of nuance and hue, the relationship between colours can be illustrated graphically in a three-dimensional model called the NCS colour solid, having biconical shape limited by two points, white (W) and black (S). Every imaginable colour percept of the surface mode can be defi ned by a point and each point in NCS space denotes only one colour. When the NCS space is seen from above, the NCS hue circle can be seen. The side projection of a half of the NCS space is the colour triangle. The NCS system is diagrammed as equilateral triangles, but the maximum purity colours (called maximal colours) are not at the end point of a triangle. In over half the hues, the full colour is sampled by two or three colours in a vertical row inside the triangle. This is known as the oversaturated area. No simple correlate of CIE lightness or Munsell value is proposed in this system, as NCS blackness is claimed to be more readily perceived.

NCS chromaticness, c, is the resemblance of the test colour to the colour of the same hue having maximum possible chromatic content. It can also be defi ned as the sum of the chromatic elementary attributes (redness, yellowness, greenness and blueness) of a colour.

c = r + y + g + b [2.4]

The most visually intense shade of a surface colour is defi ned to have a chromaticness of 100 and a blackness of 0 (and a whiteness of 0). A slightly less intense shade of the same hue may have a chromaticness of 80, for example. The lightest possible shade with same intensity of colour has a whiteness value of 20 and a blackness value of 0. The darkest possible shade with the same intensity


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of colour has a whiteness value of 0 and a blackness value of 20. NCS blackness (s) or NCS whiteness (w) is the resemblance of the colour to the perfect black or white respectively.

The triangle shape arises as a consequence of the rule that the combined attributes of chromaticness, whiteness and blackness add up to exactly 100.

s + w + r + y + g + b = w + s + c = 100 [2.5]

Since the sum of the attributes adds to 100, it is only necessary to quote two of the attributes. The two attributes chosen were blackness and chromaticness. The third attribute, whiteness, is easily obtained by the difference of the sum of the other two from 100,

w = 100 – (s + c) [2.6]

The NCS notation starts with an S, indicating that it is the second edition. Next the nuance is described by four fi gures (the nuance is the combination of blackness and chromaticness). Lastly, the hue is described by two fi gures between the two characters for the two sounding elementary colours. The colour S 2060-Y80R (Swedish standard, sc-ϕ), for example, has blackness (s) = 20, chromaticness (c) = 60 and the hue = Y80R (red with 20% yellow). These are shown in colour Plate VI (a) and (b) respectively.

The following deductions can be derived from the above notation:

The whiteness, w = 100 – (20 + 60) = 20.Redness, r = c × %R = (60 × 80)/100 = 48Yellowness, y = c × %Y = (60 × 20)/100 = 12 or as c = r + y, y = 60 – 48 = 12.

The saturation, m is the relationship between the chromaticness and whiteness.

m = c/(c + w) [2.7]

Pure grey colours have no hue and are given nuance notations followed by -N to describe neutral. The pure grey scale is a scale from white to black and the samples are provided from 0300-N which is white, to 9000-N which is black.

Among strong chromatic colours of different hues, some appear lighter than others even if they are of the same nuance. Hering described that strongly chromatic yellow has an inherent lightness and strong chromatic blue has an inherent darkness. Lightness has been considered in the NCS system as a quantity of intensity determining how distinctly colours contrast to one another. Accordingly, NCS lightness value (v) of a chromatic colour is determined by comparing the test colour sample with the reference scale samples, with the colours juxtaposed and on the same plane (Härd et al., 1996). The chromatic sample is defi ned as having the same lightness as that grey reference sample against which its border appears to be minimally distinct (MGT) and the lightness of the grey sample is calculated from its blackness value, s, as follows:


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Lightness, v (for grey sample) = (100 – s)/100 [2.8]

Like the Munsell system, the NCS is embedded in an atlas containing samples of 40 different hues and samples in steps of ten along the blackness and chromaticness scales.

The NCS can be interpreted to have a fundamental greyness-chromaticness plane, no whiteness or blackness can be perceived in them. Some non-uniformity observed in chromaticness and hue spacing while analysing the NCS system with a nonlinear colour-appearance model, probably resulted from inaccuracy in the assessing and scaling method. Based on the analysis, a method is developed to predict NCS colour notations from colorimetric values x, y, Y using a nonlinear colour-appearance model (Nayatani et al., 1989).

The spacing of NCS aim points in the CIELAB system has been studied by Derefeldt and Sahlin (1986). The aim points had been interpolated to derive CIE values of 16,000 notations in Swedish Standard, SS 01 91 01 (1983), but no accurate analytical relations between the NCS and CIE systems could be derived as in the Munsell system.

When the samples are specifi ed by CIE tristimulus values based on extensive visual observations, there may be abstract or conceptual systems, which do not require use of an atlas. The NCS system claimed to refl ect universal perceptual processes (Härd and Sivik 1981); hence the conceptual version may be used independent of the atlas version. It is also stated that people without the knowledge of colour assessment can understand and describe the NCS notations of object colours in the absence of an atlas, after short training of about 15 minutes. But another study (Whitfi eld et al., 1988) showed that the accuracy of colour identifi cation is over-estimated and the performance of the Munsell colour order system in similar situation is not much different.

2.4.4 Ostwald system

The German chemist and Nobel laureate, Ostwald, who met Albert H. Munsell in 1905 on a journey to America, attempted to devise a colour order system, similar to that of the American painter, based on perception and equalising the respective differences between individual colours. Expressed in our modern technical language, we can say that Ostwald attempted to construct a perceptual colour-system using non-empirical methods. In place of Munsell’s three parameters, he selected an alternative group of variables: namely, colour-content, white-content and black-content. He also introduced the special term ‘full colour’, by which he meant a colour which permitted the sensation of one single colour-tone (Munsell ‘hue’) and was not tempered by white or black. To be more accurate, we could say that a full colour is an optimally pure colour – in other words, of maximum saturation and at the same time bright. Full colours are, of course, ideal colours which cannot be reproduced by actual pigments. (When Ostwald published his


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Colour Primer, his full colours contained about 5% white and slightly less black, as he himself admitted.)

Ostwald described a double-cone colour solid of related colours based on additive disk mixture. Ostwald (1915, 1916) devised this colour order system and the various physical exemplifi cations, including the ‘Colour Harmony Manual’ (1942–72), once developed but no longer available. The system was favoured by artists and designers because of the similarity of the construction and artist’s method of preparation of colour mix and has immense historical interest (Derefeldt, 1991). Ostwald’s original system and ‘Colour Harmony’ contained 24 hues, later the number of hues was increased to 60 in the ‘Swiss Colour Atlas 2541’ (SCA-2541) and related atlases (Müller, 1962–5).

Like the NCS, Ostwald’s system is also based on Hering’s opponent colour theory. The hue circle was set up with Hering’s four unique hues – red, yellow, green and blue. However, instead of perceptually opponent hues, colorimetric complementary hues (i.e. hues lying on opposite sides of a white point in the chromaticity diagram) are placed opposite to each other. The hues, particularly the last two, are different from the NCS unique hues. The opposite hues, combined in proper proportions in a rotating spinning disk, must appear neutral grey. Other hues are selected by equal visual spacing in each quadrant and are represented by constant dominant wavelengths. The Ostwald hue circle (colour Plate VII (b)) begins with yellow at the 12 o’clock position and proceeds clockwise to red, blue and green, unlike the Munsell system where it starts from red and proceeds to yellow, green, blue, etc. The opposite hues are complementary and give achromatic colour when mixed optically. Ostwald’s colour circle consists of a sequence of 24 hues divided into eight groups of three, named yellow, orange, red, purple, blue, turquoise, sea-green and leaf-green.

Like the NCS, the Ostwald system defi nes all colours as a mixture of full colour (r), black (s) and white (w) and the constant hue is represented in tri-linear space of full-colour content, white content and black content (colour Plate VII (a)). The important difference between the two systems is that in the NCS such planes are defi ned according to perceptual colour scale, while in the Ostwald system the planes are defi ned by additive colour mixtures of the three maxima located at the corners of the triangles. The system was set-up with colour appearance in mind (using Hering’s theory), but the samples were selected from additive colour mixing. In other words, the system represents a combination of a colour appearance system and a colour mixture system (Fairchild, 2006). This is based on Hering’s colour equation: r + s + w = 1. The equation was interpreted in terms of refl ectance data. However, the real colours used in the disk mixture did not have idealised refl ectances.

The ‘Ostwald solid’, constructed of equilateral triangles, is much simpler in structure as compared to the Munsell solid. Colour Plate VII (a) shows a vertical cross-section through the Ostwald double-cone solid with two complementary hues (hues 1 and 13) at two ends shown as full colours. The central vertical axis is a grey scale. The full colours locate on the periphery of the central plane. All


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colours of a particular hue are placed on an equilateral triangle, i.e. half of the vertical section through the centre of the double cone shown in the fi gure is the constant-hue page of the Ostwald system. The runs of colour in this system are straight, symmetrically arranged and the end point colours are easily recognisable. As in Hering’s constant hue triangles, lines parallel to the line joining full colour and white represent colours of equal blackness and those parallel to the line joining full colour and black represent colours of equal whiteness. The colours of equal purity lie on lines parallel to the line joining black and white.

For agreement between psychological scaling and psychophysical colour solid, Ostwald applied the Weber and Fechner law, which states that the perceived magnitude of a stimulus is proportional to the logarithmic of the physical stimulus intensity. Sixteen grades (‘a’ to ‘p’) in each direction, i.e. from black to white, black to full colour and white to full colour, resulted in 15 visual equidistant steps There are 120 chromatic samples in a triangle and a double letter system (black and white content) in addition to the hue number is used for their identifi cation. The colour solid can be sliced in four different directions, namely equal hue, equal whiteness, equal blackness and equal purity.

The three variables in the Ostwald system are hue, lightness and saturation, with saturation scaled in relation to full colours or optimal colours. The theory of optimal colour stimuli was developed by Austrian physicist, Schrödinger in 1920 and further developed by Rösch in 1928. MacAdam (1935) calculated chromaticity loci of optical colour (called MacAdam limits) as a function of luminous refl ectance Y and these are different from the NCS colours of 100% chromaticness. Most saturated colours available at the time were used as ‘full colours’, whereas 100% chromatic colours are imaginary end points. No analytical relation has been proposed between the Ostwald and CIE systems.

The Ostwald colour system remained popular for several decades following its introduction, but has now been very largely superseded by the American Munsell and Swedish natural colour systems. This is primarily because the original colours chosen for the system were laid out in such a way that (unlike the Munsell system) their arrangement could not be modifi ed or extended as pigments and dyes of greater saturation were brought onto the market.

2.4.5 DIN system

Work by Dr Manfred Richter on the DIN (Deusches Institut für Normund) system started in 1930 with the intention to substitute the older Ostwald system. The fi rst physical embodiment with 600 matt samples was produced in 1960–2, a glossy edition with 1 000 samples was released in 1978–83 and then colorimetrically specifi ed as German Standard DIN 6164 (Richter and Witt, 1986).

DIN colour solid forms a modifi ed double cone with distance from the full hue plane to white much shorter than to black. The system defi nes three scales – darkness degree (D), DIN hue (T) and saturation degree (S).


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Darkness degree (D) is the relative lightness scale with respect to that of an optimal colour having the same chromaticity. This is calculated as follows:

D = 10 – 6.1723 log (40.7 Y––Y0

– 1) [2.9]

where Y0 is the maximum possible luminous refl ectance of the optimal colour of the same hue defi ned by MacAdam (1935). For ideal black D = 0 and for ideal white D = 10. The scale is similar to NCS blackness rather than the Munsell value.

DIN hue (T) has the usual meaning utilising 24 equally spaced hues of Ostwald hue circle with some simplifi cation by defi ning lines of constant hue to be straight line radiating from white point in the chromaticity diagram. The hue circle starts with greenish yellow at 12 o’clock, followed by (clockwise) unique yellow (DIN colour 2), unique red (DIN colour 9), unique blue (DIN colour 17) and unique green (DIN colour 21); the missing colour numbers are intermediate hues.

Saturation degree (S) is the chromatic amount measured by the perceptual distance from an achromatic sample of the same luminance factor. It is calculated as follows:

S = [(u ' – 0.2105)2 + (v ' – 0.4737)2]1/2/r1 [2.10]

where (u ', v ' ) are the CIE 1976 chromaticity coordinates of the colour; (0.2105, 0.4737) are the coordinates of illuminant D65; r1 represents saturation distance and is computed from r = r6/6; r6 is obtained by interpolation from a table according to the values of T and S.

Six saturation steps were visually scaled at one lightness level only and extrapolated to other levels. Colours of equal saturation degree are located on roughly elliptical contours in the chromaticity diagram.

The DIN colour chart based on the DIN system includes constant hue pages with rectangular sampling of darkness and saturation. Columns on a DIN page represent constant DIN saturation (constant chromaticity) and appear as shadow series (same object seen at different levels of illumination of same illuminant). The system is useful to illustrate the difference between chroma and saturation as it shows how the saturation is related to a shadow series, i.e. an object illuminated by decreasing illuminance levels of the same spectral power distribution.

The methods for conversion of CIE and DIN coordinates have been discussed by Richter and Witt (1986). A number of compromises were made to keep a simple relation between DIN and CIE coordinates. The guiding principle of the DIN system is equality of visual spacing, but the equality of visual spacing was maintained locally and not globally in all three dimensions.


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2.4.6 OSA-UCS system

If all the corners of a cube are sliced off down to the midpoint of each edge, a special form will result which mathematicians call a cubo-octahedron. Such a structure, with a centre and 12 corner points, was used in 1960 by the Optical Society of America in the design of their colour system.

Intensive studies during and after the Second World War showed that it is diffi cult to represent hue, chroma and lightness in a Euclidean system. The committee on Uniform Colour Scales set-up by the Optical Society of America in 1947 proposed the OSA Uniform Colour Scale or OSA-UCS system which was described by MacAdam (1974, 1978). In spite of being a colour appearance system, it is quite different from the Munsell or NCS systems. It was claimed that best uniform visual spacing can be achieved on a regular rhombohedral (equal-sided polygon) lattice, allowing closest uniform spacing in three dimensions. In 1953, the committee aimed to produce 500 chips. However, in 1967 the committee concluded that such an ideal space does not exist and modifi ed its objective to the production of the best approximation to such a lattice for a neutral (/6) background. The committee also noticed the paucity of near-neutral samples and decided to add a series of such samples at half steps centred on L = 0. The near-neutral sample set consists of 134 samples range from L = –1.5 to L = 1.5. The revised atlas consisting of 558 chips (424 in regular set and 134 in pastel set) was produced in 1976.

The system is not based on the separate scaling of three attributes like Munsell or NCS. In order to make each sample equally spaced from each of its neighbours, a regular rhombohedral 3-D space is required (Billmeyer, 1987) in which each colour (not lying on the boundary of the object colour solid) is surrounded by 12 neighbouring colours, all at perceptually equal distance from the given colour. If the 12 points of the nearest neighbours are connected, they form a polyhedron known as a cubo-octahedron (Fig. 2.2). The objective of equal colour differences in all directions results in a very different type of colour order system.

The OSA space is designated in a three-dimensional Euclidean geometry, similar to opponent colour scale, named lightness (L), yellowness/blueness ( j, from the French term jaune) and greenness/redness (g). Figure 2.2 shows the cubo-octahedron along with locations of L, j and g axes, central colour (M) and 12 neighbouring lattices. The signs of the attributes have meaning similar to the opponent colour scale: –L (light), +L (dark), +j (yellow), –j (blue), +g (green), –g (red). For the samples in the atlas j ranges from –6 (blue) to +12 (yellow), g from –10 (red) to +6 (green), L from –7 (dark) to +5 (light). The colour having L = j = g = 0 is neutral grey with 30% refl ectance, similar to Munsell N/6.

Hue and chromatic amount has no meaning in the OSA system. However, the ASTM subcommittee E12.07 has recently proposed the concept of OSA hue and OSA chroma.

OSA hue = arctan (g/j), OSA chroma = ( j2 + g2)1/2 [2.11]


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The OSA-UCS system uses a rectangular coordinate system in which unit spacing on the vertical (lightness) axis is √2 times on the horizontal chroma axis.

Actual physical distance between two colours = [2(ΔL)2 + (Δj)2 + (Δg)2]1/2 [2.12]

Δ indicates the difference in respective attributes of the two colours.Each colour can be displayed as a part of a two-dimensional array by cutting

at various planes – horizontal, vertical and oblique. For the horizontal plane, L = constant.

OSA-UCS colour solid is proved to be visually uniform for fairly large colour dissimilarities (14–15 just-noticeable difference units, the approximate size of UCS full step), not for small colour differences (Taylor and Billmeyer, 1988).

CIE tristimulus values can be converted into L, j, g coordinates by a series of mathematical equations (MacAdam, 1974; Taylor, 1984), unfortunately the equations are not invertible. The equations and sample point specifi cations can be found in Wyszecki and Stiles (1982).

The CIE tristimulus values are converted to cone sensitivity functions (R, G, B) that are much different from those of Smith and Pokorny.

R10 = 0.799X10 + 0.4194Y10 – 0.1648Z10

G10 = –0.4493X10 + 1.3265Y10 + 0.0927Z10

B10 = –0.1149X10 + 0.3394Y10 + 0.717Z10 [2.13]

Lightness and two chromaticness coordinates are calculated as follows:

–j

+j

–L +L

+g

–g

M

2.2 OSA-UCS cubo-octahedron.


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Lightness, L = 5.9 [ Y1/30 – 2

3 + 0.042(Y0 – 30)1/3] [2.14]

where:

Y0 = Y(4.4934x2 + 4.3034y2 – 4.276xy – 1.3744x – 2.5643y + 1.8103)

yellowness/blueness, j = C(1.7R1/3 + 8G1/3 – 9.7B1/3)

greenness/redness, g = C(–13.7R1/3 + 17.7G1/3 – 4B1/3) [2.15]

where:

C = 1 + 0.042(Y0 – 30)1/3

Y1/3 0 – 2/3

The variable C adjusts chromaticness for the lightness crispening effect and the variable Y0 adjusts lightness and chromaticness for the Helmholtz – Kohlrausch effect.

2.4.7 Coloroid system

This Hungarian colour order system has been designed particularly for the use of architects and designers by Nemcsics (1987, 1993, 1994) and co-workers at the Technical University of Budapest. The system aimed at spacing colours evenly in terms of their aesthetic effects rather than in terms of colour differences as in the Munsell system or perceptual content as in the NCS system. The Coloroid system introduces the phrase ‘aesthetically uniform colour space’ for the fi rst time. A scale is regarded as being aesthetically uniform when it appears to an observer as both complete and exhibiting gradual change. The idea behind this construction will become clear with the realisation that, when planning a coloured environment, a harmony must be created for colours with regard to hue, saturation and brightness. For the designer, aesthetic uniformity is more important than the ability to accurately register small differences in colour and then repeatedly reproduce them at the same value. For him harmonious interplay of the colours was more important than the actual differences between them. A series of experiments of great dimensions have been processed between 1962 and 1996 at the Technical University of Budapest, Hungary, and also in other countries, in order to formulate rules of colour harmony and describe aesthetic relationships. Nearly 80 000 observers performed 26 million elementary observations.

The equality of spacing is considered to be ‘evenness’ of appearance in all scales of colours in the system and was done by extensive visual scaling based on harmony threshold instead of perception threshold. In harmony scaling, neighbouring colours are compared with a given group of colours and not with all hues. The colours are divided into fi ve groups – yellow, red, purple, blue and green. The largest deviation between these two types of scales occurs in the green and purple colour ranges. These are areas where colour scales of the Munsell and DIN systems (perceptual scaling) differ most from the Coloroid system (harmony


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scaling). The Coloroid system contains proportionally fewer purple and more green samples than the above two systems.

Like the Munsell system, the colours are arranged inside a normal circular cylinder with achromatic colours along its axis closed by absolute white and absolute black at two ends and hues varying with the angular coordinate. The system represents colours by three numbers – hue (A), saturation (T) and lightness (V). The system is composed of 48 basic hues having constant dominant or complementary dominant wavelength, numbering 10–76 with some missing numbers. Intermediate hues are represented by decimal numbers (e.g. 12.673). The extreme reds and violets, beyond dominant wavelengths 625 and 450 nm respectively are omitted from the system. In the Munsell system as many as four hues may have the same dominant wavelength depending on saturation. Again the dominant wavelength of colours having the same Munsell hue, say 5YR, but different lightness and chroma, may vary. The Coloroid hue value, beyond the notation ‘A’, can be expressed also by the angular value φ around the D65 white point in the CIE xy system.

Coloroid saturation ‘T’ is defi ned as the percent spectral colour (or the non-spectral purple) in an additive mixture with perfect black and perfect white to match the colour. A linear relation exists between excitation purity and Coloroid saturation. The relation between Coloroid saturation and Munsell chroma is as under:

T = kAVC2/3 [2.16]

where kAV is a variable depending on hue and lightness and ‘C’ is the Munsell chroma.

Coloroid lightness ‘V’ is defi ned as a square-root function of luminance factor claimed to produce optimum aesthetically even spacing. The lightness scale is developed from the equal harmonic interval between absolute white and absolute black. Grey scales of the Coloroid system are reported to vary visually uniformly, whereas those of the Munsell and DIN systems vary more gradually in the darker ranges. Both the Coloroid saturation and lightness are represented on a scale of 1–100.

The relation between CIE Y and Coloroid lightness V is same as that of Hunter (1942):

V = 10 Y1/2 [2.17]

No analytical relation has been proposed for Coloroid hue and saturation with CIE tristimulus values; however they are directly related to dominant wavelength and excitation purity, which can be linked with the CIE chromaticity diagram.

Hirschler (2008) criticised the Coloroid system as follows:

• The system is full of contradictions. It was originally launched as a perceptually uniform system, but after a lot of debate it was decided to call it aesthetically uniform.


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• Nemcsics investigated unevenness in Munsell colour spacing. At several regions of colour space, the spacing in the Munsell and Coloroid systems are different. This is probably because uniform variations of colour stimuli at several regions elicit uneven variations of colour perception, or human colour perception is uncertain in these regions of colour space.

• The coordinates for basic hues in the original Coloroid system are specifi ed to three decimals (e.g. 0.001 nm), which is absurd.

• The Coloroid system treats spectral colours as if they were surface colours and make them the ‘basic colours’ of the system. Moreover pure red and pure blue are considered as primary colours for both additive and subtractive colour mixing. Both are contradictory to generally accepted views.

2.4.8 Other less known systems

There are a few less known and newly developed colour order systems such as the RAL system, Chevreul, Pope colour system, Colorcurve, Eurocolour system, Acoat system, etc. Some of these systems are defi ned by a set of aim points specifi ed in the CIE system.

RAL system

RAL atlas (BS: 5252) is a successor to the German DIN atlas and is based on CIELAB colour space. It comprises 1 688 colours, each with a seven-digit notation describing hue, lightness and chroma, e.g. RAL 210 60 30. Hue, the horizontal angle in the colour space, runs from 010 to 360 – in 10° increments, so there are 36 hues. Lightness, the vertical axis of the colour space, runs from 0 (black) to 100 (white). Chroma corresponds to the distance from the vertical axis, with achromatic colours at zero chroma. Saturated (maximum chroma) colours vary from hue to hue and with lightness so, as in the CIELAB and Munsell colour spaces, the envelope is an irregular shape.

Chevreul colour order system

M. E. Chevreul (1786–1889), a French chemist and director of a famous French manufacturing company, described a three-dimensional colour space (Heila, 1991) in the form of a hemisphere in which 12 pure colours and gradations, with the adjacent colour running clockwise, formed a 72-hue chromatic circle from the base. Twenty grades of lightness of the corresponding hue are located on radial lines from the white centre, ending in black on the periphery of the circle. In this ladder the full colour of each hue is located at its appropriate level of lightness. However, Chevreul later placed all full colours on the same grade. The modifi cation of pure colour by grey forms the upper part of the hemisphere in the form of a quadrant, the radius of which is equal to that of the circle. The Chevreul system


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was never fully illustrated. Ten 72-hue circles form the full colours with increasing amounts of black and twelve constant hue ladders represent the key colours of the base plane and were prepared by Chevreul using paper printing techniques. The Chevreul colour order system was useful for the artists desiring to paint in a naturalistic manner as well as for those who were interested to know the change of colour due to light effects.

Pope colour system

The rounded double cone with tilted central plane colour solid proposed by American art educator Arthur Pope (1880–1974) is based on the subtractive mixing of pigment colorants, primarily to assist the understanding of the relationships among colours produced by the artists putting less importance on colour notations and more an visual perception (Heila, 1988). The system has three attributes, namely: value, hue and intensity. Twelve pure colours (of 100% intensity) are placed at equal distances from the neutral axis. Scales of purity (i.e. the degree of blackness) and brilliance (i.e. the degree of whiteness) lie on the two clearly placed and visually evident planes. This is similar to the black or white content of the Ostwald system. Pope, however, acknowledged that his system is based on approximations rather than scientifi c measurement.

Colorcurve colour order system

This colour communication system (Stanziola, 1992) represents a combination of colour appearance and colour mixture systems. Eighteen constant lightness planes were constructed using CIELAB space and L* levels ranging from 30 to 95 in steps of 5. A few extra levels at higher L* were included for light colours which are popular for wall paint. At each lightness level, nine hue points with specifi ed (a*, b*) values, i.e. of specifi c hues, were defi ned using principles of colour appearance space.

Each quadrant of the a*b* plane was fi lled with a rectangular sampling of additive mixtures of grey and three chromatic (hue) starting points in that quadrant. Equal steps in the Colorcurve designations represent equal additive mixtures between the four starting points. After defi ning the aim points by additive mixing, the standards were formulated with real pigments. The system is represented by two atlases on nitrocellulose coated paper – the master atlas with 1 200 standards at 18 lightness levels and 956 additional standards in a grey and pastel atlas. As standards are specifi ed by spectral refl ectance characteristics, it is possible to prepare a spectral match. The samples thus prepared are universal matches and the viewing illumination is not important in these cases. This is not possible with other colour order systems. The standards in the atlas are circular and not square as in other systems. This avoids contrast illusion of dark spots at the corners between the square samples (Hermann grid illusion) (Fairchild, 2006).


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Eurocolour system

The Eurocolour system exhibits planes of constant CIELAB hue angle on which CIELAB chroma and lightness are variable. An atlas was published by Schwabenmuster in Germany (Gall, 1984), but it is no longer available.

Acoat system

An atlas based on the Acoat system was published by Sikkens GmbH (1978) in the Netherlands for paint industries. Using the techniques of colorimetry, the Acoat colour coding (ACC) system was intended to facilitate the uniform supply of colour-batches and colour-charts while attaining clarity through even spacing in the system and economy through the avoidance of complex conversion procedures between perceived and actual values; to offer, in other words, economically priced colour samples (Döring, 1981). The ACC system comprises a cylinder with a base circle divided into 24 colour segments arranged alphabetically. The two other parameters, brightness and saturation, can register 100 graduations numbered 00 to 99. The chromatic content does not distinguish saturation or chromaticness (Krewinkel, 1979).

2.5 Comparison and interrelation of various systems

The principal goal of colour order systems is to facilitate the specifi cation and communication of colour information. The existing colour order systems will not be superseded by a single universal system because (Rhodes, 2002):

• different systems have already been adopted as either national or industry standards,

• many users are highly experienced with a particular system and the change in the system is time consuming, expensive and less attractive,

• the historical data, such as colour differences in a system, are diffi cult to transform into other forms.

In the absence of a universal system, the colour communication may need interrelation and conversion between existing systems. The computer software has been developed for inter-conversion, but the source code has not been published.

Smith et al. (1990c) compared different colour scales, using the OSA-UCS as a benchmark for comparison. OSA-UCS atlas samples were mapped on to other colour spaces to check the perceptual spacing of the respective colours atlases. They observed that

• the NCS system is most radically different in hue spacing from that of the OSA-UCS system,

• the OSA chroma, Munsell chroma and NCS chromaticness have similar but non-identical axis,


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• OSA chroma, DIN saturation and Coloroid saturation are distinctly different from each other,

• NCS, DIN and Coloroid achromatic scales are distinctly different from the OSA-UCS lightness scale, and

• the Munsell and OSA-UCS spaces are closer.

Judd and Nickerson (1975) derived idealised relations between the NCS and Munsell systems. Billmeyer and Bencuya (1987) found that no simple relation could be written between NCS hue, NCS chromaticness and NCS blackness against Munsell hue, Munsell chroma and Munsell value respectively. However, they were convinced that the two systems sample the same underlying colour space. No analytical relation could be formulated, possibly due to incompatibility of their respective aim points. It is suggested that some smoothening of NCS aim points may be necessary, as for Munsell renotation in the Munsell system. One reason for the difference between the two systems is reported to be the lower precision of the magnitude estimation technique used to scale the NCS system as compared to the ratio scaling technique used to scale the Munsell system.

A colour notation conversion program was developed (Smith et al., 1990d) for mutual conversion between the Munsell, OSA-UCS, NCS, DIN, Coloroid and CIE systems. The conversion was based on the principle that the colour order systems are defi ned by their aim points defi ned by CIE coordinates. Conversion from one system to another, therefore, can be achieved by converting the given point in the source system onto CIE colour space and then by converting the coordinates onto the target colour space.

Two problems are associated with this conversion. First, illuminant, illuminating and viewing conditions should be same in both cases. Correction is not possible for variations in the above conditions. Rhodes (1995) tried to compensate the differences in illumination conditions and media through the application of a colour appearance conversion model. Secondly, the aim points and actual samples are not necessarily the same.

Smith and Billmeyer (1994) compared the attributes of different colour order systems which can be summarised as follows.

2.5.1 Representation of hue

The OSA-UCS and Colorcurve systems use a grid arrangement having only four constant hue planes. Most of the other colour order systems represent constant hue along radial lines. All but the NCS colour order system have colours spaced at visually equal steps around an achromatic axis. NCS is based on four elementary colours located 90° apart on opponent axes as is the CIELAB colour scale. In the Munsell system, the hues are so arranged that equal small hue differences occupy equal angles around the entire hue circle, and hence the unique hues are located at irregular angular spacings – red to yellow 67°, yellow to green 75°, green to


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blue 90° and blue to red 128°. This difference in hue spacing is because the NCS system is based on colour-appearance magnitude, while the Munsell system is based on colour-appearance differences. Since the Munsell system is based on polar coordinates, the samples at chroma 4 have larger hue difference than those at chroma 0.5.

The samples of Colorcurve are based on cartesian coordinates, with samples at the corner of a square grid. Compared to the Munsell ‘Nearly Neutral Collection’, the ‘grey and pastel’ Colorcurve atlas furnishes samples of higher lightness, however the number of samples in both the atlases are approximately the same – 1 132 in Colorcurve and 1 100 in Munsell.

2.5.2 Representation of chroma/saturation

The notations of chromatic amount are of three types – chroma, saturation and a combination of whiteness, blackness and chroma. Munsell chroma is independent of Munsell value. DIN saturation and Coloroid saturation are radically different in spite of the fact that both take into account the effect of lightness. In a perceptual uniform colour space, colours of equal Munsell chroma lie on the surface of a cylinder whereas colours of equal DIN saturation lie on the surface of a cone (Robertson, 1984). In the NCS and Ostwald-based Swiss Colour Atlas (SCA-2541) systems, the concept of chroma is tightly bound between two achromatic scales – the sum of the chroma, blackness and whiteness values is always constant.

Smith and Billmeyer (1994) summarised that the three approaches to chromatic scales are not comparable. NCS and SCA-2541 chroma are more close to Munsell and OSA-UCS chroma than DIN and Coloroid saturation.

Judd and Nickerson (1975) attempted to derive chroma-chromaticness conversion and postulated a simple proportionality,

NCS chromaticness, c ≈ 5 × C (Munsell chroma) [2.18]

Billmeyer and Bencuya (1987) found a still good approximation as

C = Ac + B [2.19]

where A and B vary with hue.

2.5.3 Representation of achromaticity

There are two approaches producing substantial underlying scaling differences:

1 Lightness axis in Munsell, OSA-UCS, Colorcurve, Coloroid and DIN (opposite notation Darkness).

2 Blackness-whiteness-chroma combined relationship of NCS and SCA-2541.

Billmeyer and Bencuya (1987) suggested the following relation for achromatic colours between Munsell Value (V) and NCS blackness (s).


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V = 10.03 – 0.1248s + 1.209 × 10–3s2 – 8.793 × 10–6s3 [2.20]

The relation for chromatic colours in Munsell and NCS systems is considerably complicated.

2.6 Accuracy of colour order systems

The accuracies of the NCS, DIN and OSA-UCS atlases have been studied (Smith et al., 1990a). It was found that the accuracies for the DIN and OSA-UCS systems are similar. Initially it was reported that these systems are on average 3½ times more accurate than NCS colour atlas samples. However it was corrected further (Smith et al., 1990b), saying that the errors of NCS atlas samples on average are approximately one ΔECIELAB unit. The error for the DIN and OSA-UCS systems varies between 0.11 and 6.48 ΔECIELAB units, whereas that of the NCS system varies between 0.04 and 16.21 ΔECIELAB units. The major source of inaccuracy for NCS samples present on the edge of the NCS colour solid. The samples of NCS blackness = 0 or NCS whiteness = 0 are highly inaccurate (Smith et al., 1991). However Döring (1995) pointed out that the accuracy of NCS samples are independent of chromaticness. In the outside gamut of the NCS solid, all colour samples deviate from their aim points systematically towards the centre of the colour solid. For chromaticness greater than 50, the accuracy decreases slightly.

A visually ordered colour atlas permits selection of not only specifi c colours found in the set, but also of a way to specify many intermediate colours by visual interpolation. A set of 1 000 colours may allow one to visualise and specify 100,000 colours. The accuracies of visual interpolation for various colour order systems, namely Munsell, NCS and DIN 6164, were studied by Döring (1990). The uncertainties during visual interpolation had been found to be independent of colorimetric precision of the colour samples in the atlas. Döring also observed that the mean colour difference (ΔECIELAB) between colour notation by colour measurement and by visual interpolation were 2 ± 2.7 and 4 ± 3.9 respectively for the DIN and NCS systems which reduced to 1.2 ± 2.8 and 1.7 ± 2.6 for low to medium chroma samples.

2.7 Computer-based systems

Though colour atlases are convenient, portable, easy to understand and relatively cheap, there are several reasons for the increasing popularity of computer-based colour order systems (Rhodes, 2002):

• The cost of colour atlases, especially those containing tight-tolerance colour samples, are ever-increasing and a set of multiple atlases are not affordable for many users. On the other hand, computers and software are becoming a cost-effective alternative.

• The physical atlases are inherently portable. On the other hand, LCD and other low-power displays have made portable computers a viable alternative.


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The latter systems can be used anywhere, depending on the availability of the software.

• The availability of coloured chips in an atlas is limited by the practical constraints and costs and generally restricted to around 2 000 – far below the number of perceptible colours (a few million). Computer equivalents have no such limitation and any number of colours can be interpolated. An electronic or digital atlas can represent colours outside the colour gamut, but the accuracy of display may be questionable.

• Computer software can instantly convert colours from one notation system to another, while for physical samples even experienced observers need substantial time for specifying colours in a colour notation system.

• Physical coloured samples have a limited lifespan – they fade, scratch and soil easily. These limitations are not applicable to computer monitors and they can accurately represent colours if properly characterised and periodically calibrated.

• To avoid metamerism, physical colour standards are recommended to be viewed under a specifi c illuminant, which may not be portable. Monitor colours are self-luminous and such a problem does not arise.

• The greatest advantage of computer-based systems is that colours can be communicated globally through electronic networks, even in the absence of physical samples.

The monitors and printers follow device-dependent specifi cation systems. In cathode ray tube (CRT) displays, colour television, and most computer video displays, colour stimuli are generated with three different types of phosphors after activation by electron beams. The three additive primary colours generated by such activation are orange-red, leaf-green and violet. A large number of colours can be created by their mixture.

The two most common additive systems used in connection with computer displays are RGB (based on mixing three additive primary colours red, green and blue, produced by the phosphors of the display unit in cubic space) and HSB (hue, saturation and brightness in cylindrical form).

RGB is a device-dependent colour space. Not all monitors or other RGB devices can produce the same range of colours. The term gamut is used to describe the universe of colours a given device or other range of colours can produce or describe. A better monitor, for instance, probably has a wider gamut than a cheaper one does and older monitors will have a harder time than newer ones, since their phosphors are starting to wear out. With today’s technology, a CRT monitor has a wider gamut than an LCD one does. If we feed 255, 0, 0 (pure red in RGB) to one monitor then we might get a more saturated red than another monitor is capable of. Each is doing its best to put out pure red, and neither can do a perfect job of it, but one may do better than the other. A colour space is a particular instance of a colour model that describes the specifi c colours one may get for each combination


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of numbers (red, green and blue in this case). Thus, a colour space differs from a colour model in that it maps specifi c values to specifi c colours while a colour model only determines that the values will be the red, green and blue components (or whatever) without saying how much of any given component is needed to get what specifi c result. Every RGB device (scanner, monitor) will have its own unique colour space even though they all share the same RGB colour model.

When the exact chromaticities of the red, green and blue primaries are defi ned, the colour model then becomes an absolute colour space, such as sRGB (s = standard) or Adobe RGB (which has a signifi cantly larger gamut). A set of primary colours, such as the sRGB primaries, defi ne a colour triangle inside the chromaticity diagram. Only colours within this triangle (colour gamut) can be reproduced by mixing the primary colours. The chromaticity of illuminant (D65, D50 or C) is the white point. The chromaticity coordinates of red, green, blue and white point are (0.64, 0.33), (0.30, 0.60), (0.15, 0.06) and (0.31, 0.33) respectively. As of 2007, sRGB is by far the most commonly used RGB colour space, particularly in consumer grade digital cameras, high defi nition video cameras, computer monitors and high defi nition televisions, because it is considered adequate for most consumer applications. Having all devices use the same colour space is convenient in that an image does not need to be converted from one colour space to another (colour management) before being displayed. However, sRGB’s limited gamut leaves out many highly saturated colours that can be produced by printers or in fi lm, and thus is not ideal for some high quality applications. The wider gamut Adobe RGB is being built into more medium-grade digital cameras, and is favoured by many professional graphic artists for its larger gamut.

The mixed colour stimuli are represented in the RGB colour cube. The abbreviations (R, G, B) are used to represent, loosely, the three additive colour primaries used. The cube resembles the Benson cube (Kuehni, 2003) in which white and black are placed on two opposed corners of the tilted cube with yellow, pink and sea-green on the upper three intermediate corners and red, blue and green on the lower three. The centre of the cube is occupied by a medium grey. For a colour, the standard values of the three components in the RGB system range from 0 to 255. This gives us 256 different possible values for each primary colour which works well with the way computers store numbers. It is possible to generate 16.7 million different possibilities (256 × 256). As the cube is rotated, the white and black fall on a vertical axis, a version of a polar coordinate system is imitated and termed as HSB space. In this space, hue is expressed in hue angle in degree. Saturation is expressed in percentage – 0% at achromatic point (grey) to 100% at full saturation. Brightness is expressed as a percentage from 0% at black to 100% at white. Achromatic colours have identical values for the three components, while for chromatic colours they have different values. Both the spaces are regular but not uniform. The gamut or maximum chromatic range, possible to create, is dictated by the phosphor used. These systems are based on increments of colour stimulus and have no connection to perceptual scales (Kuehni, 2005).


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On a printer, a complete absence of any ink would leave the colour of the paper to be printed (usually white) unchanged. The RGB system does not work too well for printers since they have to combine various inks to get the desired colour and it is not possible to produce inks that are suffi ciently pure in colour. Generally, black ink is added as a fourth colour to deal with the situation. Printers therefore work differently from monitors to produce colour and we most often use printer inks with colours different from the primary colours used by monitors. The CMYK colour model uses cyan, magenta, yellow and black inks (K is used to avoid confusion with blue) combined to produce various colours. A white colour has zero values for all components, while the grey scale differs in percentage of K. A chromatic colour may have percentage values in all four categories. The gamut of CMYK is usually smaller than the gamut for RGB because of the limited chroma of printing primaries.

HSL and HSV are two related representations of points in an RGB colour space, which attempt to describe perceptual colour relationships more accurately than RGB, while remaining computationally simple. HSL stands for hue, saturation, lightness, while HSV stands for hue, saturation, value. The HSV model (Fig. 2.3(a)) forms a single hexacone colour space starting from black ‘K’ (S = 0, V = 0) with the grey scale run vertically and ends at white ‘W’ (S = 0, V = 1) with six corners with primary and secondary colours, namely red ‘R’ (H = 0°), yellow ‘Y’ (H = 60°), green ‘G’ (H = 120°), cyan ‘C’ (H = 180°), blue ‘B’ (H = 240°) and magenta ‘M’ (H = 300°). The HLS model (Fig. 2.3(b)) forms a double hexacone space in which the white point is stretched to form the upper hexacone at L = 1. In the former model, the white point lies in the centre of a hexagon, while in the latter it is the starting point of the upper hexacone. For three sets of RGB values, the corresponding HSL and HSV values are shown below:

RGB HSL HSV (1, 0, 0) (0°, 1, 0.5) (0°, 1, 1) (0.5, 1, 0.5) (120°, 1, 0.75) (120°, 0.5, 1) (0, 0, 0.5) (240°, 1, 0.25) (240°, 1, 0.5)

The fi rst device, on independent colour specifi cation system for display users, was commercialised by Tektronix (1990). In the TekHVC system based on CIELUV, the hue (H) is offset (hUV – θ) by angle (θ) so that 0° corresponds with illuminant-dependent ‘best red’ at u ' = 0.7127 and v ' = 0.4931. The chroma (C) is multiplied by a scaling factor, while V is identical with L*. It is a widely used, device-dependent, cross-platform colour notation system. However, like the CIELUV and CIELAB systems, physical embodiment is not available with the system.

Various software packages also implement individual colour notation systems. Adobe Photoshop (www.adobe.com) displays colours in terms of RGB, HSB, CMYK and CIELAB values. In addition, the Adobe Colour Picker allows choosing custom colours from the Pantone Matching system, the Trumatch Swatching system, the Focoltone colour system, the TOYO Color Finder 1050 system, the ANPA-Colour system, HKS colour system, and the DIC Colour Guide.

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G

S

L

Y

R

M HB

K

W

(b)

C

2.3 (a) HSV and (b) HLS colour spaces.

G

W S

VY

R

H

MB

K

(a)

C


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The Pantone matching system includes 1 114 solid colours (premixed ink) and 3 000 process colour (colour separable, CMYK ink) combinations. The Trumatch system, represented in a swatch book containing 2 000 process colours, is slightly more perceptually based than the Pantone system and allows computer users to select CMYK colour specifi cations according to the appearance of printed patches instead of the approximate colour represented on CRT displays. The user chooses the desired colour from the swatch book and uses CMYK values to colour the images, ignoring the colour displayed on the monitor.

A colour profi le is a fi le that the computer uses to understand what it needs to know about any given colour space. It does this by mapping colours in an internal colour space (usually CIE LAB or CIE XYZ) used by the colour management system (CMS) embedded in the operating system or other software. This internal colour space is known as the profi le connection space and serves only as a way to map colours in one space to those in another.

Standard ‘ICC-profi les’ are produced according to a norm of the International Colour Consortium (ICC), in order to reproduce colour fi les on diverse output devices with colour fi delity. The procedure can perfectly adjust colour fi les via ICC-profi le, e.g. for offset print, provided accurately documented high value profi les are available. The ICC process for pictures containing many colours delivers good overall results. However, our naked eye may fi nd better matching RGB/CMYK values for individual specifi c colour tones than the calculation does. An ICC profi le contains between 400 and 1 500 interpolation points in which the comparison colours are mathematically interpolated. This is by no means enough to fi lter out the best-fi tting fi eld from the approximately 20,000 CMYK colour fi elds within a sensible atlas. Imprecise results may occur when an ICC profi le is used to convert a specifi c RAL colour into RGB or CMYK.

2.7.1 Digital colour atlases

Most of the material based atlases are now available in digitised form. ‘Colourtalk’ software system (Rhodes et al., 1992) incorporates both an on-screen visualisation of existing colour notation systems and also the transparent inter-conversion between them.

The NCS Digital Atlas (www.ncscolour.com) is a colour atlas that visualises all the 1 950 NCS original colours specifi ed in CMYK and positioned in the NCS colour space. The NCS colours can easily be selected from the digital colour palettes by clicking on the colour sample in the colour library of the software.

The RAL system is also available for various CAD packages, through RAL Digital.

The Coloroid Colour Plan Designer (1994) software generates very simple and user-friendly harmonic colour sets in computer monitors, and it can be applied in architecture, computer graphics, visualisation, product design, web page planning, in the paint industry and other fi elds, where harmonic colour sets are required.


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The Designer supports monochromatic, dichromatic and trichromatic harmonies, based on 1, 2 and 3 basic hues, respectively. The software takes the level of ambient light in consideration, using a colour appearance model, CIECAM97. Coordinates of colours, selected interactively by mouse or by defi ning coordinates, will be transformed in several colour systems, like CIE XYZ, xyz, Lab, Luv, Hunter Lab, display RGB with the corrected g values, and linear rgb in [0,1] assuming the sRGB primaries, and also all of Coloroid related data, like A, T, V, φ, additive components of s, w and p, and all of the hue angles and ‘A’ hue coordinates with highest harmony. A number of colour harmony rules like ‘Equidistant colour scales are always harmonic’ have been suggested in the software (Neumann et al., 2005).

The Digital Colour Atlas 3.0 (www.dtpstudio.de) enables comparing colour tones from about 150 colour systems (e.g. Munsell, Pantone, RAL, etc.). Persuasive harmonies can be calculated quickly and every colour from every system can be imported into any software. 200 colour fans and CMYK-books were measured spectral photometrically and all the calculations are based on this huge CIELAB database (about 200,000). During colour comparison, the program searches for the colour which has the minimum colour distance (ΔECIELAB) to the input colour.

The advantages of the Digital Colour Atlas are:

• The colour samples can be compared with reasonably high precision very quickly (maybe in a fraction of a second) as compared to hard copy comparison, which may take several minutes, e.g. CMYK values needed for RAL 3000 can be found in fractions of a second.

• A further advantage is the independence of such comparison from ambient light. On the other hand, paper or textile colour atlases are to be used in standardised artifi cial light.

• Many colour atlases are diffi cult to obtain or are no longer available. The digital colour atlas can specify colours in terms of several atlases and colour order systems in digital format even in their absence.

• Colour harmonies can be created on a CIELAB basis assuring accuracy to colour perception.

• Very fast visual communication in the trade.

In the software an approximate evaluation takes place according to the following conditions:

ΔECIELAB Difference between two colours

< 0.5 not distinguishable 1 distinguishable by expert 2 distinguishable< 5 slightly different< 10 different, but still fairly similar 10 different


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The eye is much more sensitive to bright, unsaturated areas than it is to dark or heavily saturated colour areas. In other words: ΔECIELAB = 2 leads to an obviously deviating colour when dealing with a pastel colour, whereas the same difference would be barely noticeable with dark colour tones. The eye is also more sensitive in the orange and yellow colour areas.

2.8 Universal colour language (UCL)

Many of the problems of colour technology could be more readily solved if everyone used a universal colour language that is understandable by all, at least in a general way. Such a language should allow colours to be described with different degrees of accuracy (by names or numerical notations), relate directly to the best known colour order systems, and provide meaningful translations of exotic or promotional colour names (Billmeyer and Saltzman, 1981). The NBS/ISCC colour naming system existed for over 15 years and was sold by the National Bureau of Standards (Washington, USA) in the name ‘Color: Universal Language and Dictionary of Names’ (special publication 40), but surprisingly it has not yet been widely adopted. It provides six levels of accuracy to describe colour by names (levels 1–3) or numerical designations (levels 4–6). In the fi rst three levels of the UCL, everyday language is used to describe colours. Level four of the UCL is for the colour order systems, level fi ve allows for interpolation between the colour samples of a colour order system, and, at level six, colorimetry provides precise specifi cation.

Level 1Least precise, number of divisions of colour solids is only 13, represented by

generic hue names and neutrals, e.g. brown.Level 2The number of division increased to 29 by incorporating all hue names and

neutrals, e.g. yellowish brown.Level 3The number of colour samples increased to 267 as in the ISCC-NBS collection

using all hue names and neutrals with modifi ers, e.g. light yellowish brown (centroid # 76).

Level 4The collection of colour standards is increased to 943 (7 056 in higher version)

based on systematic sampling within colour solid on the basis of the Munsell colour order system, e.g. 10 YR 6/4.

Level 5The colour discrimination can be increased by up to one million colours by visual

interpolation of Munsell notations, e.g. 9½ YR 6.4/4¼.Level 6The most precise, number of divisions of colour solid may be as large as fi ve

million on the basis of CIE (x, y, Y) or instrumentally interpolated Munsell notations, e.g. colour having specifi cation as x = 0.38, y = 0.37 and Y = 34.7.


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The ISCC-NBS method of designating colours (Kelly and Judd, 1955) is based on the most comprehensive investigation done on relating colour names to areas or volumes of colour space. Thousands of visual estimates were made to relate colour names to Munsell notations and the range limits of each colour word was charted in Munsell space. The reference colours of the standardised language are called ‘centroid colours’. Because of non-linearity in our visual apparatus and irregularities in our natural-language system of colour names, not every hue has the full complement of modifi ers. There are in fact only 267 centroid colours. That is a good practical number, small enough to be easily learned but large enough to make the distinctions needed for many applications. Since the eye and brain can only distinguish about 300 colours by memory, this system is of about the right size for distinguishing basic colours.

A series of centroid chips are available which represent each of the colours. The chips are identifi ed by descriptive colour names together with serial numbers. For example, school-bus yellow would carry the designation ‘Moderate Orange Yellow’. The system is useful for colour identifi cation in design, architecture and art and about 7 500 colour names are listed and cross-referenced. Since its publication in 1955, thousands more names have been devised and one can presume that there is no end in sight. Simon (1995) regarded the colour ‘blue’ of level 1 as Munsell purple-blue and proposed a division of the area adding cyan/turquoise.

Since the CIELAB method is widely used for the numerical representation of colours, the Munsell system was mapped into this space by Pointer (1981), Hunt (1982), Nayatani et al. (1988) and others. However, it is not feasible to use a simple distribution of the metric hue angles to represent various Munsell hues around the circumference of a*b* space. Despite the limitation that the Munsell hues are not equally spaced in the CIELAB diagram, several computer programs have opted for simplicity and segmented the a*b* diagram into equal-angular divisions. This treatment leads to errors in two ways:

1 A plot of the true Munsell centroid versus the equal angle centroid shows varying hue angle difference for various chromatic colours.

2 Visual names do not match with the Munsell hue name, e.g. a colour with Munsell designation of 3.2 B 6.1/9.2 actually appears green and not blue.

To correct the problems with equi-angle segmentation, unequal hue angle was obtained by mapping the ISCC-NBS named colour areas onto a*b* space (Simon, 1995). The hue angle boundaries, thus obtained, are shown in Table 2.1 against names under the universal colour language along with centroid colours.

Colour zones, a fl exible system for describing colours which links everyday language to colour order systems based on that of the Natural colour system (NCS), similar to the UCL system, is also proposed (Green-Armytage, 2002).


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2.9 Future trends

There are relatively limited ranges of fi bre types, yarn constructions, spinning methods and fabric forming techniques that form the basis for designing textile materials. By contrast, the range of colours that can be applied to these textile substrates is extremely wide. Though at least one million colours can be perceived, far less colours are produced on textile substrates. From a vertical production-led organisation in the 1980s, the textile industry has changed into a retail-specifi ed manufacturing industry. The retailers now need to have a major input into the colour selection process as selection of an appropriate colour range is a means whereby both manufacturers and retailers can differentiate their products from those of their competitors. Selection of a proper colour range is equally important for other coloration industries too, e.g. paint (Park, 2007).

Shade ranges, eventually known as colour palette, were traditionally developed by collection of samples from many sources on various substrates. These physical samples were then matched on appropriate textile substrates. This method resulted in several problems such as physical difference between target colour and dyed textile substrate, inaccuracy of visual assessment, etc. To overcome some of the problems, the coloration industries produced shade cards consisting of standard colours from production ranges. However, study showed that in a typical range of standard colours, 80% of sales were generated by 20% of the colours (Park, 2008). A further attempt to assist the colour selection process was the development of colour atlases by a number of textile-producing organisations in a more organised format.

A major development in the colour selection process is the availability of colour specifi cation products based on colours being uniformly distributed throughout

Table 2.1 Universal colour names (level 1), centroid colours and hue angle boundaries

Sr. No. Universal colour names Centroid colours Hue angle boundaries

1 Red 2.5 R 5/10 351–38 2 Pink 2.5 R 7/8 320–38 3 Orange 2.5 YR 7/10 38–77 4 Brown 2.5 YR 3/4 38–77 5 Yellow 7.5 Y 8/12 77–100 6 Yellow-green 5 GY 7/10 100–128 7 Green 5 G 6/8 128–200 8 Olive 5 G 4/4 128–200 9 Cyan/Turquoise 7.5 BG 5/6 200–22510 Blue 2.5 PB 4/10 225–28511 Purple/Violet 5 P 5/10 285–35112 White13 Grey N/4 0–36014 Black


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the colour solid. They assist rapid communication of colour and larger swatches can be used as master standards. They are a valuable tool for quick generation of palettes, with reduction in laboratory matching and savings in time and cost. Many of these products are often less than perfect and atlases based on colour order systems are preferred.

Physical standards are quickly soiled and the concept of using non-physical standards (NPS) based on refl ectance measurement is slowly getting momentum. High repeatability and reproducibility of modern spectrophotometers with better inter-instrument agreement (variability as low as 0.3 ΔECMC unit) along with improved accuracy in computer colour matching and laboratory dyeing, have allowed NPS in the form of refl ectance data. A major development in digital colour communication is the use of NPS for communication, visualisation, evaluation and manipulation of colours on screen. The research initiated at UMIST resulted in the Shade Master system (Haydon and Oulton, 1994), subsequently commercialised as Colorite (at present Envision) by Datacolor and a few others. The various probable steps of digital colour communication in future may be as follows (Park, 2007):

1 Precise colour generation on a calibrated screen by retailer (probably using digital atlas).

2 Request to the matching laboratory for virtual matching and prediction of approximate dye formulation.

3 Return of virtual matching to the retailer quickly by email.4 Assessment of virtual match and modifi cation, if necessary, by the retailer.5 Laboratory dyeing of approved virtual match for keeping as ‘master standard’.6 A suffi cient quantity of ‘engineered standards’ is produced by matching with

master standard.7 Engineered standards may be produced on various substrates and are

distributed by retailer to suppliers.8 Production and laboratory dyeing conducted with engineered standard.

Colour order systems are based on various principles and in most cases they are not compatible with each other. Each system serves some specifi c fi eld or purpose. Several nations have adopted specifi c colour order systems as national standards: Germany (DIN); Sweden and other Scandinavian countries (NCS), Japan, Italy and many others (Munsell). There is no internationally accepted colour order system which is very necessary for quick global communication. Tonnquist (1986) suggested that the Munsell and NCS colour order systems can mutually benefi t from each other.

Judd and Wyszecki (1963) concluded that it is not possible to map equi-luminous colours uniformly in a plane surface. The perfect model for colour perception can be created only in non-Euclidean space and no colour space has achieved such perfection. As all samples cannot be collected, interpolation or extrapolation is necessary, but this may raise a point of controversy from observer to observer;


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hence equal visual spacing is very much essential. This spacing depends on illumination too; illuminating conditions are therefore to be specifi ed. Digital colour communication based on existing colour order systems is extremely helpful for colour communication between manufacturers and retailers.

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communication’, Displays, 13 (2), 89–96, Butterworth, UK.Rhodes P A (1995), Computer mediated colour fi delity and communication, PhD Thesis,

Loughborough University of Technology.Rhodes P A (2002), ‘Colour notation systems’, in Colour Engineering, P Green and

L MacDonald (eds), pp 307–331, Chichester, England, John Wiley.Richter M and Witt K (1986), ‘The story of the DIN color system’, Col. Res. Appl., 11,

138–145.Robertson A R (1984), ‘Colour order systems: An introductory review’, Col. Res. Appl., 9,

234–240.Roy Choudhury A K and Chatterjee S M (1992), ‘Quantifying metamerism’, Rev. Prog.

Col., 22, 42.Roy Choudhury A K (1996), ‘Colour order systems’, Rev. Prog. Col., 26, 54–62.Roy Choudhury A K (2008), ‘Colorimetric study of SCOTDIC Colour Specifi er’, Color

Technology, 124, 273–284.Scotdic Colour Book, Higashi-ku, Osaka 541 Japan, Kensaikan Ltd. www.scotdic.com,

www.scotdic.co.in.Sikkens GmbH (1978), ‘Acoat Color Codifi cation System: Handbuch für Farbgestaltung’.Simon F T and Frost J A (1987), ‘A new method for conversion of CIE colorimetric data to

Munsell notations’, Col. Res. Appl., 12, 256–260.Simon F T (1995), Colour names for CIELAB space, ISCC-AATCC joint meeting,

Greenboro (USA), April.Smith N S, Whitfi eld T W A and Wittshire T J (1990a), ‘The accuracy of the NCS, DIN and

OSA-UCS Colour Atlases’, Col. Res. Appl., 15, 111–116.Smith N S, Whitfi eld T W A and Wittshire T J (1990b), ‘Research note on the accuracy of

the NCS, DIN and OSA-UCS Colour Atlases’, Col. Res. Appl., 15, 297–299.Smith N S, Whitfi eld T W A and Wittshire T J (1990c), ‘Comparison of the Munsell, DIN

and Coloroid colour order systems using the OSA-UCS model’, Col. Res. Appl., 15, 327–337.

Smith N S, Whitfi eld T W A and Wittshire T J (1990d), ‘A color notation conversion program’, Col. Res. Appl., 15, 338–343.

Smith N S, Whitfi eld T W A and Wittshire T J (1991), ‘The accuracy of the NCS Atlas samples’, Col. Res. Appl., 160, 108–113.

Smith N S and Billmeyer F W (1994), ‘Comparison of the Colorcurve and SCA-2541 colour order systems using the OSA-UCS model’, Col. Res. Appl., 19, 363–374.

Stanziola R (1992), ‘The Colorcurve system’, Col. Res. Appl., 17, 263–272.Taylor J M (1984), Multidimensional scaling of selected samples from the Optical Society

of America Uniform Colour Scales, PhD Thesis, Rensselaer Polytechnic Institute, Troy, New York.

Taylor J M and Billmeyer F W Jr. (1988), ‘Multidimensional scaling of selected samples from the OSA-UCS’, Col. Res. Appl., 13, 85–98.


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Tektronix (1990), Tekcolor color management system: system implementer’s manual, Tektronix, Inc.

Tominaga S (1993), ‘Color notation conversion by neural networks’, Col. Res. Appl., 18, 253–259.

Tonquist G (1986), ‘Philosophy of perceptive color order systems’, Col. Res. Appl., 11, 51–55.

Whitfi eld T W A, Powell A S, O’Conner M and Wittshire T J (1988), ‘The conceptual NCS: an empirical investigation’, Col. Res. Appl., 13, 119.

Wright W D (1984), ‘The basic concepts and attributes of colour order systems’, Col. Res. Appl., 9, 229–233.

Wyble D R and Fairchild M D (2000), ‘Prediction of Munsell appearance scales using various color-appearance models’, Col. Res. Appl., 25 (2), 132–144.

Wyszecki G and Stiles W S (1982), Colour Science: Concepts and Methods Quantitative Data and Formulae, New York, Wiley.

Wyszecki G (1986), ‘Colour Appearance’, Chapter 9 in Handbook of Perception and Human Performance, New York, Wiley.

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White

0/

1/

2/

3/

4/

5/

6/

7/

8/

9/

10/

5R

5YR

5Y

5GY

5Ghue

5BG

5B

5PB

5P

5RP

Value

Chroma

Black

Plate V Munsell colour order system.

Step 1. Classification of achromatic colours according to lightness

Step 3. Further classification of chromatic colours into intermediate hues

Step 4. Classification of chromatic colours with varying lightness (e.g. same red hue but in the order increasing lightness, very dark red to pink)

Step 5. Classification of chromatic colours with varying saturation of chroma (e.g. light bluish red with increasing chroma)

Step 2. Classification of chromatic colours according to common names (principal hues)

Black, greys, white (in the order of increasing lightness)

Red Green Blue PurpleYellow

Yellow-Green Green Green-Blue

Plate IV Desert island experiment (classifi cation of pebbles according to colour).

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1005

02

10

05

20

30

40

50

60

70

80

90

20

30

40

5060

W

S

C

70

80

90

B

Y

R90B

R80B

R70B

R60B

R50B

R40B

R30B

R20B

R10B

RGY90R

Y80RY70R

Y60RY50

RY40RY

30R

Y20

R

Y10

R

B10

G

B20

G

B30

GB40G

B50G

B60GB70G

B80G

B90G

G10Y

G20Y

G30Y

G40Y

G50Y

G60Y

G70Y

G80Y

G90Y

Plate VI (a) NCS con stant hue trian gle, (b) NCS hue circle

(a)

(b)

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2

5

8

11

14

17

20

23

Plate VII (a) A vertical cross-section through Ostwald double-cone colour solid, (b) Ostwald hue circle.

Equi-whiteness series

Equi-blackness seriesIncreasing blackness

Increasing whitenessWhite

Full colour(13)

Full colour(1)

a

c

e

g

ili

nipi

plpn

pp

pp

nn

nn

ll

ll

ii

ii

gg

gg

ee

ee

cc

cc

aa

aa

pgl

n

p

caea

gagc

geia

lana

pa

pepc

Black

(a)

(b)

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70

3Expressing colours numerically

V. C. GUPTE, Advanced Graphic Systems, India

Abstract: Colour is an integral part of our daily life and impacts on many decisions that we make. There have been several attempts by colour scientists and artists to specify colour, but no specifi c language evolved to describe it. The Commission Internationale de l’Eclairage (CIE) standardized light sources/illuminants and defi ned the standard observer response to colour which resulted in specifying colour numerically. It specifi ed colour in terms of unreal primaries, XYZ, referred to as tristimulus values, which are further converted to calculate chromaticity coordinates – x y z, providing additional information about colour. Describing colour in terms of tristimulus values was itself a signifi cant achievement, but had its limitations as well, mainly it was a non-uniform system. To overcome its limitations, the CIE transformed the system into a more uniform one and specifi ed colour in terms of L*, a*, b*, Cab* and hab, which formed the basis for most colorimetric calculations and helped colorists in handling colour applications.

Key words: the CIE, standard illuminants, standard observer, tristimulus values, chromaticity diagram, CIELAB 1976 Colour Space, CIE chroma ‘C’ and hue ‘H’.

3.1 Introduction

It is very diffi cult to imagine the world around us without colour as it has become an integral part of our daily life, be it textiles, paints, plastics or printing. All our daily necessities are packed in attractive packaging that tempt us to buy, even if we do not need the product. We cannot imagine our television without colour. The colour of the garments we wear projects our personality and the paint we select for our houses creates a pleasant atmosphere. We select the colour of our furnishings to match the paint in the room and it has even been proved that the colour depicts the nature and inner personality of the car owner. Colour has become a focal point of what we do every day.

Despite being so important and so close to everyone’s day-to-day life, it is not possible to express colour in a unique or specifi c language. We remember colour while we look at it, but the moment we look away it gets erased from our memory. So colour scientists attempted to specify colour in an explicit universal language so that it could be understood by everyone involved with colour and colour reproduction. This led to effects to express colour numerically in a unique and unambiguous way.

3.2 Colour specifi cations

Many scientists put forward different systems to specify colour and these are described in Chapter 2. The Munsell1 colour order system, although over 100 years

Expressing colours numerically 71

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old, is still used in many industries today, for example in specifying the colour of instruments used in medicine and the colour of electric motors in engineering. Munsell was an artist and one of the fi rst to state that colour cannot be specifi ed in two dimensions, but requires three dimensions. He specifi ed colour in hue, value and chroma. He defi ned them as:

Hue (H) is by which we differentiate red from green, yellow from blue, etc. Or by which we refer to colour, like red, yellow, green, blue, etc.

Value (V) is the lightness/darkness or in short the greyness of a colour.Chroma (C) is the saturation of colour.

The Munsell system comprises 10 main hues – red (R), yellow (Y), green (G), blue (B), purple (P), yellow-red (YR), green-yellow (GY), blue-green (BG), purple-blue (PB) and red-purple (RP). Each main hue is preceded by the number 5 and each is further divided into 10, giving a total of 100 hues. The intermediate hues are graded on the main hues, such as 7.5RP, 10P, etc. When arranged in a circle, these form the Hue Circle.

The Munsell value scale ranges from 1 for black to 10 for white. Black, white and the greys between them are neutral colours. These are all available as the Munsell Neutral Scale2 in a fandeck or in individual sheets in steps of 0.25 and every colour has value, whether it is a chromatic or neutral colour. The Munsell Neutral colour is specifi ed as N V/. The grey colour used in most light booths is N 6/ or N 7/, although some specifi ers recommend N 5/, a darker neutral grey. Standards like ANSI specify N 8/, while ASTM D1535 specifi es N 7/ for light booths. It has not been possible so far to practically produce Munsell black N 1/ or white N 10/ using available white and black colorants.

Munsell chroma is saturation. Imagine gradually adding a pure red hue, 5R, to a grey having a value of 6 until the original red is achieved. As the hue reaches a point when further addition does not change the shade, it is at saturation. Munsell referred to it as chroma and he prepared chips in such a way that there was uniform difference between two adjacent chips. The chroma is denoted by an even number preceded by ‘/’, with normal colours limited up to 20 and fl uorescent colours as high as 30.

Munsell prepared chips for all hues, values and chroma and arranged them huewise. This is the famous Munsell Book of Colors.3 The Munsell Book of Colors is available in glossy (1 488 chips) and matt (1 277 chips) versions. Each chip is denoted by Munsell notation – HV/C, for example 5R6/8. After the Commission Internationale de l’Eclairage (CIE) system was announced, the Munsell system was redefi ned and the revised spacing was defi ned in terms of the CIE Standard Observer and CIE source (2° observer and light source C) and the original Munsell notations of each sample were correlated with renotations from revised spacing. The Munsell system now has intermediate steps, such as 5.4R6.2/12.4. These are now referred to as Munsell renotations.


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3.3 The Commission Internationale de

l’Eclairage (CIE) system

The CIE is an international commission originally based in France, but now based in Vienna. In 1913 it took over the functions of the International Commission on Photometry (Commission Internationale de Photometrie) and established standards and measurement procedures for use in colour and appearance. It standardized light sources/illuminants, observer response to colour and also geometry for refl ectance spectrophotometers. Using these inputs, the CIE specifi ed colour in terms of additive primaries – R (red), G (green) and B (blue) – which expressed colour in the same way the human eye would see it. The red, green and blue primaries so selected have specifi c wavelengths – red at 700 nm, green at 546.1 and blue at 435.8.

3.4 The CIE standard light sources/illuminants

We know that the appearance of colour depends on the light incident on it. There are many lights around us, which are very different and hence make the colour assessment diffi cult. So it is necessary to have standard light sources characterized in numerical terms.

The CIE standardized light sources based on their colour temperature, spectral power distribution and chromaticity coordinates. (Colour temperature of a light source is the temperature of a black body at which the light of the black body matches the light of the light source and is expressed in Kelvin – absolute temperature, for example + 273°C.) The CIE initially standardized three light sources which represented different phases of daylight.

Light source A – 2856 KLight source B – 4874 K noon daylightLight source C – 6774 K north sky daylight

Light source A can be simulated in a laboratory using a tungsten fi lament operating at 2856K. It is mainly used as a secondary light source for detecting metamerism (how an object will appear different when viewed under two light sources).

Light source B represents noon daylight, but is not used so commonly in colour assessment.

Light source C is the north sky daylight from overcast sky. The CIE recommended that the colour assessment be carried out under light source C and released the details of laboratory procedures to produce light sources B and C. However, it is not easy to simulate the daylight in a laboratory, particularly in the UV region, unless one adds the same amount of UV light to the laboratory light.

It is further observed that light source C is not available throughout the day. It changes from place to place, season to season and it is not even observed in many countries during most periods of the year. The colour temperature of daylight varies signifi cantly from 2800K to 25000K from sunrise to sunset – when it is


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clear blue sky, it reaches the maximum colour temperature. Every hour the colour temperature of daylight changes by 1000K.

The CIE later introduced D illuminants representing various phases of daylight with different colour temperatures:

D55 – 5500KD65 – 6500KD75 – 7500K

These are referred to as illuminants. The difference between a light source and an illuminant is that while the former exists physically, the latter may or may not exist physically. The CIE defi ned D illuminants based on work by Judd4 and co-workers. Illuminant D55 is not used in any colour assessment, but its modifi ed version, D50 is used in graphic art industries and photography for colour assessment. The reason for selecting 6500K and 7500K was that the average colour temperature of daylight, excluding sunrise and sunset, lies between 6000–7000K. The D65 illuminant was also arrived at by taking into account measurements of total daylight in several countries. D65 and D75 are referred to as average daylight. However, there are no physical light sources which simulate the CIE daylight D-illuminants. The CIE recommended use of D illuminants for calculation of tristimulus values and colorimetric data in place of light source C in most applications in colour industries all over the world. Illuminant D75 is used by a few retailers in North America for colour assessment where blue phase of daylight is preferred. The main problem is that D illuminants cannot be closely produced in the laboratory.

The CIE also took note of fl uorescent lamps, which were then widely used in stores and standardized 12 such lamps. Of the 12 lamps, three are extensively used in the industry.

F-2 – Cool white fl uorescent (CWF) light with correlated colour temperature 4150K. It is known as a wide band fl uorescent lamp and is defi cient in the red region, hence suppresses reds, but enhances all other colours.

F-7 – Daylight fl uorescent lamp with correlated colour temperature of 6500K. The household daylight lamps commonly use single phosphor, while some use 3-phosphors and do not simulate daylight. This results in enhancing certain colours, but depressing other colours. The 7-phosphor daylight fl uorescent lamps nearly simulate daylight.

F-11 – Represents a TL84 fl uorescent lamp with correlated temperature of 4150K and is known as a narrow band fl uorescent lamp, as these produce narrow bands in blue, green and orange-red regions. Due to nearly equal energy in the visible region, most objects appear uniformly under these lamps.

The CIE published relative spectral power distribution tables of all the CIE standard illuminants in the visible region spectrum 360–780 nm at intervals of 5 nm.


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3.5 The CIE Standard Observer and

unreal primaries

The standard observer represents how a human eye responds to spectrum colours wavelength by wavelength. The human eye perceives a colour due to a combination of red, green and blue sensitive cones which is explained by the different theories of colour vision, notably the trichromatic theory.

Wright5 and Guild6 separately carried out experiments using red, green and blue lamps and a test lamp to produce spectrum colours wavelength by wavelength. They used one half of the fi eld of a spectrum colour of a single wavelength and it was matched by the combination of red (R), green (G) and blue (B) primary lights in the other half. The red, green and blue lamps had measuring devices; it was therefore possible to match a single wavelength spectrum colour by knowing the individual amounts of red, green and blue lights. The experiments were carried out at intervals of 5 nm. The observer was kept at such a distance that he could see a two degree fi eld of vision using the arrangement as shown in Figure 3.1. Wright used 10 observers while Guild had seven. The CIE combined the results of Wright and Guild to describe the CIE Standard Observer.

The results were expressed as the tristimulus values for an equal-energy spectrum using R, G and B primaries and the amounts were expressed as r –, g–, b

–

equired to match each wavelength in the visible region.

CIE Standard Observer experiment

Testlamp

Yellow

Mas

king

scr

een

Black partition

Blue

Green

Red

Observer

Whi

te s

cree

n

3.1 The CIE Standard Observer.


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3.5.1 The CIE unreal primaries

It was observed by the CIE that the real primaries – red, green and blue – did not match certain spectrum colours. For example, a pure cyan colour could not be just produced by blue and green primaries alone. If we add red to blue and green, we may get a less saturated cyan. To match it correctly, we may have to add a red primary to a pure cyan colour. Mathematically,

Cyan (C) + Red (R) = Blue (B) + Green (G) orCyan (C) = – Red (R) + Blue (B) + Green (G)

In other words, to match certain spectrum colours, there may be negative values for one or more real primaries (we have to remove one or more primaries for matching these colours). The negative values are not desirable. This meant that although red, green and blue primaries produced most available colours, all colours could not be produced using positive mixtures of real primaries. To avoid negative values, the CIE recommended use of unreal primaries, X (red), Y (green) and Z (blue), as illustrated in Fig. 3.2.

3.2 The real and unreal primaries.

[Z]

[B]

[G][R]

[X]

[Y]

M


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It can be explained using a two-dimensional fi gure with caution, as any colour requires three dimensions. If the primaries are red [R], green [G] and blue [B] (representing an equilateral triangle in Fig. 3.2), we can match any colour using proportions of R, G and B represented by r, g and b respectively. For example,

r = R + G + B

R

g = R + G + B

G [3.1]

b = R + G + B

B

where r + g + b =1 [3.2]

To match any point within the triangle, we would require, P = r.R + g.G + b.B where all values are positive. However, if we have to match a colour, M, we would require M = – r.R + g.G + b.B with appropriate proportions so that it meets equation 3.2. The colour M can be matched using positive values of blue and green, but would require a negative quantity of red. This is true for all colours lying outside RGB triangle. This would also be true for any three R, G and B primaries. To avoid these negative values the CIE recommended unreal primaries – X, Y and Z representing R, G and B respectively.

The dotted triangle in Fig. 3.2 represents the gamut of unreal primaries wherein any colour will always be matched with positive values of X, Y and Z. The position of points X, Y and Z can be easily located using r, g, and b. The equilateral triangle RGB, which was used in the early days as a chromaticity diagram, was referred to as the Maxwell Colour Triangle, while Rigg7 has elaborately explained the concept of unreal primaries.

The CIE adopted unreal primaries, X (red), Y (green) and Z (blue). The revised values x–, y–, z– form the CIE Two Degree Standard Observer and are always positive. It is also referred to as the 1931 Standard Observer. The standard observer is also referred to as colour matching functions. The Two Degree Observer in real and unreal primaries is illustrated graphically in Figure 3.3.

The CIE realized that the two degree fi eld of vision was too small for colour assessment. Stiles and Burch8 and Speranskaya9 used a much wider 10° fi eld and repeated the experiments of Wright and Guild. The CIE averaged these results and released the new data, which is the CIE 1964 Supplementary Standard Observer, commonly referred to as the CIE Ten Degree Observer. It is referred to as x–10, y

–10,

z–10 just to differentiate from the 2° observer. It is noted that ‘y–10’ differs from ‘y–’ and hence it does not represent lightness of colour in the same way as the latter. It is also observed that x–,10 and y–10 have values throughout the region, while z–10 is practically zero beyond 550 nm.

The CIE Ten Degree Standard Observer is used in most calculations. The Two Degree Observer is used in select applications, like calculating Munsell notations or in some whiteness index calculations.


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3.6 Computation of tristimulus values

Using the spectral power distribution and the standard observer, a colour can be specifi ed into the CIE primaries X, Y and Z when the refl ectance of the object is known. The refl ectance of an object is measured using a refl ectance spectrophotometer. The CIE released the mathematical relations to compute X, Y and Z, commonly referred to as the tristimulus values. The initial equations

0.3

b

0.2

0.1

Tris

timul

us v

alue

s

Wavelength, nm

0

–0.1380 480 580 680 780

2

1.5

1

Tris

timul

us v

alue

s

Wavelength, nm

0.5

0380 480 580 680 780

g

r

z

y x

3.3 Graphical representation of (a) real and (b) unreal primaries.

(a)

(b)


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involved integrations and the CIE subsequently noted that integration is approximated by summation in all practical calculations. With the product of refl ectance, spectral power distribution and standard observer at a wavelength and further summation over the visible region 360–780 nm, and one can compute X, Y and Z values. The CIE released the revised procedures mathematically,

X = k Σ Rλ.Eλ.–xλ.Δλ

Y = k Σ Rλ.Eλ.–yλ.Δλ

Z = k Σ Rλ.Eλ.–z λ.Δλ [3.3]

with

k = 100/Σ Eλ.–yλ.Δλ

where X, Y and Z are the tristimulus values

Rλ is refl ectance at wavelength λEλ is spectral power distribution of the CIE illuminant at λx–λ, y–λ, z–λ is the standard observer at λ.Δλ is wavelength interval.

It is common to carry out summation for calculating X, Y, Z using normalized weight factors Wx, Wy, Wz as follows:

Wxλ = k.Eλ.x–λ.Δλ

Wyλ = k.Eλ.y–λ.Δλ [3.4]

Wzλ = k.Eλ.z–λ.Δλ

for λ = 360 to 780 and

k = 100/Σ Eλ.y–λ.Δλ

For given CIE illuminant, standard observer and wavelength interval, weight factor can be calculated once only and these weight factors are available in tabular form in standards like ASTM E308:08. The equations then become,

X = Σ Wxλ.Rλ.Δλ

Y = Σ Wyλ.Rλ.Δλ [3.5]

Z = Σ Wzλ.Rλ.Δλ

X, Y and Z are arbitrary values and have no units, while tristimulus values are numbers which represent how the human eye-brain responds to or sees a colour. Y defi nes the luminance factor and is normalized so that it lies between 0–100.

780

360780

360780

360

780

360

780

360

780

360780

360780

360


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The Y value for all illuminants is normalized to 100, while X and Z vary, Z more signifi cantly than X.

Theoretically, when a standard and a sample have the same X, Y, Z values under given illuminant and observer conditions, they are a matched pair. This is true in most cases for objects which do not have any physical attributes like gloss, texture, etc. Such an observation would also be true visually. However, when standard and sample differ in their physical attribute, their X, Y, Z might match mathematically, but their visual assessment would not. Both the standard and sample would appear different visually.

The CIE has published tables for spectral power distribution and standard observer at 5 nm intervals. However, not many refl ectance spectrophotometers measure refl ectance at 360–780 nm, but at quite different ranges like 400–700 nm, 360–750 nm or 380–700 nm. There are no standard tables for such wavelength ranges. There are also a few spectrophotometers which report measurements at 5 nm intervals, but the bandpass of most spectrophotometers is 10 nm or higher. Most present generation commercial spectrophotometers report the measurement at 10 nm intervals. The ASTM E308 standard, (recent version ASTM E308-08) which is commonly used in most colour software, provides tables at 10 and 20 nm intervals. The colour software used with these instruments add the standard observer and spectral power distribution data to the fi rst and the last wavelength data, for example 360–390 nm data is added to 400 nm data and 710–780 nm data to 700 nm data. Theoretically, this may appear correct, but Stearns10 has shown errors in calculating tristimulus values by such random addition. He has suggested a suitable method to overcome the problem. The X, Y, Z calculated using Stearns method provides the same results as would have obtained by using the CIE tables at 5 nm intervals in 360–780 nm. Stearn’s method can also be used to calculate correctly X, Y, Z values from any wavelength range or interval. If proper care is not taken it can cause major difference in X, Y, Z values and in subsequent colorimetric applications, like colour difference, etc. The proper method to check whether one has used the correct table is to calculate the Y value by inputting refl ectance as 100% at all wavelengths. One must obtain the Y value as 100.

3.7 Refl ectance measurement

X, Y, Z calculations require refl ectance of the object and refl ectance measurement is carried out using a refl ectance spectrophotometer. Refl ectance measurement is important as it is the only measurable parameter in the colour science and all other data are calculated from it. The basic components of a refl ectance spectrophotometer comprise a light source, illuminating and viewing geometry, monochromator and a detector. The illuminating and viewing geometry of a refl ectance spectrophotometer was standardized by the CIE. These geometries are 45°/0°, 0°/45°, d/8° and 8°/d, the fi rst parameter indicating illuminating angle,


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while the latter indicates the viewing angle (or an angle at which refl ected light is directed to the detector via a monochromator). A monochromator disperses the refl ected light which further passes through an exit slit to the detector. The modern spectrophotometers provide refl ectance data at 10 nm intervals, but the wavelength range varies for different models. The spectrophotometer and its functioning are described in Chapter 6. It must be pointed out here that the refl ectance measurement must be correct as it is the basic data for all colorimetric calculations.

3.8 Chromaticity coordinates and

chromaticity diagram

Tristimulus values X, Y, Z are further converted into chromaticity coordinates using simple mathematical relations,

x = X + Y + Z

X

y = X + Y + Z

Y [3.6]

z = X + Y + Z

Z

where x, y, z are the chromaticity coordinates andX, Y and Z are tristimulus values.It also means x + y + z = 1, or in other words when any two coordinates are known

the third can be calculated; however, x and y are commonly used. The Y tristimulus value provides the third dimension. One can also specify colour in x, y and Y.

When y is plotted against x for spectrum colours and all points are joined, it forms a horseshoe shape diagram – the chromaticity diagram. The two extremes – violet (360 nm) and red (780 nm) are connected with an imaginary line. All available colours lie within the diagram. The CIE illuminants lie at the centre forming a curve, light source A lying towards red and D75 towards blue, as illustrated in Figure 3.4. One can imagine chromaticity diagrams stacked along the Y axis. It is generally observed that one compares a colour in a two-dimensional chromaticity diagram and virtually ignores the Y value.

The chromaticity diagram provides additional information about colour in terms of dominant wavelength and excitation purity. Though dominant wavelength gives indication of hue and purity provides chroma, it does not hold true for all colours due to the non-uniform nature of the chromaticity diagram. The diagram was used extensively early in the development of colour science applications. Wright,11 MacAdam12 and Stiles13 used the chromaticity diagram in describing ‘the just noticeable colour differences’. MacAdam’s famous perceptibility tolerance ellipses diagram work on the chromaticity diagram formed the basis for some important colour difference equations, like FMC-II and CMC (l:c). The chromaticity diagram is used in setting tolerances, for example signal light colours


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(green, amber, red and white) in rail, road or life jacket lamps. The diagram is also used in gamut mapping – in colour monitor colours (R, G, B), for different dye classes, etc. – while chromaticity coordinates are used in many applications, e.g. whiteness index calculations.

3.9 Usefulness of the CIE XYZ system

The CIE system specifi ed colour in numerical terms which was the one of the main objectives of the CIE. It helped in eliminating the subjectivity in colour assessment. Computation of tristimulus values X, Y and Z from refl ectance measurement formed the basis of all colorimetric calculations. The CIE standardized illuminants in terms of colour temperature, spectral power distribution and chromaticity coordinates and these were useful not only in calculation of tristimulus values, but also in visual assessment. The development of the standard observer was one of the signifi cant achievements and is the basic parameter for tristimulus calculation. The CIE also standardized illuminating and viewing conditions for spectrophotometers. Chromaticity coordinates provided more information in terms of dominant wavelength and purity, though these do not specify hue and chroma in the true sense. There have been a few additions

0.8

y 520

540

560

580

600

620650

770nm

380nm

480

500

x0.80.40.2

0.6

Green

Yellow

Orange

Red

Blue

Violet

0.4

0.2

0

3.4 Chromaticity diagram.


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since its introduction in 1931, like D illuminants, and ten degree observer, but the basic system remains unchanged and there is no possibility of replacing it altogether in the near future. The CIE system is very well accepted and successful in most colorimetric applications. Nevertheless there are some major limitations of the system which came to the fore as colorists started using it.

3.10 Limitations of the CIE system

The CIE specifi ed colour in terms of tristimulus values X, Y and Z, but even an expert cannot visualize colour from given tristimulus values.

The CIE ignores the physical or geometric attributes or, in simple words, the surface texture. For example, when a standard is glossy and a sample is matt their tristimulus values may match, but both the standard and sample will appear differently when observed visually. It is therefore necessary that one must have standard and sample with the same surface attributes to get any meaningful results. The CIE recommends the colour assessment – tristimulus values – to be calculated using illuminant D65 and ten degree observer or two degree observer. Both these observers represents average human response, but neither represents the individual observer and there may be differences in the observers. It is also not easy to simulate D65 in the laboratory as not many light booths simulate D65 correctly. Hence the calculated difference may appear differently if the illuminating conditions do not match.

The other major limitation of the CIE system is its non-uniformity. Similar numerical differences in different areas of the visible region appear differently when assessed visually. For example, the same magnitude differences in X, Y, Z (ΔX, ΔY and ΔZ) or in chromaticity coordinates (Δx, Δy and Δz) in a green pair and a red or blue pair will appear differently. In the case of the green pair the difference may be accepted visually, but for the same difference in the red or blue pairs, the sample may not be accepted. This is due to non-uniformity of the CIE system. This system does not specify chroma and hue, which are very important colour parameters, although purity and dominant wavelength specify chroma and hue indirectly. Tristimulus values of colour are merely arbitrary numbers having no units and which represent amounts of three unreal primaries which when mixed additively would give the colour. But it is not true for all colours.

3.11 Transformation and improvement

of the CIE system

Scientists and researchers started improving the non-uniformity by transformation of the CIE system and tristimulus values were converted into more uniform colour space. Hunter14 was the fi rst one to convert X, Y, Z into L a b colour space, known as Hunterlab colour space. The space signifi es the specifi cation of colour into three-dimension. The other colour space which is worth mentioning is ANLAB – suggested by Adams15 and Nickerson.16 While Hunter used square and square


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root function for conversion, Adams and Nickerson used polynomial equations for transformation. By 1976, there were as many as 20 colour spaces put forward by different colour scientists. Instead of helping colorists, the availability of so many colour spaces confused them. These colour spaces were based on different colour concepts.

In order to avoid confusion and bring in uniformity, the CIE stepped in again and introduced two new colour spaces – CIE 1976 L*A*B* and CIE 1976 L*U*V*. For transformation, cube and cube root functions were used to compute L*A*B* from X, Y and Z. Asterisk ‘*’ was used just to distinguish the CIE colour space from the other spaces. Of the two spaces, the CIE 1976 L*A*B* is used in textiles, paints, plastics, etc.; while the CIE 1976 L*U*V* is used in photography. We will discuss the CIE 1976 L*A*B* colour space and will not discuss the CIE 1976 L*U*V* colour space. The CIE17 released the mathematical equations for transformation of X, Y and Z into L* A* B* Cab* and hab. The original equations were revised by the CIE18 and are given below:

L*= 116f(QY) – 16

a* = 500{f(QX) – f(QY)} [3.7]

b* = 200{f(QY) – f(QZ)}

where QX = X/Xn; QY = Y/Yn; QZ = Z/Zn

and f(Qi) = Qi1/3 if Qi> (6/29)3

else, f(Qi) = (841/108)Qi + 4/29 if Qi ≤ (6/29)3

where i varies as X, Y and Z.

The tristimulus values Xn, Yn and Zn defi ne the tristimulus values of the standard illuminant with Yn equal to 100.

C*ab and hab are calculated as follows:

C*ab = {(a*)2 + (b*)2}½

hab = tan−1 (b*/a*)

If b* = 0 then hab = 90 – 90 sign(a*)

else, hab = 180 – (180/π) tan−1 (b*/a*) −90 sign(b*).

The CIE L*a*b* C*ab hab colour space is illustrated in Fig. 3.5.Colour can be specifi ed in CIE L*a*b* – lightness/darkness, red/green and

yellow/blue.L* varies from 100 to 0: for perfect white, L* is 100 and for black, it is zero.

When L* is 70, colour is light or light grey (when a and b are zero); when L* is 50, colour is medium or medium grey; when L* is 25 or less, colour is dark or


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dark grey. There is no colour having L* = 0 under SCI measurement, which is commonly used in most colour applications. L* can be near zero under SCE measurement or using 45/0 or 0/45 geometry.

When a* is positive, colour is red or in red direction; when a* is negative, colour is green or in green direction.

When b* is positive, colour is yellow or in yellow direction. When b* is negative, colour is blue or in blue direction.

Colour can also be specifi ed in LCH – lightness, chroma and hue. The chroma or saturation Cab* is the distance between the achromatic point and colour. Chroma can be explained from the a*b* plot (see Fig. 3.6). The longer the distance of a colour from the achromatic point, the higher is the chroma or brighter (more saturated) will be the colour. All bright yellows, oranges, reds, greens, blues and violets have medium to higher chroma values. The shorter the distance of colour, the lower will be the chroma or duller (less saturated) is the colour. Greys, olives and coffees have low chroma values.

Figure 3.7 illustrates hues in the a*b* plot. CIE hab is measured in degrees starting with hab=0 in the red direction and increasing anticlockwise. All real hues fall within defi nite angles expressed in degrees:

Red – 350 to 360 and 0 to 35Orange – 35 to 70Yellow – 70 to 105Green – 105 to 195Blue – 195 to 285Violet – 285 to 350

White

+bYellow

Green

a

–a

–bBlue

b*

L*

c*

h

+aRed

Black

3.5 The CIEL*a*b* 1976 Colour Space.


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3.6 Chroma C calculation from a and b.

b

c

c = (a2 + b2)½

b

a a

Yellow

70º

35º

350º

285º

195º

105º

Orange

VioletBlu

e

Green

3.7 Hue circle.


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L* a* b* are referred to as rectangular coordinates, L* Cab* hab are referred to as polar coordinates.

In conclusion, the CIE system provides a numerical specifi cation of colour in terms of tristimulus values X, Y, Z and chromaticity coordinates x, y, z. These are further transformed into more uniform colour space, L* a* b* Cab* hab. These numerical specifi cations form the basis for most colour applications.

3.12 Future trends

The CIE system is well established and despite its limitations, it enables in solving most colour applications quite satisfactorily. The CIE system is nearly eighty years old, but there have not been any major modifi cations to the basic system since its introduction and there is no possibility of the CIE system getting replaced by any other system in the near future. It is known that the CIE is not uniform due to which it has some limitations, so colour scientists and researchers modifi ed basic parameters (L* a* b* Cab hab) to improve its acceptance with visual assessment. The recent colour difference equation, CIEDE2000, which is the result of such modifi cations, hopefully will meet most requirements of colour difference assessment. However, for visual colour assessment, one must have accurate simulation of D illuminants, particularly D65. This is still not possible to simulate very easily in a laboratory or light booths. One must have the same illuminant for visual assessment as well as for instrumental assessment. When it is available, it will overcome this major limitation. The other area which may require improvement is the standard observer, which is an average of the limited number of observers (2° and 10° observers are the averages of 17 and 67 observers respectively). The objective should be such that the standard observer truly represents the average observer.

3.13 References

1. Munsell, A.H. (1905) A Color Notation, 1st Edition, Munsell Color Company, Baltimore, MD.

2. Munsell, A.E.O., Sloan, L.L. and Godlove, I.H. (1933) Munsell Neutral Value Scale, J. Opt. Soc. Amer., 23, 394–411.

3. Munsell Book of Colors, X-Rite Inc. USA 4. Judd, D.B., MacAdam, D.L. and Wyszecki, G. (1964) Spectral distribution of

typical daylight as a function of correlated color temperature, J. Opt. Soc. Amer., 54, 1031–1040.

5. Wright, W.D. (1928–1929) A re-determination of the trichromatic co-effi cients of the spectral colours, Trans. Opt. Soc., 30, 141–164.

6. Guild, J. (1931) The colorimetric properties of the spectrum, Phil. Roy. Soc. (London), A 230, 149.

7. Rigg, B. (1997) The Colour Physics for Industry, Society of Dyers and Colourists, 90–93.

8. Stiles, W.S. and Burch, J.M. (1958) NPL colour-matching investigation: fi nal report, Optica. Acta, 6, 1.


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9. Speronskaya, N.I. (1959) Determination of spectrum color coordinates for twenty-seven normal observers, Optics and Spectroscopy, 7, 424.

10. Stearns, E.I. (1975) Weights for calculation of tristimulus values, Clemson Rev. Ind. Managem. Tex. Sci., 14, 79.

11. Wright, W.D. (1941) The sensitivity of the eye to small colour differences, Proc. Phys. Soc. London, 53, 93.

12. MacAdam, D.L. (1942) Visual sensitivities to color differences in daylight, J. Opt. Soc. Amer., 32, 18–26.

13. Stiles, W.S. (1946) A modifi ed Helmholtz line element in brightness-colour space, Proc. Phys. Soc. London, 58, 41.

14. Hunter, R.S. (1942) ‘Photoelectric tristimulus colorimetry with three fi lters’, NBS Circular 429, U.S. Govt. Printing Press, Washington D.C., reprinted in J. Opt. Soc. Amer., 32, 509.

15. Adams, E.Q. (1942) X–Z planes in 1931 ICI System of Colorimetry, J. Opt. Soc. Amer. 32, 168–173.

16. Nickerson, D. (1950) Tables for use in computing small color differences, Am. Dyestuff Reptr. 39, 541.

17. CIE Publication No. 15, Supplement No. 2 (1978), Colorimetry (E1.3.1)(TC1.3) Paris, Bureau Central de la CIE.

18. CIE Publication No 15 :2004, Colorimetry, Central Bureau of the CIE, Vienna, 2004.

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88

4Visual and instrumental evaluation of

whiteness and yellowness

R. HIRSCHLER, SENAI/CETIQT Colour Institute, Brazil

Abstract: Whiteness and yellowness are important characteristics of many industrial products and due to the uncertainties of visual evaluation instrumental measurement is the preferred method. This chapter describes the problems involved in the visual assessment: the lack of standardized illumination and the controversies due to the defi nition of a ‘preferred white’. Whiteness and yellowness indices based on instrumental measurements are widely used in industry, cosmetics and dentistry, but even they do not always yield unambiguous results.

Key words: whiteness, yellowness, brightness, tint, fl uorescent whitening agent (FWA).

4.1 Introduction: whiteness and yellowness

White, like any other colour, is both an attribute of visual sensation and a characteristic of certain objects. We describe those objects as white which appear to be neither ‘coloured’ (i.e. their Munsell chroma is very low, usually less than a few tenths) nor ‘greyish’ (i.e. their Munsell value is very high, usually more than 9). According to the ISCC-NBS Method of Designating Colors (Judd and Kelly, 1967) white must have a Munsell chroma no higher than 0.5 for all hues, except for 4Y to 9Y where up to 0.7 is acceptable, and a Munsell value of at least 8.5.

White occupies a very small area within the CIE chromaticity diagram. The limits of objects that may commercially be called ‘white’ according to the CIE (2004) defi nition are shown in Fig. 4.1 as compared to the limits of optimal colours (calculated from data of Wyszecki and Stiles, 2000). The optimal colour boundary shows the limits for the given luminance factor (CIE tristimulus value Y=90) within which the chromaticity of all imaginable (not necessarily real) non-fl uorescent objects must fall. A signifi cant part of the whiteness area can only be achieved using fl uorescent whitening agents (FWAs).

However, white is not really like any other colour. The perception of white depends not only on the spectral characteristics of the stimulus reaching the eye and the characteristics of the observer, but also on the spatial characteristics of the refl ected light (diffuse or specular). Thus a mirror measured with the specular component included will show very high refl ectance (see Fig. 4.2) in the specular included (SPIN) mode as if it were white, while it will show nearly zero refl ectance in the specular excluded (SPEX) mode as if it were black. For comparison Fig. 4.2 also shows the refl ectance curves of a glossy white tile with both measurement geometries.

Visual and instrumental evaluation of whiteness and yellowness 89

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Wittgenstein (1977) raises the questions: ‘Why is it that something can be transparent green but not transparent white? … Why can’t we imagine transparent-white glass, – even if there isn’t any in actuality?’ Evans (1949) discusses in much detail the defi nition of colour (referring to a report of the Colorimetry Committee of the Optical Society of America) according to which ‘color can be seen in any mode and has only the attributes of hue, saturation and brightness’. He then concludes, that ‘color seen in the aperture mode does not contain either white or grey. We are forced to the conclusion, therefore, that white and grey are not colors under the defi nition.’

0.6

0.5Optimal colours

Whiteness limits

0.4

0.3

0.20 0.2 0.4

x

y

0.6

4.1 Limits of white objects and optimal colours in the CIE 1931 chromaticity diagram for Y = 90. The X within the whiteness limits marks the position of the ideal white for illuminant D65.

White tile SPIN

100

80

60

40

20

0400 500

Wavelength (nm)

Ref

lect

ance

(%

)

600 700

White tile SPEX

Mirror SPIN

Mirror SPEX

4.2 Spectral refl ectance factor measurements of a glossy white tile and a mirror with the specular component included (SPIN) and excluded (SPEX).


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On the other hand whiteness is so much part of the colour sensation that some colour order systems, among them the very popular Swedish NCS – Natural Colour System (see Chapter 2), use ‘white content’ as one the three attributes of any colour (together with ‘black content’ and hue). We would thus be in a very diffi cult position indeed if we were to leave white out of the universe of colours, but we must always remember that it has a unique position in this universe.

Yellow is a much simpler concept than white, being one of the fi ve principal hues of the Munsell system and one of the four unitary hues of the NCS system (see Chapter 2). According to the ISCC-NBS Method of Designating Colors (Judd and Kelly, 1967) we may call ‘yellow’ (without a hue modifi er) colours with Munsell hue 1Y to 7Y with chroma above 2 to 3 and value above 5.5.

The concept and the measurement of whiteness are of great importance in a number of practical fi elds. We shall see in the following sections the possibilities for the visual and instrumental evaluation and the indices used for the numerical characterisation of whiteness. Yellowness as a characteristic of materials is used only in a limited way; it shall therefore be discussed only briefl y.

4.2 Visual assessment of whiteness

The application of the concept of ‘whiteness’, as we could see above, is not limited to objects which are totally devoid of hue (i.e. whose Munsell chroma is zero), but also (in practice, mainly) to a wide range of slightly tinted ‘near-whites’ where the degree of whiteness is not simply a function of lightness. As soon as a combination of lightness, chroma and hue has to be evaluated, observers differ in their assessment of which sensation is to be called ‘whiter’.

One of the major diffi culties in visually assessing whiteness, determining which of two white objects is to be considered ‘whiter’ is the lack of a reference white. Before the advent of FWAs the idea of the ‘ideal white’ seemed to be very simple: an object diffusely refl ecting 100% of the incident light throughout the visible spectrum. Even that could be questioned: for some observers a bluish (or violetish or even greenish) white would be preferred, i.e. considered whiter than a neutral white of equal or even somewhat higher luminance factor. For samples treated with FWAs there is no well-defi ned upper limit, and the possibility of having ‘metameric whites’, i.e. samples with different spectral power distribution (SPD) but the same degree of whiteness, is signifi cantly greater. The decision would then have to be taken whether to prefer a white with a somewhat higher luminance factor, or one with lower chroma, or a third one with a different tint. According to Vaeck (1979): ‘At any given level of illuminance and for all normal observers, the chromaticity corresponding to the highest whiteness perception is never identical with the achromatic colour. It is always to the blue or purple side of the achromatic point (achromatic being taken here in the colorimetric sense).’

When ordering 20 white samples (all within the CIE defi ned range of ‘commercially white’) by 22 naïve observers, Jafari and Amirshahi (2008) found


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that observers can rank samples with a low whiteness index with much more consistency than those with a high whiteness index.

4.2.1 The effect of illumination

In spite of the truly amazing capability of the human eye to adapt to changes in the illumination, visual evaluation of whiteness (just as that of colour) depends on the quality (and to a lesser degree, the intensity) of illumination. A white object will appear white even under extreme conditions, e.g. illuminated by monochromatic yellow sodium light (Wright, 1972). A white sheet of paper in the shade may emit less radiant power than a light grey sheet in sunlight, yet we shall perceive the former as white and the latter as grey.

For the comparative evaluation of non-fl uorescent objects the quality of illumination may not be critical, but, as we shall see later, the spectral radiance factor, and consequently the perceived whiteness of fl uorescent objects, depends to a large extent on the SPD of the illumination. Therefore it is very surprising how little attention to the exact specifi cation and recording of the SPD of the illumination was paid during the visual assessment and evaluation of white samples reported in the literature. Over 35 years ago Stensby (1973) complained that ‘many of the previous whiteness studies lack strict scientifi c meaning because experimental conditions and infl uencing factors were not carefully standardized and defi ned’. This was, in a way, understandable for the early work done on non-fl uorescent samples, much less so for those involving samples treated with an FWA. MacAdam (1934) reported on the arrangement for the visual classifi cation of 36 samples by 30 observers as follows: ‘A room was selected on the top fl oor of a building high enough to avoid refl ection from neighboring buildings. This room had white walls and a northern exposure through large windows. The tests were carried out on completely overcast days.’ Judd (1936) was even less precise in determining the illumination conditions for his 15 observers: ‘The observations were carried out in the observer’s own laboratories under the customary illuminant, presumably daylight, and by his own method.’

Hunter (1958) reported on the work of the Inter-Society Color Council Subcommittee on Problem 19, White Surfaces, and stated that: ‘Standardized artifi cial light sources and inspection cabinets are popular, but natural daylight, preferably from the north sky, is still used for much if not most visual examinations.’

Since the 1970s, experimenters have become more careful in designing and defi ning the experimental conditions also as regards illumination, yet it should be noted how little we know of the exact conditions under which the basic experiments, leading up to the currently used whiteness formulae, were conducted. Probably the most carefully designed and best documented series of experiments in this fi eld were those conducted by members of the CIE Subcommittee on Whiteness as reported in Die Farbe (Berger-Schunn, 1977; Berglund and Stenius, 1977; Mattiello and Lozano, 1977; Stenius, 1977) and in a series of articles by


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Ganz (1976, 1979a, 1979b), Ganz and Griesser (1981) and Ganz and Pauli (1995) in Applied Optics.

From these publications we learn that indeed, the experiments were carefully designed, but, unfortunately, the technical details of the illumination are not suffi cient to reproduce them if one wanted to. Berger-Schunn (1977) specifi ed the following conditions: (a) ‘northsky light, behind glass’ and (b) ‘alternatively artifi cial daylight the irradiance distribution of which should be largely in accordance with D65’. Berglund and Stenius (1977) only state that the experimental conditions have to be standardized: ‘With regards to whiteness this means that the surround should be neutral with constant illuminance and constant SPD of the illuminant. The latter should moreover be of best obtainable agreement with the CIE standard illuminant D65 chosen for colorimetric analysis of the samples.’

Mattiello and Lozano (1977) illuminated their samples by ‘southern-hemisphere daylight of approximately 1000 lx’, while Stenius (1977) stated that: ‘The illumination was either natural daylight in front of a window, or the samples were placed in a viewing booth equipped with an illuminant claimed to correspond to the CIE standard illuminant D65.’ Swenholt et al. (1978) used ceiling illumination of Macbeth D65 fl uorescent lamps (but gave no details of the characteristics of these).

Ganz (1976) described in detail the importance of the control of sample irradiation in both visual and instrumental assessment, but also explained that: ‘For some industrial routine work such discrepancies are without consequence. Relative visual assessments and relative instrumental measurements of whiteness are suffi cient for comparing the whitening effect of similar products, evaluating various application procedures, assessing fastness properties, etc.’ On the other hand, for critical work this may not be suffi cient, as he himself described in an earlier work (Ganz, 1972): three whites W1, W2, W3 appear as increasing in whiteness under D65, whereas W2 appears less white than W1 under illuminant A (less UV) and whiter than W3 under unfi ltered Xenon light (more UV). In their work on the assessment of tint Ganz and Griesser (1981) stated that the ‘visual assessments were carried out on a windowsill illuminated by natural north sky light through UV transmitting windows’.

Recent research sheds some light on the infl uence of illumination on whiteness perception. Ayama et al. (2003) changed the illumination by using fl uorescent lamps with correlated colour temperatures (CCT) from 2800 to 6700 K and observing the whiteness ranking of a neutral (N9.25) and eleven nearly white (Munsell value 9.25, Munsell chroma 0.5), non-fl uorescent samples. They found signifi cant changes in the ranking of different hues (some got a higher, others a lower ranking with the increase of CCT) in spite of the samples being non-fl uorescent. Katayama et al. (2007) used exactly the same sample set, but only four fl uorescent lamps, with CCT ranging from 3330 to 6160 K, and arrived at the conclusion that the ranking of different hues depended very little on the CCT of the illumination.


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While the importance of the SPD of illumination on the ranking of non-fl uorescent whites may be open to discussion, for fl uorescent whites this SPD is obviously all-important. What should be the reference illuminant? It depends primarily on the application in question: paper industry professionals have long argued that it must be ‘indoor daylight’, which has only recently been defi ned by the CIE (2009). For the textile industry it has for a long time been D65. For household appliances probably tungsten light (illuminant A) should be preferred, while for the evaluation of the whiteness of teeth any of the above might be used (from a purely practical point of view) as reference illuminant.

The current most widely used whiteness and yellowness formulae (see 4.4) can only be applied to illuminant C or D65, thus these should be selected also for visual evaluation, provided they can be realized as practical sources.

4.2.2 Practical daylight simulators for visual evaluation

Why is it that no ‘standard light source’ was used in any of the publications cited above? The relative SPD of the D65 CIE standard illuminant for daylight (CIE, 1986a) is based on experimental measurements of natural daylight, as reported by Judd et al. (1964). The D illuminants, recommended by the CIE (1964) as the SPDs best representing various phases of daylight, have not yet been, and probably never will be reproduced ‘exactly’, i.e. there are no physical sources having identical SPDs to D65 or any of the other daylight illuminants.

Daylight simulators (i.e. practical sources with SPD similar to daylight illuminants) can be evaluated by a method published as a joint ISO/CIE Standard 23603 (2005). The basis for the assessment is the special metamerism index for change in illuminant, using eight pairs of virtual specimens: three pairs for the UV-range evaluation (consisting each of one fl uorescent and one non-fl uorescent specimen) and fi ve pairs for the visible range evaluation. The colour difference between the members of the pairs is (by defi nition) zero for the reference illuminants (D50, D55, D65 or D75) and the average of that shown under the test light source is the UV-range metamerism index (MIuv) for the fi rst three pairs and the visible range index (MIvis) for the set of fi ve pairs. Based on these indices light sources (daylight simulators) are rated in fi ve categories for the visible and fi ve for the UV range, and the rating is given by two letters, the fi rst for the visible and the second for the UV range, category A being the best and category E the worst.

According to the Standard Practice for Visual Evaluation of Color Differences of Opaque Materials (ASTM, 2003): ‘For critical appraisal of colors and color differences, the category determined by that method shall be BC (CIELAB) or better. This rating ensures that the source provides ultraviolet and visible power in the right proportions to make both nonfl uorescent and fl uorescent materials look very nearly the way they would in the corresponding phase of natural daylight.’

There are currently three major technologies competing in the fi eld: fi ltered incandescent (tungsten fi lament) lamps with an additional UV source, fi ltered


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xenon short arc and fl uorescent lamps. According to Hirschler and Oliveira (2007) there exist Category BC sources (i.e. acceptable according to ASTM D 1729) available for each technology, but the vast majority of commercially available light booths and sources are far from being adequate. This, unfortunately, means that most of the visual evaluations of white (particularly fl uorescent white) materials are not performed under conditions comparable to those of instrumental measurements, thus no close correlation may be expected between the two.

4.2.3 Sorting and ranking methods for visual whiteness determination

Whiteness, as already described above, is a very complex concept. Even its dimensionality is not quite certain: according to some studies (Evans, 1964) achromatic colours are multidimensional. Lie (1969) reviewed the literature on this problem and stated that ‘as far as phenomenological descriptions are concerned, general agreement appears to exist … that the achromatic colours may be varied along two separate dimensions, from black to white and from dim to bright’.

In the experiments leading up to the current CIE whiteness formula Berglund and Stenius (1977) arrived at the conclusion, that: ‘Perceptual whiteness may be regarded as unidimensional in the sense that a onedimensional solution correlates well with direct unidimensional scaling … [while a] more sophisticated analysis reveals that perceptual whiteness is to be considered multidimensional in the sense that independent perceptual components may in different combinations create the same perception of whiteness.’

The two most important forms of visual experiments leading up to the quantifi cation of whiteness are either of the pair comparison or the ranking type. In the former the observer is shown only two samples at a time, and has to decide which of the two is ‘whiter’. In forced judgements the ‘they are equally white’ answer is not acceptable. The consistency of pair comparisons can be measured by the ratio of circular triads, where the same observer found sample A whiter than B, B whiter than C and C whiter than A. In the CIE experiments (Stenius, 1977 and Ganz, 1979a, 1979b) the individual number of circular triads ranged from 0 (for some of the professional colour matchers) to 62 out of the theoretically possible 120 triads. It is noteworthy that the average number was somewhat higher for the assessments performed under natural daylight and somewhat lower for those under artifi cial light.

Ranking (placing a given number, in the CIE experiments 10, of samples in order according to increasing whiteness) is even more diffi cult, particularly if whites of different tint (i.e. not entirely neutral) are involved. Some professionals, who performed very well in the pair comparison experiments, simply refused to assess the samples by ranking, declaring ‘the task is impossible owing to the extreme differences in tint’ (Ganz, 1979b).


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4.3 Measuring techniques and instruments

In view of the diffi culties in visual whiteness assessment, instrumental measurement and evaluation of white samples has long been the preferred method. The measurement of fl uorescent samples, however, is far from being straightforward: differently from the spectrophotometric measurement of non-fl uorescent specimens both the optical arrangement of the instrument and the quality of the illumination have a fundamental effect on the result. Figure 4.3 shows the total radiance factor (TRF – the sum of refl ectance and fl uorescence) measurements for a fl uorescent white textile sample with two instruments. Even though the instrument with the ‘classical’ forward optics design (monochromatic illumination of the sample) has enough energy also in the UV range the TRF curve is completely wrong. The proper arrangement is polychromatic illumination and monochromatic viewing (‘reverse optics’), where we get the true TRF.

4.3.1 The effect of illumination

In the spectrophotometry of non-fl uorescent specimens the SPD of the light source has no infl uence on the results. The measured spectral transmittance or refl ectance is a relative quantity, independent of the illumination. This, however, is not the case when measuring fl uorescent specimens, where the SPD, particularly the UV/visible ratio of the illumination, is of primary importance. Figure 4.4 shows the TRF of a non-fl uorescent and a fl uorescent white textile sample measured with three positions of an adjustable UV fi lter (see 4.3.2). For the non-fl uorescent specimen the UV content has no infl uence on the TRF, while

Mono-poly

140

120

100

80

60

40

20

0400 500 600 700

Tota

l rad

ianc

e fa

ctor

(%

)

Wavelength (nm)

Poly-mono

4.3 Effect of instrument optics on the total radiance factor of fl uorescent specimen measured with forward (mono-poly) and reverse (poly-mono) optics.


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for the fl uorescent one there is a signifi cant difference in the 420 to 530 nm region and, consequently, on the whiteness indices, which increase from WI = 66 for the UV cut-off measurement to WI = 146 for the 100% UV measurement.

4.3.2 Practical daylight simulators for the instrumental measurement

We have seen before that for visual work most of the currently available practical daylight simulators are not quite acceptable according to the relevant standards. With colour-measuring spectrophotometers the situation is somewhat different. Using fi ltered pulsed xenon illumination it is possible to achieve excellent simulation of the D65 illuminant. Hirschler et al. (2003) reported Category AB and Category BA ratings for different commercial instruments and stated that: ‘Modern single-monochromator spectrophotometers can have fully satisfactory daylight simulators as light sources both for the UV and the visible range when applied to fl uorescent whites with excitation mainly in the UV region.’

The problem with these instruments is that the light source (nearly always a pulsed xenon lamp) ages and loses energy in the UV region (300 to 400 nm) as compared to the visible region (400 to 700 nm) of the spectrum. There are currently two technologies used to overcome this problem: the Gaertner-Griesser (1975) device and numerical UV control (Imura et al., 1997).

The Gaertner-Griesser (1975) UV calibrating device consists of a UV adjustment fi lter with a steep absorption slope at about 400 nm which is inserted partially into the light path of the source (Fig. 4.5.) UV-free and UV-rich radiation are mixed inside the integrating sphere of the instrument illuminating the sample

Fluorescent – 100% UV

Fluorescent – 50% UV

Fluorescent – No UV

Non-fluorescent

400

140

120

100

80

60

40

Tota

l rad

ianc

e fa

ctor

(%

)

Wavelength (nm)

20

0500 600 700

4.4 Effect of the UV content of the illumination on the total radiance factor of fl uorescent and non-fl uorescent specimens.


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with polychromatic light. When the lamp is new (high UV content) there is an excess of UV radiation which can be fi ltered out by inserting the UV-absorbing fi lter up to the optimum point. As the lamp ages and loses some of the UV (relative to the visible, i.e. longer wavelength) radiation, the UV fi lter is partially moved out of the beam thus recomposing the ‘original’ illumination (which is the best fi t to D65). The degree of removal is determined by a calibration process (Griesser, 1994). The ‘UV calibration’ is changing the balance between the short and the long wavelength parts of the emitted light; the illumination inside the sphere is a linear combination of I1 (UV-rich illumination) and I2 (UV-free illumination):

Ic(λ) = KI1(λ) + (1-K)I2(λ) [4.1]

where Ic(λ) is the composite illumination and K depends on the position of the fi lter, is λ-independent.

As can be seen in Fig. 4.6 the fi lter only changes the shape of the spectral curve in the 300–400 nm range and can thus ‘bring back’ the original UV content, it serves to compensate for the ageing of the lamp, but cannot ‘improve’ the D65 fi t, it will not turn the source into a better daylight simulator than it originally had been. This method had been developed before pulsed xenon was introduced in spectrophotometers, but has been used since in most of the advanced industrial instruments.

In 1997 Imura et al. patented a method called ‘numerical UV control’ or NUVC based on making two fl ashes for each measurement: one with a lamp with a UV-absorbing fi lter in front of it (‘UV excluded’) and another without such a fi lter (‘UV included’). In this method both lamps make one fl ash each (whose intensity is checked) for the total spectral radiance factor β1(λ) by illumination I1 (UV-rich) and for β2(λ) by illumination I2 (UV-free) and then the best linear combination of β1(λ) and β2(λ) is determined by calculation:

Integrating sphere

Xenon lamp

VUV-rich radiation

UV-free radiation

Mixed radiation

Sample

UV-absorbing

filter(400 nm)

4.5 The Gaertner-Griesser UV calibrating device (Griesser, 1994).

© 1994 John Wiley & Sons, Inc. Reprinted with permission of John Wiley & Sons, Inc.


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βc(λ) = Kλβ1(λ) + (1-Kλ)β2(λ) [4.2]

where βc(λ) is the composite total spectral radiance factor and Kλ is determined by calculation for each fl uorescent wavelength λ, may be λ-dependent (Imura, 2008).

In this case the UV part of the illumination (and the resulting fl uorescence) may be numerically calculated, in a sense ‘optimized’ between the two curves in the 300 to 400 nm region and thus the CIE rating showing the goodness of fi t is normally very high. In the visible region (400 to 800 nm) the goodness of fi t depends on the D65 fi lter, but in the case of modern instruments this is normally also very good, thus this technology ( just as the Gaertner-Griesser method) offers excellent daylight simulation in colour measuring spectrophotometers, adequate for most practical purposes.

4.3.3 Measurement of white and near-white samples

The refl ectance curves of some of the whitest non-fl uorescent artefacts used as ‘white standards’ in spectrophotometry are illustrated in Fig. 4.7.

Whiteness measurement, like colour measurement in general, is subject to measurement uncertainties and errors, and to a great extent these are due to the differences in illumination and measurement geometry of different instruments. In a detailed study conducted in the textile industry (Hardt et al., 2003), 8 non-fl uorescent and 4 fl uorescent textile samples were measured in 16 industrial laboratories, 5 public measuring laboratories and by 4 instrument manufacturers, and the results were disastrous. The difference between some of the non-fl uorescent samples was as high as 34 Berger whiteness units; for fl uorescent samples this was only somewhat worse (36 Berger units). Even within the public measuring laboratories group differences

300 400

140

160

120

100

80

60

40

Spe

ctra

l pow

er d

istr

ibut

ion

(%)

Wavelength (nm)

20

0500 600

UV = 0%

UV = 50%

UV = 100%

700 800

4.6 Three levels of UV radiation in an industrial refl ectance spectrophotometer with UV calibration capabilities.


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of 15 to 20 units were found. Willis (2002) reported differences of up to 20 CIE WI units between measurement results on the same samples on different instruments as ‘not unusual’. These differences may be reduced to a few CIE units with a proper calibration procedure. According to Hirschler et al. (2003) the differences between nominal values of a research institute’s BAM-traceable measurements and the BAM data can be as large as 9 Ganz-Griesser units, but, under carefully controlled conditions, different instruments can measure with a difference of only a few CIE WI units as shown in Table 4.1. Using proper calibration and adjustment procedures in a comparative trial involving 24 different instruments the standard deviation of the different whiteness value results was 0,7 whiteness units (ISO, 2004).

It has to be emphasized here that these differences are due only to measurement uncertainties (instrument geometry, illumination, calibration procedure) and the

100

80

60400 500

Wavelength (nm)

Ref

lect

ance

(%

)

Spectralon

Barium sulfate

Russian opal glass

White ceramic tile

600 700

4.7 Refl ectance curves of four white artefacts used for the photometric scale adjustment of refl ectance spectrophotometers.

Table 4.1 CIE WI measured on four different instruments and compared to the nominal values given by the Hohensteiner Institute for the four textile samples and by BAM for the Halon standard

Textile samples

Instrument T1 T2 T3 T4 Halon standard

Nominal 77.32 103.71 128.37 154.77 124.91 SF500 77.40 102.40 126.50 153.80 121.70

Filt

er

CM3720d 77.88 103.04 127.00 156.29 122.36

CM3600d 78.54 105.08 129.71 154.97 125.75

NU

VC

CM2600d 73.26 100.13 127.35 155.41 124.71

Source: Reproduced from Hirschler et al. (2003) by permission of the CIE Central Bureau, Vienna, Austria.


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situation becomes much worse when these are combined with the diffi culties of translating measurement results into visually meaningful whiteness formulae.

4.4 Indices for whiteness and yellowness

Three years after the introduction of the CIE system of colour measurement MacAdam (1934) suggested a method for the numerical specifi cation of whiteness based on brightness (‘a known factor of the value of Y’) and purity. Based on a study of 36 white textile samples, he arrived at the conclusion that for these samples, all having a dominant wavelength of 575±1 nm whiteness depends primarily on brightness when the purity was less than three percent; and on purity for samples having a purity greater than fi ve percent. The fi rst whiteness formula was suggested by Judd (1935) and it was followed by literally hundreds of others, which found their way into numerous fi elds of applications. References to most of these can be found in Sève (1977) and a brief description of some of the most popular ones in Puebla (2002).

4.4.1 Luminance factor Y

Everything else (hue and chroma) being equal higher Y always means higher whiteness, but we know that for the large majority of practical samples we always have to consider the infl uence of all three dimensions. In some cases Y alone was used for the characterization of the whiteness of a specimen (Hunter and Harold, 1987), such as for grading raw cotton (where it is to be expected that the yellowness will be eliminated by bleaching) or in the measurement of soil removal for textiles.

4.4.2 Paper brightness

For over 70 years the paper industry has been using ‘brightness’ as a useful measure to describe the optical characteristics of pulp and paper, particularly during the process of bleaching of pulp. (It is somewhat confusing that the word ‘brightness’ is used differently to the established CIE usage where it means the perceptual correlate of luminance – in this chapter we will restrict the usage to the special meaning as used in the paper industry.)

Paper brightness is a weighted function of the spectral refl ectance as defi ned by several national and international standards, but it is important to notice that the measurement conditions recommended are different, thus leading to different results. What has become known as TAPPI (or GE) brightness (TAPPI, 2008; ASTM, 2007) refers to bi-directional (45/0) measuring geometry. These standards defi ne the SPD for a light source between CIE illuminants A and C as illustrated in Fig. 4.8. ISO brightness (TAPPI, 2006; ISO, 2009) is defi ned for diffuse/0 measurement geometry and illuminant C, while D65 brightness (ISO, 2008) for diffuse/0 measurement geometry and illuminant D65.


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Whatever the geometry and the illumination, all paper brightness measurements refer to fi ltering the refl ected light by a fi lter with maximum transmittance at 457 nm and a width at half height of 44 nm (see Fig. 4.9). In modern instruments the measurements are made spectrally and the same weighing functions applied to arrive – depending on the illumination and measurement geometry – at the TAPPI, ISO or D65 brightness value. The considerations of Bristow (1994a) made for ISO brightness are thus valid for all brightness measurements:

• [ISO] brightness is essentially useful as an arbitrary but sensitive measure of the progress of bleaching in a bleaching plant.

120

100

80

60

40

20

0300 350

Wavelength (nm)

Spe

ctra

l pow

er d

istr

ibut

ion

(%)

400

ASTM

CIE A

CIE C

450 500

4.8 SPD of the light source recommended by ASTM (2007) and TAPPI (2008) (solid curve) for the measurement of TAPPI (GE) brightness as compared to that of CIE illuminants A and C.

4.9 The brightness function with maximum at 457 nm and full width at half height of 44 nm as compared to the spectral refl ectance of a bleached and blued paper specimen.

100

80

60 BleachedBleached and bluedBrightness function40

20

0400 500

Wavelength (nm)

Ref

lect

ance

(%

)/B

right

ness

func

tion


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• [ISO] brightness is not a direct measure of any visual property. It is not related to any system of colorimetric measurement, and it has no foundation in perceptual psychology.

Brightness measurements should thus never be used in the place of whiteness measurements. Figure 4.9 also shows the refl ectance curves of two paper samples, one bleached and the other one bleached and ‘blued’ (tinted with a violet dye to achieve higher whiteness). Visually the blued sample appears to be signifi cantly whiter and this is well refl ected in the much higher whiteness index of the latter (CIE WI = 89.1) as compared to the fi rst (CIE WI = 76.4), while the ISO brightness values are nearly the same: 88.9 vs. 87.9.

4.4.3 Traditional whiteness formulae (Berger, Hunter, Stensby, Taube)

One of the best known traditional formulae was developed by Berger (1959). It is based on the Selling and Friele (1950) data with some new, FWA-treated samples added. The original formula was suggested in a form directly related to values obtainable by the then most popular tristimulus instruments:

WIBerger = f(–Y) + (

–Z –

–X) [4.3]

where –Y,

–X and

–Z are the measured instrumental values (with MgO=100 as white

reference), from which the 2 degree CIE tristimulus values for illuminant C may be calculated by

X = 0.783–X + 0.197

–Z

Y = –Y

Z = 1.181–Z [4.4]

For f(–Y) Berger suggested Y/3, the whiteness formula. Equation 4.3 thus

became

WIBerger = (Y/3) + (–Z –

–X) [4.5]

In various later publications this was then converted to CIE tristimulus values in the form

WIBerger = Y/3 + k1Z – k2X [4.6]

but, depending on who quoted the original publication (Berger, 1959), k1 and k2 took different values as shown in Table 4.2.

Hunter (1960) suggested a very simple formula based on the difference of the Hunter lightness (L) and yellowness (b) values:

WIHunter = L – 3b [4.7]


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which, expressed in terms of CIE tristimulus values (ASTM, 2005) takes the form:

WIHunter = 10(Y – 21)1/2(Y – 0.847Z)/Y1/2 [4.8]

Stensby (1967) modifi ed the Hunter formula by adding a redness term:

WIStensby = L – 3b + 3a [4.9]

which, expressed in terms of CIE tristimulus values (Thielert and Schliemann, 1972) takes the form:

WIStensby = 20.832 – 63.50Y + 55.12X [4.10]

√Y

Taube (1958) simply took the weighted difference between the blue and green refl ectance:

WTaube = 4B – 3G [4.11]

which, according to Thielert and Schliemann (1972) takes the form

WTaube = 3.97Z – 3Y [4.12]

while the ASTM standard (2005) gives it as

WTaube = 3.388Z – 3Y [4.13]

Thielert and Schliemann (1972) compared the performance of the Berger, Taube, Hunter, Stensby and Stephansen formulae and found that they are all equally well suited for describing the visual impression of whiteness, except when the samples that differed strongly in hue or neutral white had to be compared with lighter but distinctly coloured samples. They found correlation coeffi cients above 0.97 for not too different samples, but for ‘complicated’ sample sets it was 0.78 or even much less.

Figure 4.10 compares the hue preference of the four maybe best known conventional formulae. The different whiteness indices are plotted for a set of Munsell samples of different hue at chroma 0.3 and the neutral of the same Munsell value (9) in function of the hue. The Taube and Hunter formulae show no hue preference, which means that the P, PB and B samples are rated whiter, the G to R

Table 4.2 Coeffi cients in the Berger whiteness formula (Eq. 4.6) according to different sources; all values refer to CIE illuminant C

Observer Reference f(–Y ) k1 k2

CIE 1931 Puebla (2003) Y 3.440 3.895CIE 1964 Puebla (2003) Y 3.448 3.905CIE 1931 Thielert and Schliemann (1972) Y/3 1.240 1.310CIE 1931 Smith (1997) Y/3 1.060 1.277CIE 1931 ASTM (2005) Y 3.108 3.831


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samples are rated less white than the neutral, the RP and BG hues are judged equal to the neutral. The Stensby formula (red preference) rates the R and RP hues whiter, the B as white as and the BG to YR less white than the neutral; while the Berger formula (green preference) rates the R and RP less white and the BG whiter, the G as white as the neutral.

4.4.4 Colour difference from a reference white

A very logical whiteness index was mentioned by Judd and Wyszecki (1963) based on the colour difference from the ‘preferred white of whatever material is being studied’. Today we would defi ne it as the colour difference (in CIELAB) from the perfect diffuser:

W = 100 – √(100 – L*)2 + a*2 + b*2 [4.14]

This index has never been much used, because it does not take into account the preference for bluish white as opposed to yellowish whites; a sample with the same L* and a* coordinates would be rated just as white with b* = 0.5 (yellowish) as with b* = – 0.5 (bluish) which, visually, is clearly not the case. In spite of its shortcomings this index appears in the literature for some applications.

4.4.5 The Ganz linear whiteness and tint formulae; hue preferences

Contrary to the ‘traditional’ whiteness formula based on some kind of combination of the three tristimulus functions, Ganz (1972) suggested a family of linear whiteness formulae with the general form

100

90

80

70

60

50

WI T

aube

Munsell hue5R 5P5RP 5PB 5B 5BG 5G 5GY 5Y 5YR

100

90

80

70

60

50

WI H

unte

r


100

90

80

70

60

50

WI S

tens

by


100

90

80

70

60

50

WI B

erge

r


4.10 Comparison of the hue preferences of four conventional brightness formulae.


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WI = f(Y) + g(x,y) [4.15]

Based on systematic investigations of previously published L, a, b type (e.g. Hunter, Stensby) and B, G, A type (e.g. Berger, Taube) formulae his new, versatile, linear formula was:

WIGanz = ∂W∂Y

(Y + ω(px + qy)) + c [4.16]

in which p and q depend on the hue preference only, c serves to keep W = 100 for the ideal diffuser and with (∂W / ∂Y) = 1. The scaling of the formula may be adjusted and any combination of illuminant and observer may be selected by varying the coeffi cients in the formula. This formula is the basis for the Ganz-Griesser method described in 4.4.7.

Based on visual assessments of the sample set prepared by Berger-Schunn (1977) for CIE TC-1.3, Ganz (1976, 1979a) later proposed a tentative formula for tint and new linear formulae for whiteness of neutral, green and red hue preference. The neutral hue preference formula for illuminant D65 and both CIE 1931 and 1964 standard colorimetric observers is:

WIGanz, neutral = Y – 800(x – x0) – 1700(y – y0) [4.17]

the supplementary formula of green hue preference:

WIGanz, green = Y – 1700(x – x0) – 900(y – y0) [4.18]

and the one of red hue preference:

WIGanz, red = Y – 800(x – x0) – 3000(y – y0) [4.19]

Figure 4.11a shows the very high correlation of the Ganz green preference formula (Eq. 4.18) with the Berger formula (Eq. 4.6), and Fig. 4.11b that of the Ganz red preference formula (Eq. 4.19) with the Stensby formula (Eq. 4.10).

Two standard tint formulae were proposed for illuminant D65, one each for the CIE 1931 standard colorimetric observer:

T = – 1000(x – x0) + 700(y – y0) [4.20]

and for the CIE 1964 standard colorimetric observer:

T = – 900(x – x0) + 800(y – y0) [4.21]

Based on new visual evaluations these tint formulae were later slightly modifi ed by Ganz and Griesser (1981), at the same time setting T = 1 for the perfect diffuser:

TVGG(1931) = 1000(x0 – x) – 650(y0 – y) + 1 [4.22]

and

TVGG(1964) = 900(x0 – x) – 650(y0 – y) + 1 [4.23]


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Red preference

Green preference90

85

80

75

70

65

6060 65 70

WI Berger

WI G

anz

red/

gree

n

75 80

R2 = 0.9982

4.11a Correlation of the Ganz green preference formula with the Berger formula (also showing green preference).

Red preference

Green preference90

85

80

75

70

65

6075 80 85

WI Stensby

WI G

anz

red/

gree

n

90 95

R2 = 0.9806

4.11b Correlation of the Ganz red preference formula with the Stensby formula (also showing red preference).

4.4.6 The CIE whiteness and tint formulae

In the 1960s and early 1970s there were hundreds of whiteness formulae used in different applications, but none of them was proven to be much better than the others. A subcommittee was formed in 1969 within CIE Technical Committee


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TC –1.3 to start a systematic study on whiteness. Berger-Schunn (1977) prepared a set of 56 paper samples within a range of (what we now call) CIE whiteness 48 to 148 and CIE tint –10 to +5. Berglund and Stenius (1977) provided a methodological study on the evaluation of perceptual whiteness, and Mattiello and Lozano (1977) and Stenius (1977) reported on the psychophysical studies and the fi rst results. Sève (1977) published an extensive bibliography on whiteness.

In 1978 a new CIE task force was set up (Brockes, 1982) which, after much discussion, formulated a proposal for two whiteness and two tint formulae, one each for the 1931 and the 1964 standard colorimetric observer, based on the publications of the subcommittee cited above and later publications by Ganz (1976, 1979a, 1979b) and Ganz and Griesser (1981). The whiteness index adopted was the neutral (no hue preference) formula of Ganz (Eq. 4.17) and the tint formula was that of Ganz and Griesser (1981) (Eqs. 4.22 and 4.23) but without the additional term ‘+1’ at the end (thus for the perfect diffuser T = 0 again).

The current form of the CIE whiteness and tint formulae for the CIE standard illuminant D65 was published in the latest edition of CIE Publication 15 (CIE, 2004) as:

W = Y + 800(xn – x) + 1700(yn – y) [4.24]

W10 = Y10 + 800(xn,10 – x10) + 1700(yn, 10 – y10) [4.25]

Tw = 1000(xn – x) + 650(yn – y) [4.26]

Tw, 10 = 900(xn,10 – x10) – 650(yn,10 – y10) [4.27]

where Y is the Y-tristimulus value of the sample, x and y are the x,y chromaticity coordinates of the sample, and xn, yn are the chromaticity coordinates of the perfect diffuser, all for the CIE 1931 standard colorimetric observer. Y10, x10, y10, xn,10 and yn,10 are similar values for the CIE 1964 standard colorimetric observer.

The application of the CIE formulae is restricted to samples that

• are called ‘white’ commercially;• do not differ much in colour and fl uorescence; and• are measured on the same instrument at nearly the same time.

The CIE also set limits to the whiteness and tint limits. The values of W and Tw must lie within the following limits for the 1931 standard colorimetric observer: 40 < W < 5Y-280 and –4 < Tw < +2 and similarly for the 1964 standard colorimetric observer.

These limits have been changed since the previous edition of CIE Publication 15 (CIE 1986b) where the tint limits were given as –3 < Tw < +3. Figure 4.12 shows the old and new tint limits for L* = 96 on the CIELAB a*–b* diagram, indicating the positions of the original whiteness samples (Berger-Schunn, 1977) used by the CIE subcommittee in the development of the formulae.


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The restriction that the samples be measured ‘on the same instrument at nearly the same time’ limits the application of the CIE formulae to relative measurements, i.e. they are not supposed to be used when the samples have been measured in different laboratories (even of the same organization) and only differences – rather than absolute values – are supposed to be relevant. These restrictions are not always followed in an industrial environment, where whiteness and tint tolerance limits are very often set up and used in absolute terms. In the paper industry these practices are standardized with the provision that traceable reference white standards are used for the adjustment of the UV content of the measuring spectrophotometer; in the textile industry the Ganz-Griesser calibration procedure and the application of the related formulae gained popularity (see 4.4.7).

AATCC (2005) permits using the CIE formulae also for the illuminant C / 1931 standard colorimetric observer combination using the appropriate values of xn and yn. ASTM (2005) gives the xn and yn values for illuminants C, D50 and D65 for both observers, but both standards carry the warning that conditions for illuminants C or D50 ‘are unoffi cial and should be used for in-house comparisons only’.

4.4.7 Instrument specifi c parameters; the Ganz-Griesser method

The Ganz linear whiteness formula has been in use at the former Ciba-Geigy since 1971 (Griesser, 1981) in the general form:

New limits

a*

b*

20.00

10.00

0.00

–10.00

–20.00–5 0 5 10

Old limits

Berger samples

T=0

4.12 Old and new tint limits in the CIELAB a* – b* diagram at Y = 90 (L* = 96) as defi ned by the CIE (1986b resp. 2004) whiteness and tint formulae. Also shown are the data points of the original whiteness samples (Berger-Schunn, 1977).


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W = (DY) + (Px) + (Qy) + C [4.28]

where Y, x and y are the colorimetrically determined values for the Y tristimulus value and the chromaticity coordinates; D, P, Q and C are the formula parameters whose magnitude determines the ‘whiteness bias’ of the formula. These parameters may be standard values for a given illuminant/observer combination, or instrument specifi c values determined through the Ganz-Griesser calibration procedure. The CIE whiteness formula is a specifi c case with the parameters given in Eqs. 4.24 and 4.25 for D65 and the two standard colorimetric observers, respectively.

The Ganz-Griesser calibration method is based on one proposed by Levene and Knoll (1978) to calculate the formula parameters so that the calculated whiteness values

• relate to the nominal values of any existing white scale, and• are adapted to the illumination of the measuring instrument used (Griesser,

1981).

The tint formula uses the adjustable parameters m, n and k:

TVnom(Ganz-Griesser) = mx + ny + k [4.29]

also calculated through the calibration procedure.The method uses a set of calibrated whiteness scales incorporating the hue

preferences and scaling for the Ganz whiteness formula (Eq. 4.28) and the Ganz-Griesser tint formulae (Eq. 4.29). Using these scales the industrial user or the equipment manufacturer performs the following steps (Griesser, 1994):

1 Adaptation of the illumination for the best UV/visible ratio that can be achieved with the particular instrument, with the light source using the Griesser-Gärtner UV-calibration device or numerical UV-control (see 4.3.2).

2 Calculation of the instrument-specifi c formula parameters D, P, Q and C.3 Determination of the illumination check samples for the working instrument.4 Periodic checking of the illumination conditions for the working instrument.

Should the measured values of the illumination check sample exceed the predetermined tolerance limits the process has to be re-started from Step 1.

According to Smith (1997):

The combination of the methods of adjustable fi ltration and modifying the coeffi cients of the Ganz formula thus removes the restriction that specimens to be compared must be measured on the same instrument at nearly the same time. Although neither method has been accepted as a standard, the hardware and software required for the combination has been widely used in industry for several years, generally with good results.

Gay et al. (2004) reported some improvement in the comparison of four industrial spectrophotometers using the Ganz-Griesser calibration procedure as compared


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to using the CIE formula, but there still seems to be not enough evidence in favour of the former to have justifi ed standardization.

4.4.8 Yellowness indices

Yellowness is defi ned by ASTM (2005) as ‘the attribute of color perception by which an object color is judged to depart from colorless or a preferred white toward yellow’. According to Hunter (1981): ‘Yellowness is the result of a tendency of many materials (especially organic) to absorb more light in the blue end than in the rest of the visible spectrum.’ Traditionally yellowness was defi ned by visual scales and in the 1970s and 1980s there were ‘as many as 25 different scales for rating the yellowness of oils, resins, chemicals, solvents, plastics, fi bers and so on, by visual comparison with color standards’. A typical such scale was the Gardner Scale (ASTM, 2004) consisting of 18 glass fi lters from colourless (Y = 80) to very dark reddish amber (Y = 4) as illustrated in Fig. 4.13.

Yellowness is defi ned by ASTM (2005) as

YI = 100(CXX – CZ Z)

Y [4.30]

where CX and CZ are coeffi cients defi ned for different illuminant/observer combinations as shown in Table 4.3.

Very often the CIELAB b* yellowness coordinate is used as a measure of yellowness (the higher the b* value the yellower the specimen), but this does not take the lightness dimension into consideration. Hunter (1981) described the results of a study of four tristimulus scales most frequently used for measuring yellowness and came to the conclusion that bL (Hunter b) or b* (CIELAB), as well

–200

20

40

b*

a*

60

80

100

0

12

3

45

6

78

910

11

12

13

14 15

1617

18

20 40 60

4.13 Data points of the 18 Gardner fi lters (ASTM, 2004) in the CIELAB a* – b* diagram.


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as an earlier yellowness index of ASTM, were only satisfactory for the white-to-yellow portion of the colour gamut, but did not work where the scales turn from yellow toward reddish amber. This is well illustrated in Fig. 4.14 showing the b* and the current ASTM (2005) Yellowness Index values for the original Gardner Scale, with the YI steadily increasing (as it should with increasing ‘yellowness’) while the b* value starts to decrease after scale point 11 (the lightness steadily decreases from scale point 1 through 18).

4.5 Applications in industry, cosmetics and dentistry

4.5.1 Textiles

The textile industry was the fi rst to use the concepts of whiteness and yellowness for product qualifi cation. As early as 1931 Nickerson described procedures for the grading of raw cotton, by means of disk colorimetry, which provide an adequate description of the colour of the samples. Even today cotton grading is to a large extent made by an

Table 4.3 Coeffi cients of the ASTM E 313-05 YI for different illuminants and observers

CIE standard illuminant and standard observer

Coeffi cient C, 1931 D65, 1931 C, 1964 D65, 1964

CX 1.2769 1.2985 1.2871 1.3013CZ 1.0592 1.1335 1.0781 1.1498

Source: Adapted from ASTM (2005).

0.6

0.5Optimal colours

Whiteness limits

0.4

0.3

0.2

y

4.14 Yellowness index (ASTM, 2005) and CIELAB b* coordinate as a function of the Gardner Scale value.


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instrument called the High Volume Fiber Test System or HVI (Zellweger Uster, 1999) which measures the colour of cotton in +b (the yellow coordinate of the Hunter system) and Rd (refl ectance), and only recently attempts have been made to convert these values into CIELAB coordinates (Thibodeaux et al., 2008).

Most of the advanced colour measuring systems offered for textiles have UV-calibration capabilities for the measurement of fl uorescent white specimens, using either the Gaertner-Griesser device or numerical UV control (see 4.3.2). Most of the spectrophotometers, however, have sphere geometry, only one supplier offers bi-directional (0/45) geometry with UV-calibration capabilities. Whereas the ISO-authorized laboratories use the CIE-recommended 0/45 geometry for the calibration of fl uorescent standard reference material (SRM), the transfer to industry is done through research laboratories already using sphere instruments. In spite of its wide acceptance by industry the Ganz-Griesser method has not yet been standardized. The textile industry thus uses either the CIE whiteness formula (with or without some kind of UV calibration) or the Ganz-Griesser formula (based on transfer standards provided by not ISO-authorized laboratories).

The number one unsolved technical problem in textile colorimetry today is probably that of the determination of the degree of whiteness of fl uorescent (optically brightened) textiles, and the basic reasons for this are the following:

• Visual assessments are generally performed under non-standard illumination conditions (acceptable D65 simulators for visual inspection are extremely scarce in the textile industry), and thus the results are not reliable.

• Even under the best-controlled conditions, the concept of ‘white’ or ‘whiter’ is subjective, observers within the same organization show signifi cant disagreement in ranking samples according to whiteness.

• There are very few colour-measuring refl ectance spectrophotometers in industry which are ideal for the measurement of fl uorescent samples. Those which would be adequate are very often not properly calibrated, and even in the best possible cases the calibration process is not standardized. Strictly following CIE recommendations, whiteness indices can only be used as relative values – a solution which, for many of the applications, just does not suffi ce.

4.5.2 Paper and pulp

Instrumental methods for evaluating whiteness, brightness and fl uorescence have long been used in the paper industry (Parkes, 1989). TAPPI and ISO brightness has been widely used for process control in pulp manufacturing, e.g. in the control of colour removal in paper recycling (Popson et al., 1997).

Whiteness and brightness are important quality attributes of offi ce paper, and the paper industry has long been in the forefront of research and application of whiteness evaluation. According to Smith (2008) industry standard offi ce paper has TAPPI brightness 92, but demand has increased also for brightness 96 or even


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98 in North America, and a minimum of CIE whiteness of 145 was also set. The whitest paper reported so far has CIE whiteness of 175 (Tindal, 2005).

Bristow (1994b) reported the methodology developed in fi ve authorized laboratories to establish and maintain a calibration chain for reference instruments and paper samples to achieve reasonable agreement between different instruments at the user’s site. The intention was to use the CIE whiteness equation and to avoid instrument-specifi c constants. The paper industry has thus not embraced the Ganz-Griesser method of whiteness determination so widely used in the textile fi eld.

The d/0 measuring geometry implemented in the standards and consequently in the measuring instruments for brightness and whiteness measurement of paper is unique (as opposed to the d/8 geometry used in all other fi elds). Another speciality of the paper industry is the use of illuminant C (e.g. TAPPI, 2005a, 2005b), which is no longer a CIE standard illuminant, but its SPD has been reproduced in the latest edition of the Colorimetry publication (CIE, 2004) ‘as many practical measurement instruments and calculations still use this illuminant’. A number of publications discuss the most appropriate illuminants (and sources) to be used in the paper industry (Jordan and O’Neill, 1991; Jordan, 2003; Jordan et al., 2003) as well as that of the most appropriate geometry (Singh et al., 2008), but the choice among the many standard conditions is left for the user. Bonham (2006) came to the conclusion that ‘if a single variable is used to rank the appearance of white papers, it makes little difference for a broad data set … whether one uses brightness or whiteness or whether one uses illuminant C or D65’. He warns, however, that ‘this statement should not be interpreted as saying that the choice of illuminant is irrelevant’.

4.5.3 Leather

In the leather industry they very rarely try to obtain a really white surface (it would only be possible with a strongly hiding white pigment). Very often the ‘white’ obtained is a greyish white of rather low CIELAB lightness (L* around 80–85) and very low degree of whiteness (CIE WI around 55–60) (Defoe, 1993).

4.5.4 Food

In the food industry whiteness formulae are rarely applied, probably because ‘white’ foodstuff (milk, fl our, etc.) is seldom really white due to its natural coloration. Rankin and Brewer (1998) used L and b values for ‘whiteness’ and ‘yellowness’ in quantifying the effect of fermentation on the colour of milk. Avena-Bustillos et al. (1993) used the colour difference from the ideal white (Eq. 4.14) to characterise a whitish, dried appearance (white blush) on the surface of peeled carrot pieces. Gobbi et al. (2006) used the same index (together with L*) to measure the effect of an anti-oxidant treatment and the infl uence of cultivar on the change of quality – including whiteness – of minimally processed organically grown apples during shelf-life.


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4.5.5 Cosmetics

In cosmetics the main product whose whiteness is of importance is talcum powder. The whiteness of talcum powders (CIE WI of 50 to 75) as a quality index for pharmaceutical uses was described by Soriano et al. (1998). In other applications CIELAB lightness (L*) is often used for ‘whiteness’ and b* for ‘yellowness’. An interesting combination of L* and b* is used for the characterization of skin type (Choe et al., 2006):

ITAo = (arctan(L* –50)/b*) × 180/π [4.31]

The individual typology angle is believed to be a quantitative and objective value, which can be used to classify an individual’s skin colour, but not even the category of ‘very light skin’ (ITAo > 55; L* ~ 72, b* ~10) can be considered white.

4.5.6 Dentistry

In clinical practice the evaluation of tooth whiteness is still mostly done by visual comparison using one of the well-known shade guides (Bayindir et al., 2007; Paravina, 2008), but the dentistry literature is rich in articles referring to some kind of instrumental measurement (Joiner et al., 2008). In addition to spectrophotometers and tristimulus colorimeters, digital cameras are often used (Luo et al., 2007); and there are already quite a few instruments developed specifi cally for the measurement of tooth colour, such as the Shade Vision (X-Rite), the ShadePilot (DeguDent) and the SpectroShade (MHT). There are, however, a number of problems in the colour measurement of human teeth: the reproducibility of the measurement itself is very poor due to the irregular surface of the teeth, translucency infl uences the measurement accuracy, and in vivo measurements are only possible with special tooth colorimeters or digital cameras (whose precision is still not as good as that of industrial spectrophotometers). The evaluation of whiteness is also rather controversial: human teeth cannot be considered ‘white’ due to its low luminance factor and yellowish coloration. Typical values for human teeth are in the order of L* = 55 to 75 and b* = 6 to 12 (Joiner et al., 2008), and only the extreme case of L* = 89.5, a* = 0.3 and b* = 5.7 signifi es WICIE = 48, just above the limit which may be considered white. Typical values for some of the shade guides were given by Lee et al. (2002): the ‘whitest’ specimens would get WICIE = –13 to –16.

In spite of the human tooth not being ‘white’ in the colorimetric sense there are regular references in the literature to CIE whiteness (Eq. 4.24) with absurd values of –142.29 or similar. A special ‘tooth whiteness index’ has also been suggested (Luo et al., 2005):

WIO = Y + 1075.012(xn – x) + 145.516(yn – y) [4.32]

but this also yields large negative values.


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The use of the Judd and Wyszecki (1963) colour difference type formula (Eq. 4.14) was also reported by Luo et al. (2007). In spite of the large negative values obtained for the CIE index and for WIO, and the extreme colour difference from the perfect diffuser (average values of over 48), reasonable correlation was claimed in comparing relative measurements with visual evaluations.

Details on tooth colour measurement may be found in Chapter 14.

4.6 Future trends

4.6.1 New whiteness formulae

Although the CIE whiteness and tint formulae have been widely accepted and used there is a search for ‘better’ formulae (which, according to their authors, correlate better with the results of visual evaluations or correct some of its shortcomings).

The fi rst ‘improved’ formula is a result of extensive work conducted by the Color Science Association of Japan, as reported by Uchida (1998). In fact there are two formulae, one for ‘in-base point samples’ (i.e. those whose CIE whiteness indices lie within the newly defi ned limits 40 < WCIE < 5Y–275) and another one for ‘out-base point samples’. For the former group the formula simply deducts twice the tint value squared from the CIE whiteness index:

W10 = WCIE, 10 – 2 (Tw,10)2 [4.33]

For the ‘out-base point samples’ a new formula is suggested:

W10 = Pw,10 – 2 (Tw,10)2 [4.34]

where

Pw,10 = (5Y10 – 275) – {800[0.2742 + 0.00127(100 – Y10) – x10]0.82 } + 1700[0.2762 + 0.00176(100 – Y10) – y10]

0.82 [4.35]

Uchida found much better correlation between the visual and instrumental evaluation with the new formula than with the original WICIE. Jafari and Amirshahi (2007), however, could not confi rm these results. In their investigations conducted with 113 different white fabrics they arrived at the conclusion that for both inside and outside boundary samples the CIE formula performed noticeably better than the one proposed by Uchida.

Aksoy et al. (2003a, 2003b) proposed two new whiteness formulae, one corresponding to maximum whiteness for the perfect refl ecting diffuser, the other based on the assumption that observers prefer a more bluish white (if it is not too blue).

Coppel et al. (2007) proposed a further two formulae, WNEW and WeCIE which are modifi cations of those proposed by Uchida (1998) and Aksoy et al. (2003a, 2003b):

WNEW = W0 0.5 ( (a'−a1*)2

c2

3 +

(b'−b1*)2

c2

4

) [4.36]


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where the a' and b' variables are the coordinates in a coordinate system aligned with the CIE whiteness, C3 is the distance in a' at which the function decays to half its maximum and C4 is the corresponding distance in b'. C3 was set as the maximum absolute a* in the whiteness region determined by the original CIE tint and whiteness limits. C4 and b*1 were determined by requiring WNEW = WCIE at maximum whiteness and at L*a*b* = {100,0,0}. An alternative (WeCIE) to the previous whiteness model is to keep the CIE whiteness within its region of validity and use a penalty function only for samples outside the limits (Coppel et al., 2007).

4.6.2 Open questions in the instrumental evaluation of whiteness

There are four open questions of great practical importance in the instrumental evaluation of whiteness which need to be resolved in the future (Zwinkels, 2009):

• measurement geometry,• illumination,• practical calibration of spectrophotometers,• CIE whiteness limits.

Measurement geometry

ISO standardizing laboratories use the CIE recommended 45/0 geometry for the measurement of fl uorescent samples, including transfer standards, while authorized laboratories and industry use sphere geometry (typically d/0 in the paper industry and d/8 or d/t in the textile industry). A standardized procedure is needed for how to apply this geometric correction (Hirschler and Zwinkels, 2008).

Illumination

The currently used whiteness formulae (CIE and Ganz) have been developed and tested for illuminant D65. Out of practical necessity some standards (AATCC, ASTM) permit other illuminants (C, D50), and the paper industry is advocating, in addition to D65, C and also ‘indoor daylight’ defi ned only recently by the CIE. There is, however, no practical experience in using the whiteness formulae under these light sources, so further research and standardization is needed.

Practical methods for the calibration of spectrophotometers

For fl uorescent specimens the quality (SPD) of the illumination in commercially available refl ectance spectrophotometers has a strong infl uence on the measured


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total refl ectance factor. When measuring white fl uorescent specimens the UV content of the illumination in the instrument has to be regularly checked and adjusted (the instrument has to be ‘calibrated’). There are different methods used in the paper industry (based on paper specimens calibrated by ISO standardizing laboratories) and in the textile industry (based on the Ganz-Griesser method, which has never been accepted as an international standard).

CIE whiteness limits

The CIE whiteness formula establishes tint and whiteness limits (see 4.4.6) but many commercial papers and possibly also textiles perceived as white fall outside these (Coppel et al., 2007). The formula should be modifi ed, probably by the introduction of a penalty function to handle white materials at the vicinity of the upper CIE whiteness limit.

4.7 Sources of further information and advice

Standards organizations

AATCC: American Association of Textile Chemists and ColoristsResearch Triangle Park, NC, USA, www.aatcc.org

ASTM: American Society for Testing and MaterialsWest Conshohocken, PA, USA, www.astm.org

CIE: Commission Internationale de l’ÉclairageCentral Bureau, Vienna, Austria, www.cie.co.at

ISO: International Organization for StandardizationCentral Secretariat, Geneva, Switzerland, www.iso.org

TAPPI: (Formerly) Technical Association of the Pulp and Paper IndustryNorcross, GA, USA, www.tappi.org

National standardizing/metrology institutions

BAM: Bundesanstalt für Materialforschung und -prüfungBerlin, Germany, www.bam.de

NIST: National Institute of Standards and TechnologyGaithersburg, MD, USA, www.nist.gov

NPL: National Physical LaboratoryTeddington, UK, www.npl.co.uk

NRC: National Research Council of Canada (ISO Standardizing Laboratory)Ottawa, Ontario, Canada, www.nrc-cnrc.gc.ca


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PTB: Physikalisch-Technische Bundesanstalt (ISO Standardizing Laboratory)Braunschweig, Germany, www.ptb.de

Instrument manufacturers

Autoelrepho: d/0 spectrophotometers for the paper industrySan Pedro de Alcántara, Marbella, Spain, www.autoelrepho.com

Datacolor: d/0 spectrophotometers for the paper industry, d/8 spectrophotometers for other industries

Lawrenceville, NJ, USA, www.datacolor.com

DeguDent: dental colorimetryHanau, Germany, www.degudent.com

HunterLab: 45/0 and d/8 spectrophotometersReston, VA, USA, www.hunterlab.com

Konica-Minolta: d/0 and d/8 spectrophotometersTokyo, Japan, www.konicaminolta.com

Lorentzen & Wettre: d/0 spectrophotometers for the paper industryKista, Sweden, www.lorentzen-wettre.com

MHT S.p.A.: dental colorimetryVerona, Italy, www/mht.it

Technidyne: d/0 spectrophotometers for the paper industryNew Albany, IN, USA, www.technidyne.com

X-Rite: industrial spectrophotometers, dental colour measurementGrand Rapids, MI, USA, www.xrite.com

Industrial research and ISO authorized laboratories providing whiteness standards and/or calibration services

Centre Technique du Papier: ISO authorized laboratoryGrenoble, France, www.webctp.com

FPInnovations – Paprican Division: ISO authorized laboratoryPointe Claire, Quebec, Canada, www.paprican.ca

Hohenstein InstituteHohenstein, Germany, www.hohenstein.de

KCL The Finnish Pulp & Paper Research Institute: ISO authorized laboratoryEspoo, Finland, www.kcl.fi

STFI-Packforsk AB Optical Calibration Laboratory: ISO authorized laboratoryStockholm, Sweden, www.stfi -packforsk.se


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Technidyne Corporation: ISO authorized laboratoryNew Albany, Indiana, USA, www.technidyne.com

TITV: Textilforschungsinstitut Thüringen-VogtlandGreiz, Germany, www.titv-greiz.de

4.8 References

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Aksoy B, Fleming P D, Joyce M K and Sharma A (2003a), New measures of whiteness that correlate with perceived color appearance. Available at www.wmich.edu/pci/faculty/publication/fl eming_Pb.html, downloaded on 9 July 2009.

Aksoy B, Fleming P D and Joyce M K (2003b), Whiteness evaluations on tinted and FWA added papers. Available at www.wmich.edu/pci/faculty/publication/fl eming_Pb.html, downloaded on 9 July 2009.

ASTM (2003), D 1729–89 Standard Practice for Visual Evaluation of Color Differences of Opaque Materials, West Conshohocken, PA, American Society for Testing and Materials.

ASTM (2004) D 1544–04 Standard Test Method for Color of Transparent Liquids (Gardner Color Scale), West Conshohocken, PA, American Society for Testing and Materials.

ASTM (2005), E 313–05 Standard Practice for Calculating Yellowness and Whiteness Indices from Instrumentally Measured Color Coordinates, West Conshohocken, PA, American Society for Testing and Materials.

ASTM (2007), D 985–97 Standard Test Method for Brightness of Pulp, Paper, and Paperboard (Directional Refl ectance at 457 nm), West Conshohocken, PA, American Society for Testing and Materials.

Avena-Bustillos R J, Cisneros-Zevallos L A, Krochta J M and Saltveit M E (1993), Optimization of edible coatings on minimally processed carrots using response surface methodology, Transactions of the ASAE, 36: 801–4.

Ayama M, Akatsu T, Toriumi E, Mukai K and Kanaya, S (2003), Whiteness perception under different types of fl uorescent lamps, Col Res Appl, 28: 96–102, doi: 10.1002/col.10129.

Bayindir F, Kuo S, Johnston W M and Wee A G (2007), Coverage error of three conceptually different shade guide systems to vital unrestored dentition, J Prosth Dent, 98: 175–85, doi: 10.1016/S0022–3913(07)60053–5.

Berger A (1959), Weissgradformeln und ihre praktische Bedeutung, Die Farbe, 8: 187–202.Berger-Schunn A (1977), Description of samples used and their colorimetric measurement,

Farbe, 26: 7–16.Berglund B and Stenius Å S (1977), A methodological study on the evaluation of perceptual

whiteness, Farbe, 26: 17–45.Bonham J S (2006), The appearance of ‘white’ papers, Appita Journal, 59: 446–51.Bristow A (1994a), What is ISO brightness? Tappi Journal, 77: 174–8.Bristow A (1994b), The calibration of instruments for the measurement of paper whiteness,

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Brockes A (1982) The evaluation of whiteness, CIE Journal, 1: 38–9.Choe Y B, Jang S J, Jo S J, Ahn K J and Youn J I (2006), The difference between the

constitutive and facultative skin color does not refl ect skin phototype in Asian skin, Skin Res Techn, 12: 68–72, doi: 10.1111/j.0909-725X.2006.00167.

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CIE (1986a), Colorimetric Illuminants, CIE S 001, Commission Internationale de l’Éclairage, Vienna.

CIE (1986b), Colorimetry, CIE 15.2 – 1986, second edition, Commission Internationale de l’Éclairage, Vienna.

CIE (2004), Colorimetry, CIE 15:2004, third edition, Commission Internationale de l’Éclairage, Vienna.

CIE (2009), Indoor Daylight Illuminants, CIE 184:2009, Commission Internationale de l’Éclairage, Vienna.

Coppel L, Lindberg S and Rydefalk S (2007), Whiteness assessment of paper samples at the vicinity of the upper CIE whiteness limit, Proceedings CIE 26th Session, Vol. 1, D1-10–14, International Commission on Illumination, Beijing.

Defoe G A (1993), The psychological concept of whiteness in crust leather, Farbe, 39: 169–75.

Evans R M (1949), On some aspects of white, gray, and black, J Opt Soc Am, 39: 774–9, doi:10.1364/JOSA.39.000774.

Evans R M (1964), Variables of perceived color, J Opt Soc Am, 54: 1467–9, doi:10.1364/JOSA.54.001467.

Gaertner F and Griesser R (1975) A device for measuring fl uorescent white samples with constant UV excitation, Farbe, 24: 199–207.

Ganz E (1972), Whiteness measurement, J Col Appear, 1(5):33–41.Ganz E (1976), Whiteness: photometric specifi cation and colorimetric evaluation, Appl

Opt, 15: 2039–58, doi:10.1364/AO.15.002039.Ganz E (1979a), Whiteness formulas: a selection, Appl Opt, 18: 1073–8, doi:10.1364/

AO.18.001073.Ganz E (1979b), Whiteness perception: individual differences and common trends, Appl

Opt, 18: 2963–70, doi:10.1364/AO.18.002963.Ganz E and Griesser R (1981), Whiteness: assessment of tint, Appl Opt, 20: 1395–6,

doi:10.1364/AO.20.001395.Ganz E and Pauli H K A (1995), Whiteness and tint formulas of the Commission

Internationale de l’Eclairage: approximations in the Lab color space, Appl Opt, 34: 2998–9, doi:10.1364/AO.34.002998.

Gay J K, Melo C C and Hirschler R (2004), Instrumental Whiteness Evaluation – Practical Results Of Inter-Instrument Agreement Tests, Proceedings of the AIC 2004 Color and Paints, Interim Meeting of the International Color Association. Available at http://www.aic–colour.org/congr_archivos/aic2004proc.pdf

Gobbi S, Genna A, Cerasi M, Kelderer M and Senesi E (2006), Infl uence of cultivar and dipping pre-treatment on quality of minimally processed organically grown apples, Paper presented at Joint Organic Congress, Odense, Denmark, 30–31 May 2006. Downloaded from http://orgprints.org/7557/ 14 March 2009.

Griesser R (1981), Instrumental measurement of fl uorescence and determination of whiteness: review and advances, Rev Prog Coloration, 11: 25–36.


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Griesser R (1994), Assessment of whiteness and tint of fl uorescent substrates with good interinstrument correlation, Color Res Appl, 19: 446–60, doi: 10.1002/col.5080190605.

Hardt P, Kahle V, Drenker K H, Lillotte W, Metz P, Mücke U, Müller B, Schlegel F, Schütze U, Seidl B, Tiedemann K, Mehlhorn A and Wurster P (2003), Are we being deceived by the instrumental measurement of whiteness? Melliand English, 84, E96 (534–8).

Hirschler R, Gay J K, Oliveira D F and Gomes J C (2003), Practical daylight simulators for the colour measurement of fl uorescent substrates, Proceedings CIE 25th Session, Vol. 1, D2-14–D2-17, International Commission on Illumination, San Diego.

Hirschler R and Oliveira D F (2007), Visual colour control – are we standardized? AIC 2007 Color Science for Industry, Hangzhou, 12–14 July 2007 Proc. Guanrong YE and Haisong XU (eds.) pp. 14–17.

Hirschler R and Zwinkels J (2008), ‘Use of CIE colorimetry in the pulp, paper, and textile industries’, in: Schanda J (ed.), Colorimetry: Understanding the CIE System, New York, John Wiley and Sons, 427–8.

Hunter R S (1958), Description and measurement of white surfaces, J Opt Soc Am, 48; 597–605, doi:10.1364/JOSA.48.000597.

Hunter R S (1960), New refl ectometer and its use for whiteness measurement, J Opt Soc Am, 50: 44–8, doi: 10.1364/JOSA.50.000044.

Hunter R S (1981), Conversion of visual to instrumental measurement of yellowness, JAOCS, 58: 608–12, doi:10.1007/BF02672375.

Hunter R S and Harold R W (1987), The Measurement of Appearance, New York, John Wiley & Sons, 195–208.

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Imura K, Imai K, Kawabata T and Makino M (1997), Measuring apparatus for measuring an optical property of a fl uorescent sample, United States Patent 5,636,015, United States Patent and Trademark Offi ce, Attlington.

ISO (2004), 11475 Paper and board – Determination of CIE whiteness, D65/10 (outdoor daylight), International Organization for Standardization, Geneva.

ISO (2008), 2470–2 Paper, board and pulps – Measurement of diffuse blue refl ectance factor – Part 2: Outdoor daylight conditions, International Organization for Standardization, Geneva.

ISO (2009), 2470–1 Paper, board and pulps – Measurement of diffuse blue refl ectance factor – Part 1: Indoor daylight conditions (ISO brightness), International Organization for Standardization, Geneva.

ISO/CIE (2005), 23603 Standard method of assessing the spectral quality of daylight simulators for visual appraisal and measurement of colour, Commission Internationale de l’Éclairage, Vienna.

Jafari R and Amirshahi S H (2007), A comparison of the CIE and Uchida Whiteness Formulae as predictor of average visual whiteness evaluation of textiles, Textile Research Journal, 77: 756–63, doi: 10.1177/0040517507080688.

Jafari R and Amirshahi S H (2008), Variation in the decisions of observers regarding the ordering of white samples, Color Technol, 124: 127–31, doi:10.1111/j.1478–4408.2008.00132.x.

Joiner A, Hopkinson I, Deng Y and Westland S (2008), A review of tooth colour and whiteness, J Dent, 36S: S2–S7, doi: 10.1016/j.jdent.2008.02.001.

Jordan B D (2003), Accurate colorimetry of fl uorescent paper, Proceedings CIE 25th Session, Vol. 1, International Commission on Illumination, San Diego.


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Jordan B D, Zwinkels J and McGarry P (2003), ‘The infl uence of the illuminant on the luminescent radiance factor spectrum of a reference fl uorescent paper’, in: Proceedings of TAGA, Montreal, QC, 420–34.

Jordan B D and O’Neill M A (1991), The whiteness of paper – Colorimetry and visual ranking, Tappi J, 74: 93–101.

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125

5Use of artifi cial neural networks (ANNs) in

colour measurement

M. SENTHILKUMAR, PSG College of Technology, India

Abstract: An artifi cial neural network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. ANNs are used for modelling non-linear problems and to predict the output values for given input parameters from their training values. Most of the coloration processes and the related quality assessments are non-linear in nature and hence neural networks fi nd application in colour science. The conventional approaches used for assessing and predicting the colour parameters are based on the approximations to the physical processes actually taking place and this leads to inaccuracy. It has been suggested that ANNs could be used to predict the colour parameters and recipe, controlling of dyeing process, classifi cation of dyes, etc., based on the colorant concentrations and spectral refl ectances. The formulation of colour parameters and assessments using ANNs is claimed to have better accuracy compared to any other models.

Key words: feed forward neural networks, back propagation, recipe formulation, absorbance and refl ectance value, colour difference.

5.1 Introduction

Artifi cial neural networks (ANNs) are used for modelling non-linear problems and to predict the output values for given input parameters from their training values. Most of the coloration processes and the related quality assessments are non-linear in nature and hence neural networks fi nd application in colour science. Inspection of dyeing defects in the textile dyeing process, prediction of refl ectance values from the concentration values of various colorants, colour matches on yarns dyed with different dyes, controlling of batch dyeing process of textiles, construction of refl ectance curves of cotton fi bres based on their tristimulus values, colour calibration, colour classifi cations in textiles, on-line colour measurements, prediction of dye absorbance from wavelength and concentration, predicting the concentration of fl uorescent dyes, prediction of dyeing time and CIELAB values in textile dyeing are some of the areas where ANNs have been attempted.

Since the early development of a computer colorant formulation method, computer recipe prediction has become one of the most important industrial applications of colorimetry. The determination of the amount of colorants, which are required in the application on substrate in order to produce the same colour as target, is the purpose of any colorant formulation. A relatively simple approach for


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relating concentration and refl ectance was presented by Kubelka-Munk (Nobbs, 1985). This model, which is still commonly used in computer colorant prediction, is a source of error in colorant formulation. Some theories have been introduced for colour matching of blend of precoloured fi bres, the most applicable being the Kubelka-Munk theory. This method is commonly used in colorant prediction (Amirshahi and Pailtorpe, 1994).

The Kubelka-Munk theory allows the prediction of spectral refl ectance for a mixture of components (colorants) that have been characterized by absorption K and scattering S coeffi cients. It has been shown that the Kubelka-Munk coeffi cients K and S are related to, but not equal to, the fundamental optical coeffi cients for absorption and scattering (Marjoniemi and Mantysalo, 1997a). The conventional 2-fl ux theory of Kubelka and Munk employed for computer colorant formulation reaches its limits in certain areas of coloration, suggesting the need to look at an alternative approach. More recently it has been suggested that ANNs may be able to provide alternative mappings between colorant concentrations and spectral refl ectances (Bishop et al., 1991; Westland et al., 1991) and, more generally, are able to provide transforms between colour spaces (Kang and Anderson, 1992; Tominaga, 1993). Since most of the colour application processes quality assessments are non-linear in nature, neural networks fi nd application. The formulation of colour parameters and assessments using ANNs is claimed to have better accuracy compared to any other models.

5.2 Artifi cial neural networks (ANNs): basic principles

5.2.1 History of artifi cial neural networks

An artifi cial neural network is a system based on the operation of biological neural networks, in other words, is an emulation of a biological neural system (Hinton, 1992).

Neural network simulations appear to be a recent development. However, this fi eld was established before the advent of computers, and has survived at least one major setback and several eras (Anderson, 1995).

McCulloch and Pitts (1943) developed models of neural networks based on their understanding of neurology. These models made several assumptions about how neurons worked, their networks being based on simple neurons which were considered to be binary devices with fi xed thresholds. Progress during the late 1970s and early 1980s was important to the re-emergence of interest in the neural network fi eld.

ANNs are now recognized worldwide as the most effective and appropriate artifi cial intelligence technology for prediction and pattern recognition. They offer solutions to a variety of classifi cation problems such as speech, character and signal recognition, as well as prediction and system modelling where physical processes are not well understood or are highly complex (Rao and Rao, 1996).

Use of artifi cial neural networks (ANNs) in colour measurement 127

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5.2.2 Artifi cial neural networks

An ANN is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specifi c problems. ANNs, like people, learn by example. An ANN is confi gured for a specifi c application, such as pattern recognition or data classifi cation, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons (Haykin, 1999).

One of the main problems with recipe prediction is that the application of exact colour theory is not computationally practical and an approximation to it has to be employed. It was expected that a neural network approach to recipe prediction would offer a novel and profi table new solution to this problem, since many problems in artifi cial intelligence (AI) involve systems where conventional rule-based knowledge is not perfect or the application of the pure theory is too computer intensive to be used in practical systems. In the fi eld of recipe prediction it was hoped that a suitable network system would automatically learn relationships between colorants and colour, and hence learn to predict which colorants, and at which concentrations, need to be applied to a particular substrate in order to produce a specifi ed colour.

5.3 Architecture of an artifi cial neural network

ANNs are typically composed of interconnected ‘units’ which serve as model neurons. The schematic diagram of a typical ANN is shown in Fig. 5.1.

……….

……….

Input signals

Outputs

Connectionby weights

Input layer

Hidden layer

Output layer

5.1 Schematic diagram of an artifi cial neural network.


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5.3.1 Artifi cial neuron

ANNs consist of a large number of neurons or simple processing units, also referred to as neurodes, and an artifi cial neuron mimics the characteristics of the biological neuron. Here, a set of inputs are applied, each representing an output of another neuron. Each input is multiplied by a corresponding weight, analogous to synaptic strengths and the weighted inputs are summed to determine the activation level of the neuron. The connection strengths or the weight represents the knowledge in the system. Information processing takes place through the interaction among these units (Fausett, 1994; Gurney, 1997).

5.3.2 Network layers

The commonest type of artifi cial neural network consists of three groups, or layers, of units: a layer of ‘input’ units is connected to a layer of ‘hidden’ units, which is connected to a layer of ‘output’ units (Fig. 5.1).

The activity of the input units represents the raw information that is fed into the network. Whereas, the activity of each hidden unit is determined by the activities of the input units and the weights on the connections between the input and hidden units. Similarly, the behaviour of the output units depends on the activity of the hidden units and the weights between the hidden and output units.

5.3.3 Types of network

Feed-forward networks

A general feed-forward network is illustrated in Fig. 5.1. This is a feed-forward, fully connected hierarchical network consisting of an input layer, one or more middle or hidden layers and an output layer. The internal layers are called ‘hidden’ because they only receive internal inputs and produce internal outputs. This network allows signals to travel only from input to output. There is no feedback (loops), i.e. the output of any layer does not affect that same layer. Feed-forward ANNs tend to be straightforward networks that associate inputs with outputs.

Feedback networks

Feedback networks can have signals travelling in both directions by introducing loops in the network. Networks of this type operate by allowing neighbouring neurons to adjust other nearby neurons either in a positive or negative direction. Feedback networks are changing continuously until they reach an equilibrium point, where they remain until the input changes and a new equilibrium needs to be found. Feedback architectures are also referred to as interactive or recurrent neural networks.


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5.4 Learning process

True human-like learning is beyond all artifi cial intelligence techniques, although some learning techniques have been developed which allow machines to mimic human intelligence. These techniques that allow computers to acquire information with some degree of autonomy are collectively known as machine learning.

Neural networks exhibit the ability to learn in a similar fashion to animal learning: they have a given structure (topology and learning method), they are presented with stimulus (inputs), and they adapt to that stimulus. When the neural network produces an incorrect decision the connections in the network are weakened, so it will not produce that answer again. Similarly, when the network produces a correct decision the connections in the network are strengthened, so it will become more likely to produce that answer again. Through many iterations of this process, giving the network hundreds or thousands of examples, the network will eventually learn to classify all characters it has seen. This process is called supervised learning and it is critical that the data given to the network is very carefully selected to represent the information the network is to learn.

There are generally three different ways to approach neural network learning (Pham and Liu, 1995):

1 supervised learning2 unsupervised learning3 reinforcement learning.

5.4.1 Supervised learning

Supervised learning requires the programme to give the network examples of inputs and correct output for each given input. In this way the network can compare what it has output against what it should output and it can correct itself (Fig. 5.2). Back propagation, is the most widely used method for neural network training because it is the easiest to implement and to understand and it works reasonably well for most linear and nonlinear problems.

5.4.2 Unsupervised learning

Unsupervised learning provides input but no correct output. A network using this type of learning is only given inputs and the network must organize its connections and outputs without direct feedback.

5.4.3 Reinforcement learning

Reinforcement learning is a special case of supervised learning. Instead of using a teacher to give target outputs, a reinforcement learning algorithm employs a


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critic only to evaluate the goodness of the neural network output corresponding to a given input.

5.5 Feed-forward neural network

Feed-forward neural networks are the most popular and most widely used models in many practical applications. They are known by many different names, such as ‘multilayer perceptrons’ (MLP).

A feed-forward neural network is a biologically inspired classifi cation algorithm. It consists of a number of simple neuron-like processing units, organized in layers and every unit in a layer is connected with all the units in the previous layer. These connections are not all equal, as each connection may have a different strength or weight. The weights on these connections encode the knowledge of a network. Often the units in a neural network are called nodes.

Data enters at the input and passes through the network, layer by layer, until it arrives at the output as shown in Fig. 5.3. The input layer consists of just the inputs to the network. Then follows a hidden layer, which consists of any number of neurons, or hidden units placed in parallel. Each neuron performs a weighted summation of the inputs (eqn 5.1), which then passes a transfer/activation function, also called the neuron function. During normal operation there is no feedback between layers.

vk = Σn

j=1 wkjxj [5.1]

5.6 Training of an artifi cial neural network using back

propagation algorithm

In order to train a neural network to perform some task, the weights of each unit have to be adjusted in such a way that the error between the desired output and the actual output is reduced. This process requires that the neural network

ANNInputsignal

– + Targetvalue

Supervisedlearning

Weightadjustment Error

value

5.2 Supervised learning of ANN.


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compute the error derivative of the weights. The back propagation algorithm is the most widely used method for determining the error derivative (Werbos, 1974; Rumelhart et al., 1986).

In most learning networks the difference between the actual output and the desired output is calculated. This raw error is then transformed by the error function to match a particular network architecture. The artifi cial neuron’s error is then typically propagated into the learning function of another processing element. This error term is sometimes called the current error. The current error is typically propagated backwards to a previous layer. Yet, this back-propagated value can be either the current error scaled in some manner (derivative of the transfer function), or some other desired output depending on the network type. Normally, this back-propagated value, after being scaled by the learning function, is multiplied against each of the incoming connection weights to modify them before the next learning cycle.

5.6.1 Training phases

The training of a multilayered feed-forward neural network is accomplished by using a back-propagation algorithm that involves two phases (Werbos, 1974; Rumelhart et al., 1986).

yk

vk

Σ

wk1 ……….

Input signals

wk2 wk3 wkn

Activationfunction

……….×1 ×2 ×3 ×n

Synapticweights

Summingfunction

Output

5.3 Mathematical representation of a feed-forward neural network.


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Forward phase: During this phase the free parameters of the network are fi xed, and the input signal is propagated through the network layer by layer. The forward phase fi nishes with the computation of an error signal (Ei)

Ei = di – oi [5.2]

wheredi is the desired output and oi is the actual output produced by the network in response to the input xi.

Backward phase: During this second phase, the error signal Ei is propagated through the network in the backward direction. It is during this phase that adjustments are applied to the free parameters of the network so as to minimize the error Ei in a statistical sense.

5.6.2 Transfer function

The behaviour of an ANN depends on both the weights and the input–output function (transfer/activation function) that is specifi ed for the units. This function typically falls into one of three categories:

1 linear2 threshold3 logistic or sigmoid.

For linear units, the output activity is proportional to the total weighted output, while for threshold units, the output is set at one of two levels, depending on whether the total input is greater than or less than some threshold value.

The sigmoid (logistic) function has a rich history of application as a cumulative distribution function in demographic studies and in modelling growth function (Balakrishnan, 1992). The particular functional form that is often used for the logistic or sigmoid activation function (eqn 5.3) is

oi = f (neti) = — 1—— [5.3]

1+e−neti

which yields oi C– [0,1]. This is shown in Fig. 5.4.The sigmoid function is so important and popular because for sigmoid units,

the output varies continuously but not linearly as the input changes. Sigmoid units bear a greater resemblance to real neurons than do linear or threshold units.

5.7 Application of artifi cial neural networks to

colour measurement

The application of ANNs to colour measurement and colour match prediction was fi rst demonstrated by Westland et al. (1991), who concluded that this technique


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could be applied to colorimetric systems with complex behaviour. There are many types of ANN, one of the simplest and most successful of which has been described using colour problem solving; this is the multilayer perceptron (MLP). This network can be used to predict the refl ectance values in the range of 400nm to 700nm (visible region) from the concentration values of various colorants; for example concentration values of cyan, magenta, yellow and black (CMYK) in a paper printer. Linear output functions are not used and would not lead to satisfactory results in these networks because the functional composition of several linear functions is itself a linear function.

Before the networks can be used to solve a given task it must fi rst be trained using known pairs of input and output vectors. For example, to train the system to convert a given refl ectance curve to CMYK values, the output vectors would be a selection of CMYK values and the input vectors the measured refl ectance curves of the samples printed with these values. Pairs of input and output vectors are presented to the input and output layers of the networks respectively. The weights between the neurons are adjusted so as to reduce the error between the calculated output of the last output layer and the desired output. Mathematical techniques such as ‘back propagation of the generalized delta rule’ are used for systematic optimization of the weights to minimize error (Bishop et al., 1991). The neural network with fuzzy logic technique was applied to control the batch dyeing operation of textiles by Smith and Lu (1993).

McGregor et al. (1996) used the artifi cial neural network as a non-linear tool to predict colour matches on polyester yarns dyed using three different dyestuffs. Since neural networks perform a non-linear mapping, they should be able to predict more accurately the dye recipe of a three-component mixture than a linear model. In order to judge the effectiveness of the neural network, the results obtained were compared to results obtained using the traditional, linear algebra, method. The input to both methods were the K/S values obtained from the spectral refl ectance data taken from each yarn sample using a spectrophotometer. The structure of the

5.4 The sigmoid activation function.

f (neti)

neti0

1

1+ e–neti

1oi = f (neti) =


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neural network was set up to have thirty-one input nodes for the K/S spectral values, fi fteen middle-layer nodes, and three output nodes. The three output nodes yield the predicted concentrations of fi ve each of the dyes for a particular spectral input. The neural network was set up to be trained using back propagation with momentum and an adjustable learning rate with the middle and output layers being set up to use a log-sigmoid function neuron. All of the neurons were fully connected. The traditional method uses individual, single dyeings as the input data to ‘train’ or establish the model and the dye recipe is expressed as a linear combination of the single-dyes. The problem with this method is that the dyes do not combine linearly. On the other hand, a neural network can model the non-linear interactions but requires a larger ‘training’ set to establish the model. The ‘training’ set for the neural network includes both the combination-dye data and single-dye data.

In order to accurately compare the two methods of obtaining a colour match, an absolute error was calculated for each sample under each method.

Fernández et al. (1995) developed a multilayer neural network in the modelling and prediction of colour of red wines. While a Hopfi eld network was used for colour image segmentation by Campadelli et al. (1997). Faes (1998) has shown how neural networks based on colorimetric data obtained by refl ection measurement of the dyed material can be used as a reliable predictor of visual assessment. Gross et al. (1999) have used ANN to retrieve chlorophyll pigments in the near-surface of oceans from ocean colour measurements. This bio-optical inversion was established by analysing concomitant sunlight spectral refl ectances over the ocean surface and pigment concentration. An artifi cial neural network with Bayesian regularization training technique was developed to predict colour appearance (from colorimetric attributes to colour-appearance attributes) by Xin et al. (2000). Xin et al. (2002) have developed a multilayer perceptron feed-forward artifi cial neural network model with Bayesian regularization technique for training to predict the colour appearance from colorimetric values.

Schettini (2001) has developed a method for approximating the colour appearance model CIECAM97s by means of feed-forward neural networks trained with the error back-propagation algorithm. The ANN technique was used to reconstruct the refl ectance curves of cotton fi bres based on their tristimulus values by Dupont (2002), while Alman and Ningfang (2002) have done an experiment with CRT colour calibration to explain the methods to avoid an over-trained condition in the ANN model development. A multilayered feed-forward neural network was developed to predict the refl ectance spectra of yarn from the roving refl ectance spectra using a back-propagation training algorithm by Thevenet et al. (2002).

Xu (2003) has developed two models using neural networks, fuzzy clustering and fuzzy logic to colour classifi cations in textiles. The fi rst application was the identifi cation of colour patterns on a printed fabric. The self-organizing map and fuzzy clustering algorithm was used to automatically separate coloured patterns for independent evaluations. The second application was the colour classifi cation of cotton fi bres using fuzzy logic. He concluded that the fuzzy logic appears to


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be effective in dealing with ambiguity and uncertainty in cotton colour grading. Tandukar (2007) has developed a model to predict a recipe for reproducing the desired colour by using only three primary subtractive colours (cyan, magenta, and yellow), then be able to correct it dynamically for a reproduction close to the target. Blanco et al. (2007) have developed a multilayer feed-forward neural network with three hidden neurons to predict the colour of the fi nal food products based on in-line measurements and they concluded that the predicted CIELAB values have an acceptable correlation with off-line colour parameters.

In order to use the neural network to predict any type of value, it has to be trained with known values for the input and output parameters. The set of input and output parameters are known as the training pattern (Senthilkumar, 2007). So the colour of any material can be predicted using ANN by training the network with set of training patterns. The accuracy of the network depends on the number of training patterns used for training and the association of input parameters with the output parameters.

5.8 Recipe prediction

The conventional approaches to recipe formulation depend on equations resulting from the analytical treatment of the relationship between refl ected light and colorant concentration. In general these equations are only approximations to the physical processes actually taking place and this leads to inaccuracies in recipe formulation due to a variety of reasons, for example:

• Failure of the prediction theory to deal adequately with the non-linear relationship between dye applied from the dyebath and refl ectance.

• Interaction between dyes, leading to dye uptake behaviour which is different from the uptake of the dyes when applied individually.

• Scaling-up problems, when recipes based on laboratory-scale dyeing ranges are applied in bulk machinery, and deliberate or accidental variations in the dyeing process compared with the calibration dyeings.

• Variations in physical structure of the substrate, compared with the calibration ranges.

• Mistakes in processing.

The failure to match the target may be detected either in a check dyeing carried out in the laboratory or in the dye lot processed under bulk conditions. In either case it is necessary to have some method of correcting the defective recipe to bring the colour closer to target. Similar problems arise in many sciences, and the need to tackle them has led to the application of the branch of artifi cial intelligence known as the artifi cial neural network (ANN) theory. In a conventional computer program, the computer carries out a set of specifi c instructions to complete a given task. Unlike a conventional computer program a neural network is designed to adapt and acquire knowledge over time in order to complete a certain task. ANN


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programs represent a different approach to problem solving which has strong parallels to the way the human brain is though to operate.

Westland et al. (1991) have applied neural networks to recipe prediction with some degree of success. The network consisted of three input units (CIELAB values), 24 hidden units arranged in two layers of 8 and 16 units, and three output units (three dye concentrations). The training input consisted of a series of two-dye recipe samples and single-dye recipe samples and the output vector was the measured CIELAB values of the samples. The training consisted of 55,000 epochs using 30 dyed samples, consisting of 12 single-dye and 18 binary mixtures. Predictions were then made to match these 30 samples plus another 21 samples not included in the training set. In the 54 predictions, 33 of the targets were binary mixtures and for these 78.8% gave errors less than 0.8 CMC(2:1) unit. Predictions of the remaining 21 targets dyed with single dyes were not so accurate, leading to an overall accuracy for the complete data set of 60% with errors less than one CMC(2:1) unit.

They claim that the use of ANN offers several potential advantages over the conventional recipe prediction approach using absorption coeffi cients:

• It is not necessary to prepare a special database of dyes in order to use the ANN method. The network can be trained on actual production samples.

• The network can continue to learn after the initial training period, since future production samples can be presented to the system and this knowledge incorporated into the network weights. This gives the network the potential to adapt to changes in factors such as water supply, change of substrate, change of dye strength and so forth.

• The network may be able to learn the behaviour of colorants for which the mathematical descriptions are complex. For example, fl uorescent dyes and metallic paint systems are currently diffi cult to treat using the standard Kubelka-Munk theory.

The fi rst paper to provide details about the application of ANN in colour parameters prediction was one by Jasper et al. (1993). They took three commercial dyes, namely Cibacron yellow G-E, Cibacron brilliant red 4G-E, and Cibacron blue TR-E, and mixed them in combinations of 0.00, 0.05, 0.10 and 0.15g/L. This gave them 62 dye solutions. Each dye mixture contained 50 g/L of NaCl, and all measurements were made at 27 °C. The full visible absorbance spectra (380–780 nm) of all the 62 dye solutions were measured. Three different methods were used to predict dye concentrations from the absorbances – Beer’s law, modifi ed Beer’s law and a feed-forward neural network. Beer’s law proved to be very poor as a predictive tool and gave an average error of 51%. The modifi ed Beer’s law gave good results when all the three components were present and gave an overall error of 9%. The neural network, in which the entire absorbance spectra were given as input, gave an error of only 2.6%. In a comment on this paper, Vangheluwe et al. (1994) pointed out that this error of 2.6% was the error of the training set. Indeed,


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the researchers had not kept any test set for validating the trained network. In a reply to the comments of Vangheluwe et al. the authors reported carrying out some cross-validation.

A neuro-fuzzy technique was developed to colour recipe prediction by Mizutani et al. (1995). This relates surface spectral refl ectance of a target colour to several colorant proportions.

Marjoniemi and Mantysalo (1997a) have reported an experiment in which an Adaptive Neuro Fuzzy Inference System (ANFIS) was used to predict dye absorbance from wavelength and concentration. Two data sets were generated for this purpose, each of two dye mixtures. In data set 1, the concentration of yellow dye was kept at 50 mg/L, while in set 2 it was kept at 100 mg/L. The concentration of red dye was varied from 0 to 900 mg/L in both data sets 1 and 2, giving eighteen levels for each of them. Nineteen levels of wavelengths were recorded in the range 400–580 nm. The training data thus had 342 examples for each of the data sets. In later work, the same authors (Marjoniemi and Mantysalo, 1997b) have tried to predict the concentration of the red dye from wavelength and absorbance values.

Bezzera and Hawkyard (2000) have reported an experiment in which they tried out four different feed forward neural networks for predicting the concentration of fl uorescent dyes from the total spectral radiance factor (SRF), SRF curves (SRF-C), XYZ, and L*a*b*, respectively. Some 283 samples were used for training and 28 for testing. They reported that the method used for this study is simple to apply, and requires only a representative database of fl uorescent and non-fl uorescent colorants, a commercial spectrophotometer adequately calibrated to measure SRF values and the software to create and train an adequate network able to learn the relationship between the colour parameters and dye concentrations. From the network types studied to predict dye concentrations, the one using SRF values as the input colour parameters proved to be the most appropriate way (an average error of 3.92%) to relate a fl uorescent coloured sample with the dyes and the concentrations required to reproduce it. When the SRF-C network was used to predict dye concentrations for a sample, the only way to fi nd out the difference in colour between that and the standard sample was to apply predicted dye concentrations to a substrate and then measure its SRF. Predicting SRF curves from concentrations also produced good results.

Westland et al. (2001) have developed number of multilayer feed-forward neural networks and those networks were trained, using the back-propagation with momentum learning algorithm, to perform the mapping from colour to refl ectance for a set of known paint samples (training set). Each of the networks used a single hidden layer of processing units and the number of units in that layer was varied between 3 and 15. Each network was trained using the full training set and alternative training sets (each being sub-sets of the full training set) in order that the performance of the networks could be assessed for different training set sizes. Furthermore, two types of networks were employed. The fi rst


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type of network was a standard network that was fully connected. The second type was a hand-crafted partially connected system that was specially designed for the task of colour prediction. This second type of network was inspired by a consideration of the K-M model. The performance of all of the trained networks was assessed by computing CMC(2:1) colour differences, under D65/1964 conditions, between the predicted refl ectances and the measured refl ectances for the test set.

Senthilkumar and Selvakumar (2006) have reported an experiment in which they predicted the dyeing time of cotton fabrics dyed with high exhaustion reactive dyes using a feed-forward neural network. For the study they selected three reactive HE dyes, namely Procion Brilliant Red HE 3B, Procion Green HE 4BD and Procion Brilliant Red HE 7B, and two different types of cotton fabric. The following training patterns were used to train the network.

Input parameters

• K/S values of the undyed fabrics• %Total dye fi xed on the fabric for a selected %shade• Percentage shades• NaCl concentrations• Na2CO3 concentration• K/S values of the dyed and washed samples.

Output parameters

• Time for primary exhaustion• Time for dye fi xation.

The network was trained using the input and output parameters and the training process of the neural network developed was started with 10,000 preliminary cycles to optimize the ANN prediction accuracy. These cycles were carried out with different network structures and different learning parameter values and the network training errors were obtained. The best structure is the one that gives the lowest training error and it was found to be 6/9/9/9/2 in the present study (the structure used in this study is given in Fig. 5.5). The training of the network was further continued in order to reduce the training error and the average training error of 1.0% was obtained when 1,00,000 cycles were used. Senthilkumar and Selvakumar concluded that the neural network developed could be used to determine the primary exhaustion time and fi xation time for producing the expected depth of shade with high exhaustion reactive dye on cotton fabric.

Senthilkumar (2007) developed a model using the ANN technique to predict the CIELAB value of vinyl sulphone dyed cotton fabrics. He used three dyes and two different types of cotton fabrics to develop a model using the following training patterns.


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Input parameters

• Whiteness Index of the fabric to be dyed• %Total dye fi xed on the fabric• Percentage shades• Salt concentrations• Alkali concentration• Dyeing time.

Output parameters

• L*• a*• b*

The above training patterns were used to develop a network and the best structure that gives the lowest training error was 7/10/10/10/3. A training error of 2.0% was obtained when 85 000 cycles were used. The network was also tested with six different sets of input parameters and the error percentage was calculated (see Table 5.1). Senthilkumar also concluded that the neural network model developed could be used to optimize the dyeing parameters for producing the required depth of shade for any type of vinyl sulphone dyes.

In order to formulate the dyeing recipe or optimize the process parameters, ANN can be used by selecting suitable input parameters (should be associated with output parameters) and training the network with suffi cient cycles. There are limitations in the use of neural networks for colour recipe prediction.

• An adequate number of samples must be prepared and presented for the network to learn the relationship between input and output parameters

• An increase in number of dyes/colours will increase the network topology.

K/S values of thefabrics to be dyed

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Input layer Hidden layers Output layer

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5.5 Schematic diagram of the ANN used for prediction of dyeing time.


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5.9 Evaluation of the ANN method

In order to evaluate the prediction accuracy of ANNs, a set of input parameters has to be fi xed to evaluate output parameter and the error percentage has to be calculated. Senthilkumar and Selvakumar (2006) have selected a twill fabric with unknown specifi cations and a dye namely Procion Brilliant Red HE 7B to test the prediction accuracy of the neural network developed. The K/S value of the fabric and the %total dye uptake of the fabric to be dyed with above dye were found out. Testing samples were produced with the %shades beyond the range used for training the network and the K/S values of these samples were found out. Followed by this, the input parameters were fed in to the neural network and corresponding output parameters, namely primary exhaustion time and fi xation time, were obtained. These predicted timings, along with the actual timings, are given in Table 5.1. It can be observed that the mean absolute error with respect to prediction is around 1%.

Senthilkumar (2007) has tested the model developed by calculating the two input parameters, namely %total dye fi xed on the fabric and the whiteness index of undyed sample, experimentally. The rest of the input parameters were fi xed arbitrarily and dyeing was carried out at various %shades using unknown vinyl sulphone dye. The above-prepared samples are considered as the control and using those input parameters, the output parameters (L* a* b*) were predicted. The actual and predicted values are given in Table 5.2. The colour difference (ΔE*) also calculated between actual and predicted values (Table 5.2).

5.10 Case studies

The developed ANN model was implemented in a textile based dyeing industry to predict the dyeing time. When goods are taken for dyeing, once the recipe and the conditions of dyeing for a given machine is fi xed, the only parameter which needs attention to achieve the expected depth of shade is ‘the duration of the process’.

Table 5.1 Actual and predicted dyeing time

% shade Primary exhaustion Fixation time (min) time (min)

Actual Predicted % error Actual Predicted % error

0.25 15 14.90 0.67 15 14.91 0.601.00 15 15.21 1.40 15 15.20 1.331.75 15 15.05 0.33 15 15.06 0.402.50 15 15.33 2.20 15 15.32 2.133.25 15 15.34 2.27 15 15.33 2.224.00 15 14.96 0.27 15 14.95 0.33

Mean absolute error 1.19 1.17


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The following input parameters were fi xed for known reactive HE dye, fabric and target with six different depths of colour.

• K/S value of the undyed fabric and target• % total dye fi xed was calculated• % shade• NaCl and Na2CO3 concentration based on percent shade.

All the above parameters were fed into the network, the dyeing time was predicted and samples were produced. The spectral refl ectance curves of the samples produced with predicted timings and the target are given in Fig. 5.6. As these curves show insignifi cant difference with respect to various %shades, it can be said that the network developed can be implemented to predict the dyeing time for achieving expected depth of shade.

Dyed textile materials are generally accepted when the ΔE* values are between 0 and 1.5 and the ΔL* values are between –0.7 and 0.4. If the ΔE* value is above 1.5 the colour difference between sample and control is very high and it is to be rejected. If the ΔL* values are less than –0.7 the samples are darker in shade and if greater than 0.4 the samples are lighter in shade compared to that of the control sample (Shah and Gandhi, 1990). The L*, a* and b* values of the sample to be dyed were predicted before dyeing by feeding input parameters to a developed ANN. Based on the CIELAB values predicted, the input parameters were adjusted and the fabric was taken for dyeing, then compared with the target. The spectral refl ectance curve of those samples is shown in Fig. 5.7 for various %shades.

5.11 Future trends

The work reported in this chapter suggests that neural network techniques can be useful for solving recipe prediction problems. It has been shown that the Kubelka-Munk model has been approximated to develop the system. There is no

Table 5.2 Actual and predicted L* a* and b* values and ΔE* value

% L* a* b*

Shade Actual Predicted % Actual Predicted % Actual Predicted % ΔE* error error error

0.25 66.03 65.02 1.53 44.34 45.27 2.09 13.53 13.93 2.96 1.430.75 59.44 60.32 1.48 50.88 49.93 1.87 16.53 16.06 2.84 1.381.25 56.54 55.76 1.38 54.16 55.09 1.72 18.22 18.73 2.79 1.311.75 54.53 55.09 1.03 56.59 55.89 1.24 19.59 19.19 2.04 0.982.25 53.37 52.92 0.84 58.05 58.65 1.03 20.46 20.83 1.81 0.832.75 52.34 52.66 0.61 59.40 58.98 0.71 21.30 21.03 1.27 0.60

Mean absolute error 1.15 1.44 2.29


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reason to believe that similar neural networks cannot learn the relationship between colorant concentrations and colour coordinates for real coloration systems. In order for this approach to become viable it will be necessary to extend the training data to include a greater number of colorants. It remains to be seen how many training recipes will be necessary to enable the network to make accurate predictions when a larger number of colorants are used. It will also be necessary to be able to include information regarding illuminants other than D65; it is possible that this may be accomplished by entering colorimetric data for more than one illuminant or by using refl ectance values for the target data during training.

The ANN can also be used in colour science, coupled with fuzzy logic so the accuracy of prediction will be enhanced. In order to develop a universal solution

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for recipe prediction, a network has to be developed by training the network with the training pattern generated with all combination of dyes, dye depth, dyeing conditions, dyeing additives concentration, dyeing machines, substrates, etc.


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5.13 References

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Anderson J A (1995), Introduction to Neural Networks, Cambridge, MA: MIT Press.Balakrishnan N (1992), Handbook of the Logistic Distribution, New York: Marcel Dekker.Bezzera C M and Hawkyard C J (2000), ‘Computer match prediction for fl uorescent dyes

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during food extrusion: Sensitivity and correlation with product colour’, Food Res International, 40(9), 1129–1139. doi:10.1016/j.foodres.2007.06.008

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Fausett L (1994), Fundamentals of Neural Networks, Englewood Cliffs, NJ: Prentice-Hall.

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267, 145–151.Jasper W J, Kovacs E T and Berkstresser G A (1993), ‘Using neural networks to predict dye

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147

6Camera-based colour measurement

F. MARTÍNEZ-VERDÚ , E. CHORRO and E. PERALES , University of Alicante, Spain, M. VILASECA and J . PUJOL, Technical University of Catalonia, Spain

Abstract: For several decades, imaging sensors (CCD and CMOS) have been extensively used in many types of imaging capture devices (cameras and scanners). This fi rst stage in any digital imaging workfl ow is very important in order to control the exact colour reproduction of images in subsequent applications (astronomy, television, cinema, printing, machine vision, mobiles, etc). However, there are many parameters (spectral sensitivities, white balance, dynamic range, etc) which can negatively infl uence accurate control of the colour reproduction of digital imaging devices. Nevertheless, if all these parameters are controlled, it is possible to transform a conventional digital imaging capture device into a versatile tele-colorimeter or even tele-spectrocolorimeter. In this chapter, the fundamentals and challenges of camera-based colour measurement will be explained, including several aspects of special interest, such as the control of raw RGB colour space, and the similarities and differences between spectral and colorimetric characterization and calibration. Finally, future trends with clear industrial applications will be described, including case studies focused on the spatial-chromatic dithering of texture images (textiles, ceramic tiles, natural stones, etc), and the pseudo-visualization of non-visible images from multi-spectral imaging capture.

Key words: imaging sensor, digital imaging workfl ow, spectral sensitivity, white balance, exposure level, dynamic range, raw RGB colour space, colour gamut, calibration vs. characterization, luminance adaptation, multi-spectral imaging, spatial-chromatic dithering in texture images, pseudo-visualization of non-visible images

6.1 Introduction

With the invention in 1969 of the charge-coupled device (CCD) (Holst 1998) by Willard S. Boyle and George E. Smith – both awarded the Nobel Prize in Physics in 2009 – and its combination with photo-detection sensors, it was possible to expand many industrial applications, and even introduce new ones. In digital colour imaging, this invention was considered a signifi cant milestone, together with the invention in 1993 of the CMOS or active pixel sensor by E. Fossum (Lee 2005; Nakamura 2006; Ohta 2008). Although both imaging sensors present similarities and differences, it is probable that the specifi c advantages and drawbacks of each in respect to each particular application will condition future trends in their application fi elds (see Fig. 6.1).

In digital colour imaging these imaging sensors are essential elements for any digital imaging workfl ow (Jacobson et al. 2000; Saxby 2002; Sharma 2003; Peres


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2007; Trussell and Vhrel 2008; Langford and Bilissi 2008) focused on digital cinema, television, print media, telecommunications, and other applications (Lukac and Plataniotis 2007; Koschan and Abidi 2008), since they substitute the human eye position and role in any capture of a scene. Thus, for any digital imaging workfl ow we can defi ne the main elements as follows:

• Scene: composed of several natural and/or artifi cial objects and light sources, arranged in different positions and orientations towards each other.

• Input devices: imaging capture devices (cameras or scanners) based on CCD/CMOS sensors, with an optical system which enables the light coming from the scene to be focused on the photodetector plane (Johnson 2003), as in the human retina.

• Output devices: display and printing devices, primarily used for encoding and visualizing the information registered in the input devices, for editing and transference to different application fi elds.

Although there are many challenges in coordinating the colour encoding languages (namely, colour spaces) associated with each colour device in a digital imaging workfl ow, that is, colour management (Rodney 2005; Nelson 2007; Padova and Mason 2007), this chapter will only focus on the main issues related to the extensive use of imaging sensors for colour measurement, specifi cally the conversion of conventional digital cameras and scanners to colour devices, with features and performance similar to commercial colour instruments: tele-spectrocolorimeters, spectrophotometers and colorimeters (see Chapter 8, and Part II of this book) (Shevell 2003; Xin 2006). Therefore, given the general scheme of a digital imaging workfl ow, the following sections will mainly be focused on the interaction of the features of any scene with input devices, with special emphasis on the spectral

6.1 Evolution of CCD and CMOS application fi elds over the last fi fty years.

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Camera-based colour measurement 149

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and colorimetric information that these kinds of devices can provide before and after the corresponding digital colour encoding (Holm et al. 2002; Sharma 2003; Westland and Ripamonti 2004; Ramanath et al. 2005; Trussell and Vhrel 2008).

6.2 Principles of camera-based colour measurement

Imaging capture devices basically consist of an optoelectronic sensor, or analogical photosensor, and a device which converts analogical signals into a digital code. The sensor is a matrix of small cells modelled as a spatially uniform array. Each cell is a microscopic photosensitive element which has the ability to produce electrical impulses of different intensity regarding the incident light. This device, in spite of its photosensitivity, distinguishes light intensity variations, but not colours. To distinguish between colours it is necessary to use optical fi lters in order to separate red, green, or blue light into selected pixel sensors. Conceptually, the scene is codifi ed by three spectral bands (red, green and blue), as in the human visual system.

To analyse colour reproduction in imaging capture devices, one should bear in mind that the RGB colour space is dependent on the device, worsening the colour control rendering of these technologies. Due to the great variety of colour spaces, it is necessary to establish transforms which enable the RGB values associated with the imaging capture device to be converted into CIE-XYZ tristimulus values. Consequently, the device metamerism should be taken into account, that is, a colour stimulus with the same tristimulus values in the CIE-XYZ colour space can be encoded differently in the RGB colour space, and vice versa.

Imaging capture devices are additive colour reproduction systems and therefore, in order to obtain an exact reproduction, their spectral sensitivities, or equivalently, their colour matching functions, should be an exact linear combination of the CIE-1931 XYZ colour matching functions. However, only a small group of input devices fulfi ls this important condition. Thus, from an initial and general point of view, all input devices are not colorimetric devices, so it is necessary to perform a preliminary colorimetric characterization process in order to convert them into colour measuring instruments.

The main components of any imaging capture colour device consist of (see Fig. 6.2):

• An optical fi lter set and an optical system. An objective lens which excludes ultraviolet radiation (τUV, UV), an additional fi lter which cuts infra-red radiation (τIR, IR), and fi nally, the RGB colour fi lters (τR, τG, τB) to spectrally separate the photoelectrical information into three colour channels.

• An optoelectronic semiconductor device (CCD or CMOS) as a photodetector plane with a specifi c spectral sensitivity s(λ).

Mathematically, and given that the device performance is considered linear, the device responses (R, G, B) of any scene can be expressed as:


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R ∝ Σ ρ(λ)·S(λ)·sR(λ)·Δλ

G ∝ Σ ρ(λ)·S(λ)·sG(λ)·Δλ [6.1]

B ∝ Σ ρ(λ)·S(λ)·sB(λ)·Δλ

where S(λ) is the spectral power distribution of the light source, ρ(λ) is the object spectral refl ectance, and sR(λ) = τUV(λ)τIR(λ)·τR(λ)·s(λ), sG(λ) = τUV(λ)τIR(λ)·τG(λ)·s(λ), sB(λ) = τUV(λ)τIR(λ)·τB(λ)·s(λ) are the spectral sensitivities of the RGB channels respectively.

As stated above, the colorimetric characterization of a digital colour camera implies knowing which CIE-XYZ tristimulus values are associated with the object from the RGB values encoded by the camera. This characterization enables the use of devices such as tele-colorimeters. However, a complete characterization model for cameras entails spatial, spectral and colorimetric characterization, which can be summarized as follows.

• Spatial characterization (de Lasarte et al. 2007), which involves the application of a linear correction algorithm to compensate for the spatial non-uniformity of the camera sensor response when the object plane is homogeneously illuminated.

• Spectral characterization (Martínez-Verdú et al. 2002; ISO 17321-1:2006), which involves obtaining the RGB pseudo-colour matching functions of the camera, that is, the responses for each camera channel depending on the wavelength of the object being analysed.

• Colorimetric characterization (Martínez-Verdú et al. 2003; ISO 17321-2WD:2009), which implies obtaining the colorimetric profi le, that is, the

6.2 Process of imaging capture of a scene: lighting, object and imaging device with zoom lens, colour separation (fi lters) and imaging sensor.


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matrix which enables the CIE-XYZ tristimulus values to be calculated from the RGB digital levels.

A different method for colorimetrically characterizing a digital colour camera, without the need to apply the previous spectral characterization, is to use a direct transformation, which enables either the CIE-XYZ or CIELAB values to be obtained from the RGB digital signals (Hong et al. 2001; Sharma 2003). In this type of characterization, the tristimulus values of a colour stimulus are generally computed from a polynomial combination of its RGB signals. The polynomial modelling is obtained by taking into account the previous measurement of a training set with representative colour patches, from which both groups of values (RGB signals and CIE-XYZ or CIELAB values) are a-priori known.

The Color Checker charts are two of the most widely used training sets for achieving this purpose (see Colour Plate VIII between pages 42 and 43). The classic Color Checker is a colour chart with 24 patches used for calibrating and evaluating colour reproduction systems. On the other hand, the digital Color Checker Semi Gloss (SG) is specifi cally designed to meet the needs of digital photography.

Besides colorimetric characterization, it is also quite important to know the limits of the imaging capture device in order to obtain a perfect characterization, such as:

• Dynamic range: the limits of luminance values that a camera can capture, always smaller than the human dynamic range (from 10–3 cd/m2 under night illumination to 105 cd/m2 under daylight illumination).

• Spectral exposure level H(λ): the total amount of monochromatic light allowed to fall on the image sensor, which mainly depends on the spectral radiance of the colour stimuli Le(λ), the exposure time t, and the f-number N.

• White balance: the global adjustment of colour signals (typically red, green, and blue primary colours) to correctly render neutral colours, taking into account the chromaticity (correlated colour temperature) of the illuminant. Essentially, this procedure aims to replicate the human perception property known as colour constancy, using some basic algorithms from chromatic adaptation mechanisms in the human visual system.

6.3 Procedures of camera-based colour measurement

As mentioned earlier, the main purpose of using a digital colour camera as a colorimetric instrument is to transform the RGB signals corresponding to its device-dependent colour space, known in Digital Photography as raw RGB colour space (Steinmueller and Gubbins 2005; Andrews et al. 2006), to CIE-XYZ or CIELAB values. In order to achieve this, it is essential to set the camera settings which can alter the raw RGB colour space. In general, it is suffi cient to set the exposure time, f-number, white balance and other gain and offset values specifi ed


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by the manufacturer. The selected confi guration may have an infl uence on the performance limits of the input device, and may only be useful for certain specifi c scenes or input conditions (colour and intensity level of illumination, position of the input device with regard to the scene, etc). Taking these initial instructions into account, the procedures for the colorimetric characterization of cameras will now be described.

As stated above, the fi rst option in order to obtain a complete characterization model for cameras entails spatial, spectral and colorimetric characterization. The fi rst step is spatial characterization. If a digital camera is to be used as a colour measuring instrument, it must be borne in mind that they are not perfect detectors. There are various noise sources inherent to their performance that alter the digital levels corresponding to each pixel, distort the real image acquired in an unknown manner, and diminish the radiometric accuracy, the image quality and the resolution (Janesick 2001). Due to their origin and fundamental characteristics, frame averaging removes all noise sources except the spatial non-uniformity of the digital camera’s response (Healey and Kondepudy 1994), which must be corrected if the imaging system is to be used as a measuring instrument with high spatial resolution. The most commonly used technique for this is known as fl at-fi eld correction, and is based on calibrating the RGB channels of the detector by means of two images: a dark image and a uniform fi eld or fl at-fi eld image. The spatially corrected image is the result of the linear combination of these two images with the image to correct (de Lasarte et al. 2007; Bellia et al. 2003; Berns 2001).

The second step is spectral characterization, which consists in measuring the RGB signals provided by the digital colour camera when it is irradiated with a set of monochromatic stimuli with different radiances and wavelengths. Then, with a monochromator based experimental set-up (see Fig. 6.3), it is possible to obtain the spectral sensitivities of any digital imaging capture device (Martínez-Verdú et al. 2002) without knowing the spectral functions of their optical elements (zoom lens, colour fi lters, imaging sensor, etc). From these known spectral data,

Spectracolorimeter

CCDcamera

PC unit

Light diffuser

Monochromator

Halogenlamp

6.3 Monochromator based experimental set-up for measuring the spectral sensitivities of a digital imaging capture device.


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it is very easy to fi nd a basic colorimetric profi le (3×3 matrix) connecting the raw RGB and XYZ colour spaces, and to test the linear correlation between experimental and predicted RGB digital values under different lighting conditions for any scene.

The third step is colorimetric characterization. For this purpose, several options are possible, including a colorimetric profi le based on spectral sensitivities (Martínez-Verdú et al. 2003), or direct polynomial modelling (Hong et al. 2001; Sharma 2003).

Since the second method is the fastest and easiest to implement, it will be explained in more detail. In polynomial characterization or modelling, the colorimetric tristimulus values of a colour stimulus, such as the CIELAB coordinates, are calculated from the polynomial combination of their corresponding RGB signals. For instance, if a third order polynomial is used, the functions which relate both sets of values, that is, L* = f1(R, G, B, RG, …, RGB), a* = f2(R, G, B, RG, …, RGB), b* = f3(R, G, B, RG, …, RGB), can be expressed as follows:

L*1 a*

1 b*1

D = ... ... ... [6.2]

L*n a*

n b*n

1 R1 G1 B1 R1G1 R1B1 ... R31 G3

1 B31

V = ... ... ... ... ... ... ... ... ... ... [6.3]

1 Rn Gn Bn RnGn RnBn ... R3n G3

n B3n

D = M · V [6.4]

M = (mij)3×20 = ((Vt · V)−1Vt · D)t [6.5]

where M is the matrix with the transformation polynomial coeffi cients characterizing the camera, {Rn, Gn, Bn} are the digital levels of the training colour patches measured by the camera and {L*n, a*n, b*n} are the CIELAB values of the training set measured by a spectrophotometer or other conventional colour instrument. Finally, transformation of the RGB values is achieved using the following equation:

1

R

L*

= (mij)3×20 ·

G

a*

B

[6.6]

b* RG

...

B3 20×1


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It should be borne in mind that the coeffi cients which appear in the above equations are calculated by measuring the training set, such as the colour patches from the Color Checker charts.

6.4 Strengths and weaknesses

Nowadays colour characterization of input devices is a signifi cant scientifi c-technological challenge for various reasons. For example, despite the mission of the International Standardization Organisation (ISO), and the fact that some ISO standards do exist which focus on digital photography (ISO 12232:2008; ISO 14524:2009; ISO 22028:2006; ISO 15739:2003; etc), methods for determining transforms of raw RGB values from input devices to scene-referred image data (namely, absolute CIE-XYZ tristimulus values) do not constitute an active item of ISO work. Although there has been signifi cant progress with the publication of ISO 17321-1:2006 (Graphic technology and photography – Colour characterization of digital still cameras (DSCs) – Part 1: Stimuli, metrology and test procedures), consensus among ISO experts in the search for the best method for determining these RGB-XYZ/L*a*b* transforms remains diffi cult to achieve (ISO 17321-2 working draft), as it depends on camera spectral sensitivities, the spectral radiances of the colours to be analysed, and various tradeoffs (e.g. colorimetric accuracy vs. noise amplifi cation). However, regardless of the continuing confusion concerning digital camera colour characterization, even including terms and defi nitions (camera colour analysis gamut, etc), the use of digital cameras to determine scene colorimetry persists.

Bearing these antecedents in mind, the main purpose of the following subsections is to provide some advice and ideas, which have proven valid and effective, for successfully carrying out the conversion of any input device as a colour measuring instrument.

6.4.1 Similarities and differences among camera characterization models

Before describing the main similarities and differences between the input device characterization models described above, it is important to distinguish between characterization and calibration. The characterization procedure for input devices aims to select and apply a colour modelling or algorithm (with many parameters to be calculated) for use in the experimental set-up and data collection. In contrast, the purpose of the calibration procedure is to repeat the characterization procedure for obtaining new colour modelling parameters resulting from changes to the initial confi guration (illumination change, f-number, exposure time, white balance, gain, offset, etc). This issue is particularly critical for colorimetric characterization, because the colorimetric calibration parameter set should not be applied to other camera and scene conditions, due to the risk of losing all initial performance in your input device converted into a colorimeter. Therefore, our advice is very clear: one scene and camera confi guration, one characterization procedure.


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Turning to the complete characterization model, it is important to remember here that both spatial and colorimetric characterization procedures are always necessary. However, spectral characterization enables a relationship (3×3 matrix) between RGB and XYZ values to be obtained with ease. This is the basis for any colorimetric profi le, such as that necessary for colour management workfl ows. Nevertheless, colorimetric characterization based on polynomial modelling usually performs well with test colours, which are very different to the training set. Therefore, although this increases computational cost and complexity, we recommend using several training sets with reduced chromatic ranges for each polynomial modelling (maximum number, three) until CIE colour space is completely covered. Obviously, these problems and challenges are not recent, so in the following subsections, we will focus on current alternative approaches for solving or minimizing them.

6.4.2 Concept of luminance adaptation model for cameras covering a high dynamic range

Digital colour cameras have a limited dynamic range, where their response to exposure is practically linear. Generally, a change to the camera settings, such as exposure time, gain or offset, enables their dynamic range to adapt to the actual range of radiances. Nevertheless, it is very probable that digital responses for some of the image pixels are not located within the linear response zone of the imaging system due to the large radiance differences of the different objects imaged. Information loss is observed in highly illuminated areas, where all light variations are mapped onto the same value and thus become saturated, and in dimly illuminated areas, where information is overridden by sensor-noise. One way of overcoming this limitation is to use models for increasing the dynamic range of systems (Battiato et al. 2003). These techniques are mainly based on capturing sequences of images of the same scene taken under different exposure conditions (Reinhard et al. 2006; Mantiuk et al. 2007), and then merging them into a single image of increased dynamic range. For instance, luminance adaptation models (Martínez-Verdú et al. 2003; Pujol et al. 2006) transform the digital levels for each channel at a certain exposure time to virtual digital levels at a reference exposure time common to all pixels by means of a linear transformation. This enables all imaged samples to be mapped onto the same exposure time, and therefore, they are comparable and useful for metrological purposes, such as colorimetric measurements.

6.4.3 Introduction to multi-spectral capture systems: reconstruction of spectral refl ectances from multi-channel colour values

On the other hand, an alternative approach to improve the low accuracy of RGB digital colour cameras when measuring colour and which avoids the metamerism present in these systems is the use of multispectral capture systems (Hardeberg


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1999; Hill 2002; de Lasarte et al. 2006). A multispectral capture system also consists of a digital camera but it records the scene through various acquisition channels with different spectral transmittance (see colour Plate IX as an example). Although this overall defi nition includes any device capable of sampling in frequency any incoming light, the ‘multispectral’ term is mostly applied to those systems that use more than the three conventional colour channels, but with fewer spectral bands than traditional spectrophotometers. In literature, it is common to fi nd the classifi cation given in Table 6.1 of image capturing systems depending on the number of channels or spectral bands used (van der Meer and de Jong 2001; Imai et al. 2003).

A multispectral system is capable of providing instant information on the refl ectance spectrum of a colour sample, and therefore, on its colour, from the corresponding digital response level for each pixel. This is achieved by means of spectral reconstruction mathematical algorithms such as principal component analysis (PCA) (Hardeberg 1999; Hardeburg et al. 2002; Tzeng and Berns 2005), the Moore-Penrose pseudoinverse (Vhrel et al. 1994; Vilaseca et al. 2004, 2006; Hardeberg 1999) and other higher-order polynomial fi ttings (Herzog et al. 1999; Hong et al. 2001; Cheung et al. 2005). Furthermore, in order to use multispectral systems for spectral reconstructions, it is essential to carry out prior training of the system so that digital responses can be related to the spectra that originated them, through the use of a reference colour patch training set with known spectra (de Lasarte et al. 2008a), similar to the process carried out for colorimetric characterization with polynomial modelling.

The most conventional confi guration of a multispectral capture system consists of a monochrome camera and a set of narrowband fi lters covering the whole visible range of the spectrum, which may be interference (de Lasarte et al. 2006; Vilaseca 2008) or tunable (Hardeberg et al. 2002; Tominaga and Tanaka 2008). However, other confi gurations comprising a colour camera and broadband absorption fi lters (Imai and Berns 2000; Vilaseca 2008) or newer ones with a monochrome camera and a set of narrowband spectral emitters, such as light-emitting diodes (Wenger et al. 2003; Yamamoto and Miyake 2007) are also possible. Figure 6.4 shows examples of transmittances associated with the acquisition channels of multispectral systems with different confi gurations.

Due to the possibility offered by multispectral imaging systems of accurately estimating the refl ectance spectrum at each pixel and, from this, the corresponding

Table 6.1 Image capturing systems classifi cation by their number of channels

Number of channels Description

1 Monochromatic3 RGB or trichromatic4 to 9 Multispectral10 to 100 HyperspectralMore than 100 Ultraspectral


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CIE-XYZ tristimulus values of a colour sample, the application fi elds of multispectral imaging systems have increased considerably in recent years. Some of these applications include accurate colour reproduction systems (Boosman and Hill 2004), the restoration and conservation of paintings and other artworks (Schmitt et al. 2005; Tominaga and Tanaka 2008), and the acquisition of high dynamic range images (Haneishi 2005). Furthermore, multispectral technology has also been applied to reconstruct and visualize spectra outside the visible range, such as infrared (Vilaseca et al. 2005, 2006).

6.4.4 Similarities and differences between spectral and colorimetric imaging based colour measurement

Both the camera-based colorimetric and multispectral systems use imaging sensors. However, while there are three channels involved in a conventional

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6.4 Relative spectral transmittances of the interference fi lters used in a 7-channel confi guration (a) and RGB relative spectral sensitivities and transmittance of an additional blue fi lter used in a 3/6-channel confi guration (b), of two visible multispectral systems.

(a)

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colorimetric imaging confi guration (RGB), the number of spectral bands used in multispectral systems is higher. Thus, colour assessment accuracy is much higher with multispectral systems, and the metamerism often associated with digital trichromatic cameras is avoided (de Lasarte 2008a, 2008b). Nevertheless, some authors have studied changes in multispectral system accuracy by increasing the number of channels, and have shown that from a certain number of spectral bands onwards (often less than 10 (Vhrel et al. 1994; Hardeberg 1999)), neither the accuracy of colour measurement nor the accuracy of spectral reconstruction improves signifi cantly, which can be explained by the spectral properties of most surfaces, which are relatively smooth.

On the other hand, drawbacks of multispectral systems include their slowness in scene acquisition through the various acquisition channels, and the cost of current implementations, which basically depends on the number and type of fi lters used: interference or broadband absorption fi lters may be cheap, although liquid crystal tunable fi lters are expensive.

Compared to standard spectrophotometers and spectroradiometers, which use diffraction gratings in order to accurately sample the spectral data on the photosensors, colorimetric and multispectral camera-based systems do not have equally good levels of colour and spectral accuracy. However, they offer high spatial resolution and are relatively low cost; therefore, they may be acceptable for many industrial applications and thus have much potential.

6.5 Case studies

Despite the foregoing, nowadays it is possible, on the international market, to fi nd several industrial machine vision solutions which combine all the strengths of camera-based non-contact colour measurement. One example is the DigiEye™. Here we have a compact machine vision system which is mainly composed of a (fi xed) lighting system which provides homogeneous cover of the target/scene, a calibrated digital camera, versatile, powerful digital image processing software for replicating the cortical performance of human visual perception, and a calibrated monitor and printer. Other industrial machine vision solutions prefer a free lighting system for adjusting to on-line processes, or free positioning of the calibrated digital camera for measuring the 3-D optical behaviour of materials (Tominaga and Tanaka 2008), both with normal or special effects.

In the following pages, two interesting, open challenges will be described: the fundamentals of spatio-chromatic dithering in imaging capture, and the pseudo-visualization of non-visible images from multi-spectral imaging capture.

6.5.1 Spatial-chromatic dithering in imaging capture: effects on visual appearance and measurement of textures

In many cases, a trichromatic input device transforms the spectral information of a scene into three digital signals (RGB), compressing all initial spectral radiance


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data into three digital values. Consequently, the camera is also performing dithering when digitizing a scene. But depending on the distance between the camera and the target being digitized, relevant spatial information on colour can be dithered or not. This process is the same as when we use a tele-colorimeter or tele-spectrocolorimeter and its measuring spot (displayed in the ocular lens) covers spatially non-homogeneous samples (natural and artifi cial textures). Thus, both RGB pixels and measuring spots provide a spatially averaged colour signal inside the projected area in the scene. How then can we control the spatial issue before capturing any image and take into account the posterior effects on the fi nal visual appearance?

From a geometrical point of view, a digital camera’s sensor plane can be modelled as a spatially uniform array of small sensors. Camera sensor spatial resolution is determined by the sensor size p' and the distance x' between the object and sensor, according to equation 6.7:

u = 2arctan( p' ) [6.7] —

2x'

If the target/object is near enough to the camera sensor, the visual angle is smaller than the smallest detail of the object and hence the image is properly sampled. The borderline situation occurs when the visual angle subtended by the sensor exactly matches the visual angle subtended by a pixel, because this implies having identical spatial resolutions for both sensor and object. In this situation, the target will be at the appropriate distance x0 (see Fig. 6.5), and the area covered by the visual angle u is p0. Therefore, the distance x0 is fi xed by the sensor size and the focal length of the optical system and x0 is the greatest distance from the image that does not cause spatial dithering in the sensor array.

If the target is at a greater distance (x), which is k times the initial distance x0, the visual angle u takes k2 pixels of the original image. Given that, our aim is to simulate the appearance of an image placed at a greater viewing distance than the original one, without having to recapture the image at the new distance, averaging k2 pixels of the original image to calculate each pixel of the simulated

6.5 Schema of the spatio-chromatic dithering in any imaging capture process.

u

x'x0

x = kx0

u

Sen

sor

area

p

Far distance

Cell (spot, vein, etc)

Image distance

Calibrated active pixelp'


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image. The digital level values of the image could be used directly as averaged quantities, but this would be colorimetrically unsound, because these values do not predict colour appearance. Therefore, it was proposed to average CIE-XYZ tristimulus values, because these represent psychophysical encoding in colour science, necessary for calculating forward CIE L*a*b* values. Then, if the digital level values of k2 pixels are fi tted by {Xi,j Yi,j Zi,j}i=1,. .k; j=1,…k, the new tristimulus values will be:

X = 1 — k2

Σ k

i,j=1X(i,j), Y = 1 —

k2 Σ

k

i,j=1Y(i,j), Z = 1 —

k2 Σ

k

i,j=1Z(i,j) [6.8]

where all tristimulus values have the same weight, regardless of position.From this theoretical approach, combining geometrical optics and colour

science (Chorro et al. 2007), it is possible to predict different grades of spatio-chromatic dithering of captured images by changing the value k, i.e. the viewing distance, or the size of the minimum spatial detail of target, in order to compare their effects through contrasting colour differences between images. However, as with other alternative versions of this approach such as spatial-CIELAB (Sharma 2003) or iCAM, it is necessary to fi nd the direct relationship with true visual judgements by planning well-managed psychophysical experiments with human observers.

6.5.2 Multispectral capture system covering VIS and NIR radiation for textile applications

The colour control process is a very important issue in the textile industry. In the past, these adjustments were often carried out visually, although obtaining results was time consuming and they were only valid for each specifi c evaluation. For these reasons, colour measurement devices are now used extensively in this fi eld, although some drawbacks of this technology have still not been overcome. Examples of these are the colour measurement of very small areas, needed in patterned or printed fabrics, and the slowness usually associated with conventional colorimeters, which can only measure one integrated large uniform area at a time. Therefore, the use of colour digital cameras, or even multispectral systems, with high spatial resolution and a lower cost could be very useful and would speed up the control process, enabling faster production.

Some preliminary attempts at using multispectral systems in the textile industry have already been carried out. For instance, a seven channel multispectral system has been used to perform colour measurements and spectral refl ectance reconstructions of textile samples in the visible range of the electromagnetic spectrum (de Lasarte 2009). Specifi cally, the system was used to analyse 56 textile samples grouped in 28 pairs, made specifi cally to test the applicability of colour difference formulae to textile samples, particularly the CIELAB colour difference formula. Hence, these samples had quite similar spectra between pairs, making it


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diffi cult to distinguish between them (see Fig. 6.6). In this study, the multispectral imaging system developed was shown to be able to detect slight differences, therefore breaking the device metamerism, both in colour and in refl ectance spectra between real samples, and making it useful for applications that require colour discrimination.

Another multispectral system, in this case consisting of fi ve spectral bands located at the near-infrared range of the electromagnetic spectrum (800–1000nm), was used to develop a pseudo-colour visualization system for the visual discrimination of textile samples (Vilaseca et al. 2005). This system used different

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6.6 Refl ectance spectra of (a) the pair 18 with a ΔE*ab = 0.750 between them, and (b) the pair 27 with a ΔE*ab = 3.018.


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colour space representations which associated the camera responses with the colour channels of a calibrated monitor. Samples with the same colour or appearance in the visible region, which are therefore indistinguishable to the human eye, can have different refl ectance spectra in other parts of the electromagnetic spectrum, specifi cally in the near-infrared. Therefore, these samples can be discriminated by using the extra information provided by the fi ve additional multispectral images in this region. Colour Plate X shows some blue and garnet textile samples, the appearance of which is exactly the same since they have equal visible spectra, but which nevertheless have different spectral features in the near-infrared. Using the pseudo-colour visualization system developed, the samples could be clearly differentiated using the fi ve multispectral images provided by the system, and also with the pseudo-coloured images obtained as a combination of them, using different colour space representations. The methodology developed could have potential applications in the control of garment counterfeiting by adding special components with specifi c spectral characteristics to the conventional dyes utilized in the textile industry.

6.6 Future trends

During the last decade, some advances in new imaging sensors and colour architectures (Super CCD, CMOS Foveon, HAD, etc) promise new frontiers in the applicability of camera-based non-contact colour measurement. On other hand, new challenges associated with new applications for special optic materials (luminescent, gonio, sparkle, glitter, etc) with varied measurement geometries will increase in importance and relevance.

The use of camera-based systems for colour and spectral measurements is at an experimental stage, and therefore much remains to be done in order to extend their use commercially. In this context, only a few attempts have been made so far, such as the example of imaging spectrographs, which change an area scan camera to a spectral line imaging device that produces full continuous spectral information in each line pixel with high spectral resolution. Imaging colorimeters which capture complex luminance and colour distributions instantaneously constitute a further example.

On the other hand, new trends and research directions in camera-based systems for spectral measurements include the use of new light sources (for instance LEDs), the development of spectral reproduction systems rather than colour matching devices, the combination of spectral and colour information, and the compression, management and visualization of multispectral data. Finally, and of no less importance, as we mentioned previously, it is to be hoped that experts will reach a consensus in order to recommend and publish new ISO standards related to versatile and complete input device characterization procedures. Given the main goal sought as regards camera-based non contact colour measurement, these new standards should not rule out certain issues related to the repeatability and


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reproducibility of performance tests between calibrated hyper/multi-spectral and trichromatic cameras.

6.7 Conclusions

Camera-based systems for non-contact colour measuring are less accurate than conventional colorimetric systems such as spectrophotometers and spectroradiometers. However, they have higher spatial resolution and are generally lower cost. This makes them suitable for several industrial applications where colour accuracy is not the main issue. Furthermore, these types of systems can easily be integrated into industrial production lines, or even into scientifi c and multimedia applications.


http://en.wikipedia.org/wiki/Charge-coupled_devicehttp://en.wikipedia.org/wiki/Active_pixel_sensorhttp://www.123di.com/dpr.phphttp://www.cambridgeincolour.com/tutorials.htmhttp://www.color.org/info_profi les2.xalter#digitalphotographyhttp://en.wikipedia.org/wiki/Hyperspectral_imaginghttp://www.specim.fi /products/spetral-imaging-products/imaging-spectrographs.htmlhttp://www.directindustry.com/prod/instrument-systems/spectroradiometer-57082-

377325.html

6.9 References

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Battiato S, Castorina A and Mancuso M (2003), ‘High dynamic range imaging for digital still camera: an overview’, J Elec Imag, 12(3), 459–469.

Bellia L, Cesarano A, Minichiello F, Sibilio S and Spada G (2003), ‘Calibration procedures of a CCD camera for photometric measurements’, 20th Instrumentation and Measurement Technology Conference, 2003 (IMTC’03), Proc IEEE 1, 89–93.

Berns R S (2001), ‘The Science of Digitizing Paintings for Color-Accurate Image Archives: A review’, J Imag Sci Technol, 45(14), 305–325.

Boosmann T and Hill B (2004), ‘Estimation of Control Values for a 6-primary Display Considering Different Observers’, Proc IS&T Second European Conference on Colour in Graphics, Imaging and Vision (Aachen, Germany), 242.

Cheung V, Westland S, Li C, Hardeberg J and Connah D (2005), ‘Characterization of trichromatic color cameras by using a new multispectral imaging technique’, J Opt Soc Am A, 22(7), 1231–1240.

Chorro E, Perales E, Fez D de, Luque M J and Martínez-Verdú F M (2007) ‘Application of the S-CIELAB color model to processed and calibrated images with a colorimetric dithering method’, Opt Express, 15(12), 7810–7817.

Haneishi H (2005), ‘Image acquisition technique for high dynamic range scene using multiband camera’, Proc 10th Congress of the International Colour Association (AIC Colour 05) 1, 183–187.


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Hardeberg J Y (1999), Acquisition and reproduction of color images: colorimetric and multispectral approaches, PhD Thesis, École Nationale Supérieur des Télécommunications, Paris.

Hardeberg J Y, Schmitt F and Brettel H (2002), ‘Multispectral color image capture using a liquid crystal tunable fi lter’, Opt Eng 40(10), 2532–2548.

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Imai F H, Wyble D R, Berns R S and Tzeng D (2003), ‘A feasibility study of spectral color reproduction’, J Imag Sci Technol, 47, 543–553.

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GretagMacbeth˜ ColorChecker color rendition chart

Digital ColorChecker© SG

1

2

3

4

5

6

7

8

9

10

A

gretagmacbethB C D E F G H I J

10 2 3 4 5 6

mm

K L M N

Plate VIII Classic (a) and digital (b) ColorChecker charts.

(a)

(b)

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1.0

B1

BLUE SAMPLES

B2B3B4B5B6B7B8

0.8

0.6

0.4

0.2

0.0400 500

Wavelength (nm)

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ctra

l ref

lect

ance

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1.0

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GARNET SAMPLES

G2G3G4G5G6G7

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Plate X Visible and near-infrared spectral refl ectances of blue and garnet textile samples with the same appearance. Since they have different spectral features in the near-infrared, they can be differentiated in the fi ve multispectral images (Im_F1-Im_F5) provided by the system. The pseudo-coloured images obtained as a combination of the fi ve independent images using different colour space representations are also shown.

1.0

Spectralreconstruction

Sample Filters

Illuminant

CCD camera

Sample #10.8

0.6

0.4

0.2

0.0800 850

Wavelength (nm)

Reconstructedspectral reflectance

900 950 1000

Spe

ctra

l ref

lect

ance

Plate IX Multispectral imaging workfl ow.

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167

7Colour shade sorting

M. L. GULRAJANI, India Institute of Technology, India

Abstract: Shade sorting of the acceptable fabric lots is necessitated because the fabric swatches from these dyed lots, when placed together for stitching, show visual colour difference. Even though one can sort coloured samples visually, it is not in practice due to poor observer-to-observer correlation and inconsistent repeatability. Instrumental shade sorting is preferred and is considered more reliable. The fi rst instrumental shade sorting method was evolved by Simon in 1961 and over the past 40 years many sorting methods have been developed that include 555 shade sorting, Clemson Colour Clustering, K-means clustering and adaptive clustering. A brief description of these methods of shade sorting has been covered in this chapter.

Key words: 555 shade sorting, Clemson Colour Clustering, K-means clustering, sequencing, tapering, adaptive clustering, hierarchical clustering.

7.1 Introduction

Shade sorting is a process of segregating acceptable groups of dyed lots of fabric into subsets where the minute colour difference between the lots is not perceivable to the naked eye. This is necessitated because the fabric swatches from these dyed lots, when placed together for stitching, show visual colour difference. In view of this, more critical colour tolerances are required and the acceptable coloured batches need to be divided into subgroups with visually acceptable colour differences.

Even though one can sort coloured samples visually, it is not in practice due to poor, observer-to-observer correlation. Instrumental shade sorting is preferred and considered more reliable. According to Li et al. (1998) four equally important prerequisites for effi cient instrumental shade sorting are:

• Effective data gathering: this includes both the colour measurement and data treatment process.

• Uniform colour space generated colorimetrically for allocating the coloured textile samples in coordinated form.

• Proper determination of the tolerance limit for dividing colour space and population of coloured textile samples.

• An effi cient method for the separation of the colour space and population of colorimetric textile sample.

The fi rst instrumental shade sorting method was evolved by Simon in 1961. However, due to non-availability of proper instruments and software its use in


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a slightly modifi ed form started during the mid-1960s (Aspland and Howard, 1997). During the last forty years many improved methods have been developed and are available in the form of software that can be integrated with the various other colour measurement and colour communication modules.

Shade sorting methods are being mostly used by the garment industry. Even though they source the material that has been ‘passed’ by an instrumental method of ‘fail-pass’ as per the criteria set by them, still they fi nd that some colour differences become visually perceivable on stitching of the garment from the ‘passed’ lots of the same shade.

The perceptible difference in the colour of the ‘passed’ lots occurs due to various reasons, such as:

• The refl ection of more light in one direction than in another resulting in lustre and appearance differences between two samples.

• Material might have been dyed with chemically different dyes that match in one source but appear different under another source of light.

• The ‘passed’ samples may be lying at the two opposite ends of the periphery of the pass-fail ellipsoid.

7.2 (555) Fixed-grid shade sorting system

In the 555 shade shorting system as proposed in 1961, the colours were sorted on three-dimensional grids with lightness, chroma and hue as the coordinates in the UCC colour difference system (Hirschler and Zwinkels, 2007). The CIE chromacity coordinates, x, y, along with Y were calculated for each sample and used to quantify the colours for sorting. These were later replaced by more visually uniform CIELAB coordinates, L*, a* and b* or their equivalents in CMC and CIEDE2000 colour difference equations.

In this system ‘standard’ shade is assigned the number ‘5’ for all the three colour axes, i.e. L*, a* and b* or L*, C*, H*. Therefore, the ‘standard’ shade is termed as ‘555’ and located at the centre of the 555 box. It is also known as the sort code of the sample. The fi rst digit represents lightness or darkness, the second digit is for the redness-greenness (a*) or for saturation (C*), the third digit denotes yellowness-blueness (b*) or the hue angle (h) (represented by hue, H*, a function of hue angle) as the case may be.

The 555 box is created around the standard with samples having ΔL*, Δa* and Δb* or ΔL*, ΔC* and ΔH* in the pre-defi ned tolerance limit. However, a sample may have a negative value of ΔL*, or Δa* or Δb* or ΔC* or ΔH* that is at the periphery of the box and another sample with a plus value of ΔL*, or Δa* or Δb* or ΔC* or ΔH* at the other end of the box, so the tolerance limit in the box becomes twice the pre-defi ned tolerance limit (Aspland and Howard, 1997). As shown in Fig. 7.1 the two samples having maximum acceptable negative and positive values of ΔL* will lie at points A and B, the colour difference in the case

Colour shade sorting 169

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of these two samples and standard at the centre will be acceptable. However, the colour difference between these two samples will be more than the limits set in the pass-fail criterion. So a garment stitched with parts from these samples will have perceptible colour difference.

Having quantifi ed the standard and having put it in the centre box, other boxes are created around it whose orientation is parallel to the three-dimensional axes of the opponent-colour scale system shown in Fig. 7.2. There is no dead space and no

h0

A BL*

C*

7.1 Tolerance ellipsoid and position of two accepted samples A and B.

919

915

911

511

111551

151

191

155

255

455355

555545

535525

515

565

556 557558 559

575 585 595199

999

655

755

855

955

7.2 Arrangement of ‘boxes’ of 555 shade sorting system.


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overlapping portion between the boxes. The 555 box is in the centre and other boxes have sort codes between 111 and 999. The dimensions of the three axis of the boxes are set as per the preset tolerance limits for the ΔL*, Δa*, Δb* or ΔL*, ΔC* and ΔH*.

A guideline for setting up tolerance limits for individual colours in terms of ΔL*, Δa*, Δb* is shown in Table 7.1 (Li et al., 1998). These specifi cations have been worked out on the basis of the experience of colourists handling the shade sorting process and provide the specifi cation for all three dimensions for the different regions of the colour space. However, one may use one’s own shade sorting criteria for adjusting the tolerance limit according to his or her experience. These specifi cations are applicable to the Cartesian (L*, a*, b*) coordinate. These are not entirely suitable for both the polar (L*, C*, h) coordinate and CMC micro-space concept because both of these systems consider colour in terms of lightness, chroma and hue rather than lightness, redness-greenness and yellowness-blueness. In a subsequent study, Li et al. (1999) carried out regression analysis of twenty sets of coloured cotton knitted fabrics including eight main shade groups (red, orange, yellow, green, blue, violet, brown and black) dyed with reactive dyes, to determine the optimum colour tolerance level for instrumental shade sorting. On the basis of this study these investigators have concluded that although the performance of CIE94 and CMC were quite similar, the CMC equation is recommended for generation of micro-spaces for shade sorting processes since it gives a better overall performance over a whole set of batches.

Table 7.1 Colour specifi cation for apparel: block sizes for 555 shade sorting in Cartesian (L* a* b*) coordinate

Colour Lightness ΔL* Δa* Δb* range allowance allowance allowance

Grey 70–90 0.8 0.4 0.6Grey/Black 20–70 0.8 0.5 0.6Black 0–20 0.8 0.5 0.6Blue 50 0.6 0.2 0.5Blue 20–50 0.6 0.2 0.5Navy 10–20 0.6 0.3 0.6Brown 70–90 0.8 0.4 0.6Brown 40–70 0.8 0.5 0.6Brown 10–40 0.8 0.5 0.6Green 40 1.0 0.4 0.5Orange 60 0.4 0.35 0.6Orange 30–60 0.5 0.4 0.6Red 60 0.35 0.35 0.35Red 30–60 0.5 0.5 0.5Red 10–30 0.8 0.5 0.5Violet 10–80 0.7 0.5 1.2Yellow 80 0.5 0.4 1.2Yellow 10–80 0.7 0.5 1.2


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In 1986, Aspland and Jarvis introduced a graphical method for determining initial colour tolerance limits based on the CMC (l:c) formula. A tolerance limit of 0.5 CMC (2:1) or below as detailed in AATCC Test Method 173-1991 is generally used in creating boxes (Aspland and Jarvis, 1992). It also means that the maximum permissible colorimetric difference between coloured fabrics within a box will remain ≤1.0 ΔECMC units on either side (periphery) of the box. This also applies to the standard 555 box. Moreover, the coloured fabrics in a given box may ‘match’ each other but they may be several ΔE units away from the standard. Thus a dyed sample having shade number (sort code) 853 will be lighter (+L*) than the standard by 3 box units, will have the same redness-greenness (a*) as that of the standard and will be 2 box units bluer (b*) than the standard. However, in the case of the L*, C*, H* system the sample will be lighter (+L*), with the same degree of saturation as that of the standard (C*) and lower hue angle than that of the standard (h).

The advantages of 555 shade sorting are its arithmetical simplicity and a well-defi ned relationship between the shade sorting blocks and the standard. These characteristics permit allocation of a new sample of a different production lot in the previously created relevant block.

A simple method of calculating the sort code (shade number) is described by Sule (1997). According to this method, group limits (i.e. tolerance limits) have to be specifi ed for a given set of samples to be sorted. These are the dimensions of the three axes of the boxes as per the preset tolerance limits for the ΔL*, Δa*, Δb* or ΔL*, ΔC*, ΔH* systems and they have to be lower than the limits set as the pass–fail criterion set for passing or rejecting the supplied samples.

The group limits are denoted by the letter G as indicated below.

GL – Group limit for lightnessGa – Group limit for redness/greennessGb – Group limit for yellowness/bluenessGC – Group limit for chromaGH – Group limit for hue.

The next step is the calculation of the sort ratios (SR) by the following procedure:

SR (L) = ΔL/GL, SR (a) = Δa/Ga, SR (b) = Δb/GbSR(C) = ΔC/GC, SR (H) = ΔH/GH

The sort code or the shade number of any given sample can then be calculated as illustrated by an example below.

If for a given sample the colour difference as calculated by the CIELAB (1976) equation is ΔL* = 1.4; Δa* = –0.9; Δb* = 1.4 and the group limits are set as: GL* = 0.5; Ga* = 0.4; Gb* = 0.4, then the sort ratio will be,


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SR (L) = ΔL*/GL* = 1.4/0.5 = 2.8

SR (a) = Δa*/Ga* = – 0.9/0.4 = – 2.25

SR (b) = Δb*/Gb* = 1.4/0.4 = 3.5

The sort code for this sample will be obtained by adding 5.5 to the Sort Ratios and considering only the integer of the sum without rounding up.

SC (L) = (2.8 + 5.5) = 8.3 = 8

SC (a) = (– 2.25 + 5.5) = 3.25 = 3

SC (b) = (3.5 + 5.5) = 9.0 = 9

Thus the sort code of the given sample will be = 839.

For the 555 sorting method sort codes of all the supplied samples are calculated and the samples having the same sort code are grouped together and stitched so as to avoid any perceptible shade differences in the garment or any other made-up product.

The 555 shade sorting method is also referred to as the fi xed-grid method that relies on the closely packed array of boxes or blocks as discussed above. The dimensions of the three axes of the box depend on the preset tolerance resulting in rectangular or cubic blocks. Initially a suggestion was made that the best overall ratio between the three dimensions of the box is 4:2:1, that of the common (American) brick (Simon, 1983), however this was found to be unsatisfactory.

In a cubic block sample, the centre of the cube will be 0.5 units away from all the six sides of the cube (i.e. the acceptable limit), but 0.87 units away from the corners. The distance along the longest diagonal in this case will be 1.73 units and 1.41 across the shortest diagonal. This lack of non-uniformity can be overcome by changing the shape of the box to a spherical shape with only one colour unit such as the ΔE colour tolerance unit for diameter such as 0.5 CMC (2:1). As discussed above, in that case all the samples within the sphere would have ≤1.0 ΔECMC units colour difference and hence will be in the acceptable limit. However, the spheres cannot be closely packed as shown in Fig. 7.3 and would cover only 52.3% of the original tolerance cube; resulting in the non inclusion of many acceptable samples in the specifi ed tolerance space (cube). Another shape of the tolerance space that has been used is the truncated octahedron (TO) with 14 faces, six square and eight hexagonal faces, with each side having the same length as shown in Fig. 7.4(a) (Aspland and Howard, 1997). Truncated octahedrons can be closely packed as shown in Fig. 7.4(b). The numbers are integers in the case of 555, but include half-integers in the case of TO sorting blocks, e.g. 5.5, 4.5, 6.5. The truncated octahedron shade sorting programme was used in all shade sorting programmes supplied by Instrumental Colour Systems in 1984 (McLaren, 1987).


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7.3 Packing of spheres.

(a) (b)

7.4 (a) Truncated octahedron; (b) close packing of truncated octahedrons.


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It is suggested that the relative colour uniformity of the samples in the chosen block may be assessed from the ratio of the minimum and maximum dimensions of the shade sorting blocks and the inherent effi ciency, as a percentage relative to the volume of a sphere of the same diameter (Table 7.2) (Aspland et al., 1990).

The use of shapes of blocks other than simple cubes, such as the rhombic dodecahedron and truncated octahedron shapes, improve relative uniformity but still suffer from the inherent disadvantage of the block systems. The block sorting system, even if refi ned, still does not optimise the sorting process to minimise the number of groups produced. They rely on allocation of samples into one of the blocks in the rigidly structured array around the standard. Block sorting systems may be satisfactory in a situation in which it is desirable to locate future production into previously established groups (Wardman et al., 1992).

Another, drawback of the fi xed grid is that of the closely matching samples falling on the periphery of the adjacent boxes resulting into more groups of samples adding to increased inventory, storage and handling problems.

7.3 Clemson Colour Clustering

In a pioneering effort of developing an alternate method of shade sorting, Aspland and co-workers (Aspland et al., 1987) proposed a clustering method in 1985 and named it as the Clemson Colour Clustering (CCC) method. In this method a set of dyed samples is divided into a minimum number of groups such that the colour difference in each group is within the specifi ed tolerance limit. Each group of dyed sample is called a cluster that is located in a sphere in colour space. The main difference between the CCC method and the 555 method is that in the 555 method the colour acceptability space (i.e. tolerance limit) is defi ned in a specifi ed manner and the dyed samples falling within the acceptability space are ‘put’ into the boxes (cubes or polyhedra) while in the CCC method a minimum number of spheres (clusters) of specifi ed size are created to house all the dyed samples. This method does not take into consideration the concept of colour acceptability space while creating the cluster.

Some of the salient points of the CCC method are:

• The exact number and position of the clusters exclusively depends on the sample colorimetric data.

Table 7.2 Indices of shade sorting effectiveness

Geometry of block Colour uniformity Inherent effi ciency (%)

Sphere 100 100Truncated octahedron 78.0 68.3Rhombic dodecahedron 71.0 47.7Cube 58.0 36.8


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• The spheres (clusters) that are created to ‘accommodate’ the dyed samples may not cover the entire acceptable colour space, however, all the dyed samples presented for sorting as a given lot are accommodated in one or the other sphere.

• The spheres created for accommodating the dyed samples may overlap each other but no dyed sample will be accommodated in more than one sphere.

• This method is independent of the equations used to determine the colour difference.

The clustering technique employed in the CCC method is known as the hierarchical agglomerative clustering technique or according to the authors, hierarchical complete linkage clustering technique (Aspland et al., 2000). In this technique, starting with one point (single sample) cluster the other clusters are recursively merged to the ‘parent’ cluster to create a large cluster until the termination criterion is reached. The choice as to which cluster is to be merged next is determined by the clusters of minimum colour difference. The colour difference between two clusters is quantifi ed by the colour difference between those two points (samples), one from each cluster, that have maximum colour difference (ΔECMC). It is also referred to as the colour merge value (Aspland and Jarvis, 1992). The diameter of the sphere (cluster) is the function of the maximum colour difference between all pairs of dyed samples in that cluster. This process of recursive merger to the ‘parent’ cluster with other clusters to create a large cluster is illustrated in Fig. 7.5. In this fi gure four clusters have been recursively merged to produce a single cluster. The fi nal number of clusters produced by this method determines the number and size of the shade sorting groups.

88,1.04

265,1.53

301,2.18

112,0.79

65,1.07

36,0.99

177,1.14

7.5 CCC clustering process in which successive groups are being linked where blocks have shade number and colour difference. (Reproduced from Aspland et al., 1987.)


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On the basis of the shade sorting of the 301 navy blue twill fabric samples by visual sorting by experts and by the CCC method into 10 and 11 clusters, respectively, it has been concluded by Aspland et al. (1987) that the samples in CCC clusters were more uniform without any exception. Moreover, they later reported that the CCC method normally classifi es a given set of samples into less than half the number of groups than the 555 shade sorting method, as indicated in Table 7.3 (Aspland et al., 1990).

These observations establish that the CCC method sorts a given set of samples into the minimum number of groups and the samples within the group have a more even distribution of samples of acceptable colour difference. When assessed by this criterion, the CCC method has been shown to be far superior to the 555 and other block methods of shade sorting.

The CCC shade sorting system is a ‘dynamic’ system, in which the supplied dyed rolls (lots) or those in the stock grouped together initially may change when a new shipment is added to the inventory. This may change the number of groups and also the position of the initially grouped rolls from one cluster to another. As previously stated, it is the colorimetric values of the rolls which determine how they are grouped, not how they fall into a grid; while in the 555 system new rolls on evaluation fall into the previously created boxes (groups) on a grid. No need to re-group all the rolls of fabric.

Figure 7.6 shows how the CCC programme should be used in order to best utilise its capabilities. Note that the inventory is shade sorted after new shipments arrive and after rolls have been removed for cutting. It is to the user’s advantage to update the clusters and repeat shade sorting after each of these operations.

A limitation of the CCC clustering method is that the sorting is carried out without reference to a standard. This means that the groups produced are not coded according to their position in the colour space relative to the standard.

Table 7.3 Comparing two populations sorted by the CCC and 555 methods

Shade sorting Population Total number of Groups Percentage ofgeometry sorted sort groups samples in group

CCC Blue twill 4 1 37 2 29 3 22 4 12555 Blue twill 11 1 77 2 8 3 5 4 3 5–10 7*

*Percentage of all groups


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Thus the colour difference between the samples within a group and the standard coloured sample cannot be assessed.

A method to overcome this limitation of the CCC method has been proposed by Wardman et al. (1992). These investigators have suggested that initially a ‘primary’ cluster be created around the standard sample having samples with less than half of the value of the set tolerance limit of acceptability. In this way, all of the samples in the primary cluster will be an acceptable match to each other. The normal clustering method is then applied to the remaining samples that lie outside the primary cluster. This has been termed the Scotsort method.

When sets of actual production samples were sorted by the visual, clustering, Scotsort and 555 methods (Table 7.4) it was observed that the Scotsort method was less effi cient than the free clustering method, but only marginally so. In fact it gave the same number of sorted groups with three of the sets, and for the dark blue set actually sorted the samples into one group less. The Scotsort method was found to be more effi cient than the 555 method.

Newinventory

Rollsremoved

Shade sort Cut rolls

Inventory

7.6 Demonstration of CCC clustering for adoption by industry.

Table 7.4 Groups produced by various sorting methods

Colour No. of Number of sorted groups

samples

Visual Clustering Scotsort 555

Turquoise 40 4 4 6 12Pink 43 2 2 3 6Dark blue 47 5 4 3 9Yellow-orange 45 4 3 3 8Khaki 30 3 2 4 6Light blue 44 2 2 2 5Brown 30 2 2 3 4Blue 29 3 2 2 5Green 45 5 5 5 15


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7.4 K-means clustering

This method has been used by Venkatraj, Gulrajani and Ramanujam (1994) to shade sort 307 olive-green dyed fabrics supplied by different dyeing units and accepted by fi eld inspectors for stitching of army uniforms. These authors measured the L*, a* and b* values of all the samples and used the K-means clustering technique module of SYSTAT statistical and graphing software. K-means (MacQueen, 1967) is one of the simplest unsupervised learning algorithms that solves clustering problems. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fi xed a priori.

The clustering modules operate using the following logic. Initially the (L*, a*, b*) data fi le is scanned by the clustering module and depending on the user defi ned clusters the seed points are created randomly. The samples having (L*, a* and b*) nearest to the seed point are picked up until no sample is left out. The fi rst step is completed and initial clusters are formed. Subsequently new seed points are recalculated to create better fi tting clusters. This iterative process continues until the seed points do not shift any further. Finally, this algorithm aims at minimising an objective function, in this case a squared error function. The objective function (equation 7.1) is

J = Σk

j –1 Σ

x

i–1 ||x( j)

i –cj||2, [7.1]

where ||x(j)i – cj||

2 is a chosen distance measure between a data point x( j)i and the

cluster centre cj, is an indicator of the distance of the n data points from their respective cluster centres.

Distance between the seed point and an isolated point within the cluster is called mean distance (MD). Since the mean distance of various points in the cluster is nearly equal, an average mean distance for all the clusters has been calculated. Likewise, the overall mean distance of each cluster group of 4 to 20 clusters has been calculated and plotted against the number of clusters as shown in Fig. 7.7. The plot indicates that as the number of groups increase, the average mean distance decreases and becomes asymptotical. This indicates that grouping of data beyond some cluster groups is not the right solution. Tangents are drawn along the curve and an optimal number of clusters, 13 in the present case with an average mean distance of 0.3053, is obtained.

The distribution of samples within a cluster is graphically represented in Fig. 7.8 for six major clusters. The graph has been plotted between sample numbers and their respective MD by scaling each MD by a factor of 1.00 to each successive cluster so that the isolated points within the cluster can be represented more clearly. Barring some isolated samples, all the samples lie within a very narrow range showing uniformity of the shade within a cluster.

Li et al. (2001) have studied the performance of CCC and K-means shade sorting methods and observed that the performance of CCC shade sorting arithmetic is


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7.7 Number of clusters vs. mean distance.

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7.8 Graphic presentation of clusters.


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very effective, resulting in a good utilisation of fabric. However, the K-means shade sorting could do the same job with higher compactness of the sorted groups. They have stated that it is rather diffi cult to distinguish which is the more suitable for shade sorting.

7.5 Modifi ed CCC shade sorting method

Li et al. (2001), having evaluated the CCC and K-means shade sorting methods and fi nding these methods to be equally effective, carried out modifi cations of the CCC shade sorting process by combining hierarchical and non-hierarchical algorithms into a single process to improve the effectiveness of shade sorting. In the modifi ed CCC method, cluster seeds are located for non-hierarchical clustering. Subsequently, sequential K-means sorting is used to modify the outcomes obtained from the CCC shade sorting method.

A comparative study was carried out to assess the performance of the original CCC and the modifi ed CCC with sequential K-means shade sorting in terms of:

• formation of sorted groups,• variation of colour within sorted groups,• utilisation of coloured fabric,• distribution of the population, and• compactness of individuals in a sorted group.

This study indicated that the modifi ed CCC method, originating from CCC sorting and subjected to additional sequential K-means clustering, shows better overall performance.

7.6 Shade sequencing and clustering

Shade sequencing is a traditional process of manually arranging lots of dyed pieces so that the colour difference between the adjacent pieces is not visually perceptible before stitching a garment. The shades can be sequenced by using visual tapering method, i.e. arranging lighter to darker or vice-a-versa, which is a one-dimensional solution to the three-dimensional problem since colours are described in terms of three parameters, namely lightness, chroma and hue. It fi ts into the argument that, not withstanding the sophistication of instruments or the complexity of the mathematical formulae applied, there is always, at some stage of the process, someone who must look at the colour and decide whether to accept or reject it.

The problem of visual sequencing becomes complex when slight variation of hue is encountered along with the shade depth variation. In a study conducted by Wills (1997) it was observed that disagreements between shade sorters as well as the repeatability of a single shade sorter increased when the variation in shade occurred due to hue, chroma and lightness-darkness of the shade (random


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variation). However, when variation occurred primarily in L* or C* (linear variation) the tapering and sequencing becomes easy with good agreement between sorters. Similarly when both L* and C* vary, tapering can be done smoothly, more so when the variation of one colour parameter is predicted by the second as in the case of dyed indigo denim, where L* and b* are taken into consideration for tapering as shown in Fig. 7.9.

A solution to the linear tapering and random variations has been proposed by Wills. In the case of linear variation, the two parameters with largest variation (such as ΔL* and ΔC*) may be plotted by adding the minimum range of L*, a* and b* to each piece so that all the points lie in the fi rst quadrant and the best-fi t line can be worked out. Using the slope and intercept of this line, ΔEcmc between each point and intercept are calculated and arranged sequentially from highest to lowest along with the sample numbers to create a linear taper sequence. The samples so arranged may be divided into groups for stitching.

For taper sequencing of samples with random variation of colour, Wills has developed a method called Minimum Path which mimics the type of pattern produced by human shade sorters. The acceptability of the taper sequence can be judged by the number of adjacent pieces that exceed the ΔEcmc value of 0.35–0.50.

L*

b*

7.9 Graphical representation of linear tapering of blue dyed sample from light to dark shade and the best fi t line.


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Clustering of the tapered samples can be carried out by a pattern recognition technique known as the nearest neighbour technique. However, this technique cannot be applied when garment parts are cut at different times and then stitched or when multiple products must match each other.

A further improvement in the combined sequencing and cluster technique has resulted in the development of the adaptive clustering technique. Adaptive clustering uses external feedback to improve cluster quality; past experience serves to speed up execution time. The algorithm carries out simultaneous n-dimensional clustering of multiple data observations. It fi rst orders the observations by successive nearest neighbour, in the n-dimensional Euclidean sense, from a defi ned starting point. It performs clustering adaptively without any assumptions about the size, number, or statistical characteristics of the clusters.

The adaptive clustering technique developed by SheLyn Inc for shade sorting combines clustering, sequencing and historical analysis. Initially ellipsoidal clusters are created based on user-defi ned ΔEcmc tolerance. The data of the pieces that do not fall into any cluster is maintained and when the new pieces arrive their data is compared with the left-out pieces. If found compatible, all these pieces are then used to create a new cluster which may be slightly shifted towards the centre of gravity of the cluster. The process is terminated when a suffi cient number of pieces are added to the cluster. The pieces within each cluster are also sequenced, thus these clusters can ‘adopt’ based on evolving history. SheLyn Inc. has incorporated all these features in their Color iMatch Industrial software. This software is being used by GretagMacbeth.

7.7 References

Aspland J R and Howard R W (1997), ‘Instrumental shade sorting: Past, present and future’, in: Colour Technology in Textile Industry, 2nd edition, Committee RA36 Colour Measurement Test Methods, AATCC, 121–130.

Aspland J R and Jarvis J P (1992), ‘Shade sorting re-examined’, Text. Chem. Col., 24(9), 88–91.

Aspland J R, Jarvis C W and Jarvis J P (1987), ‘An Improved method for numerical shade sorting’, Text. Chem. Col., 19(5), 22–25.

Aspland J R, Jarvis C W and Jarvis J P (1990), ‘A review and assessment of numerical shade sorting methods’, JSDC, 106, 315–320.

Aspland J R, Balasaygun K D, Jarvis J P and Whitaker T H (2000), ‘Alternative mathematical approaches to shade sorting’, Color Research and Application, 25(5), 369–375.

Hirschler R and Zwinkels J (2007), ‘Use of CIE colorimetry in the pulp, paper, and textile industries’, in: Schanda J (ed.), Colorimetry: Understanding the CIE System, New York: John Wiley and Sons, 425.

Li Y S W, Yuen C W M, Yeung K W and Sin K M (1998), ‘Instrumental shade sorting in the past three decades’, JSDC, 114, 203–209.

Li Y S W, Yuen C W M, Yeung K W and Sin K M (1999), ‘Regression analysis to determine the optimum colour tolerance level for instrumental shade sorting’, JSDC, 115, 95–99.

Li Y S W, Yuen C W M, Yeung K W and Sin K M (2001), ‘Modifying an existing numerical shade sorting system through cluster analysis’, Textile Res. J. , 71(4), 287–294.


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MacQueen J B (1967), ‘Some methods for classifi cation and analysis of multivariate observations’, Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297, Berkeley: University of California Press.

McLaren K (1987), ‘Colour space, colour scales and colour difference’, in: Colour Physics for Industry, ed. R McDonald, Bedford: SDC, 114.

Simon F T (1983), ‘Practical applications of colour control’, AATCC/ISCC, 22.Sule A D (1997), Computer Colour Analysis: Textile Applications, New Age International

(P) Ltd, New Delhi, India, 118.Venkatraj R, Gulrajani M L and Ramanujam V (1994), ‘Shade sorting using a novel

technique’, in Technological Conference, Resumé of Papers, BTRA, SITRA, NITRA & ATIRA, 35, 167–177.

Wardman R H, Weedall P J and Lavelle D A (1992), ‘Some observations on the colour clustering method of shade sorting’, JSDC, 108, 74–78.

Wills F B (1997), ‘Automated taper shading and clustering methods’, in: Colour Technology in Textile Industry, 2nd edition, Committee RA36 Colour Measurement Test Methods, AATCC, 131–134.

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184


8Determining uncertainty and improving

the accuracy of color measurement

J. A. LADSON, Color Science Consultancy, USA

Abstract: We review the subject of uncertainty of measurement in spectrometric measurements of colored materials. Defi nitions of uncertainty components are offered, and methods of assessment and calculation of those components are revealed; this allows a user to prepare an uncertainty budget for their system. We address the practical applications of industrial measurement system uncertainty and example data sets are presented from measurements obtained from different industries. The reduction of uncertainty in color measurements can be addressed by training, the proper use of statistics, and reducing the systematic errors in spectrometers. Reducing systematic errors enables users to correlate global color values so that they will be nearly identical and can therefore be compared directly. This includes instruments manufactured by different manufacturers, and instruments manufactured by the same manufacturer. The methodology can be deployed on six different spectrometer modalities, including bi-directional geometries, that is, 45°/0°; hemispherical, either d/0° or d/8° geometries; or multi-angle geometries. Multi-angle geometries are used to characterize and assess gonioapparent colorants.

Key words: colorimeter, colorimetry, diffuse refl ectance, inter-instrument agreement, measurement uncertainty, spectrometer, spectrophotometer, uncertainty.

8.1 Introduction to determining uncertainty

Measurement accuracy of color is an important parameter of color measurement, especially in today’s society. Looking around our world and being sensitive we see color everywhere we look as it is an important commercial parameter of many, many products. For instance, the sales of a particular brand of digital televisions (LCDs) are signifi cantly infl uenced by their color reproduction accuracy; i.e., which one has ‘better color’ when viewed and compared in the store. The same accuracy in color measurement that is required here to make the best appearing color display is also present in other industries; such as colorants, coatings, food-stuffs, inks, paints, paper, plastics and textiles. In these industries and others, color reproduction accuracy is essential to transmit not only the perception of quality to the consumer but to reduce the manufacturing expense using process control methodologies. That means reproducing the same exact color consistently from batch to batch, from day to day, and from year to year.

The best way to address the issue of color reproduction is to fi rst access the error budget and then focus on reducing the single largest source of error. Let us begin with quantifying the uncertainty of measurement.

Improving the accuracy of color measurement 185

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8.2 Uncertainty

Technically, uncertainty analysis is the determination of the quality of the measurement result. It determines the range of values that can reasonably be attributed to the measured quantity and will most often be expressed as result ± uncertainty values.

Previously published literature1,2,3 has generally referred to analysis of uncertainty components that would pertain at the level of a national standardizing laboratory. National laboratories promulgate the scales by which we measure refl ectance and thereby color. We, the users, receive the standardized scales through the mechanism of a transfer standard(s) from the national laboratory, through the instrument manufacturer, and to our measurement system where they become a component of our uncertainty.

The Guide to Measurement Uncertainty4 (GUM) is an international standard that outlines the methods by which laboratories may assess and report uncertainty. ISO 170255 requires laboratories seeking accreditation to report uncertainty with each accredited measurement. Because the teachings of the GUM do not entirely apply to the measurement of color, we offer the following as a reasonable alternative methodology for estimating the uncertainty of one’s measurements.

There are several industrial applications in which an understanding of the total measurement system of uncertainty would be useful. For instance, here are a few examples.

• What product tolerances can we maintain in manufacturing? Certainly, no product tolerance can be maintained that is less than the uncertainty of the measurement system controlling it.

• Many times multiple manufacturing sites will produce the same or a similar colored product. Total measurement system uncertainty answers the question: How close in terms of total color difference, CIELAB ΔE, can we produce product standards?

• What contribution do differing measuring instruments in different locations, say quality control and manufacturing, make to the total measurement system uncertainty?

• Another frequent issue is: Color measurement varies over the surface of the specimen and from batch to batch. How many measurements on a sample do we need to make to obtain a value that is statistically representative of the product?

Quantifying the total measurement system uncertainty allows us to provide quantitative, defi nitive answers to these and other typical industrial questions. Answering these questions, and others not posed here, adds value to the enterprise by increasing the throughput of manufacturing, increasing product quality, and decreasing manufacturing costs. Measurement system uncertainty is, therefore, going to have a wave of future interest directed to it.

186 Color measurement

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The goal of this chapter is to provide an easy-to-use methodology for the user of a spectrometer (spectrophotometer) to assess the uncertainty of his colorimetric measurement and to provide some detailed examples applicable to industries. The term spectrometer replaces spectrophotometer, and is the accepted term defi ned by ASTM6 E-2847.

8.3 Defi nitions

Let us begin by defi ning several terms that will be utilized in order that we may discuss these concepts, with reader and writer agreed as to the ways in which the language of uncertainty is to be used.

Measurement system – the entirety of variable factors that could affect a measurement’s precision, accuracy, or uncertainty such as the instrument, the operator, the environmental conditions, the traceability scheme of the calibration, the quality of the transfer standard, the specimen aperture size, as well as other factors.Instrument uncertainty conditions, of a measurement – conditions wherein the measurements are made repetitively as rapidly as is feasible, or desired, without replacement of the specimen being measured in the specimen port of the instrument.Operator uncertainty conditions, of a measurement – conditions wherein the measurements are made repetitively as rapidly as is feasible, or desired, with replacement of the specimen being measured by the operator completely withdrawing the specimen from the specimen port and replacing the specimen in the specimen port prior to the ensuing measurement so that the specimen aperture samples the same location on the specimen to the best of the operator’s ability to accomplish.Levelness uncertainty conditions, of a measurement – conditions wherein the measurements are made repetitively as rapidly as is feasible, or desired, with replacement of the specimen being measured to an entirely new location on the face of the specimen with the intent of sampling the entire surface of the specimen, or as much of the surface as is practical, by the end of the repetitive sampling run.Instrument uncertainty – the results of uncertainty analysis of a measurement system made under instrument uncertainty conditions.Operator uncertainty – the results of uncertainty analysis of a measurement system made under operator uncertainty conditions.Levelness uncertainty – the results of uncertainty analysis of a measurement system made under levelness uncertainty conditions.Levelness – the property of a colored specimen whereby the specimen presents itself to an instrument with an identical measurement over the entire surface to be measured.

Discussion: Generally high levelness (the same colorimetric value all-over) is considered a salutary property, and low levelness is considered a deleterious property.


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When we measure levelness uncertainty, for instance, the two earlier uncertainties, instrument and operator are included as may be conceptualized by Fig. 8.1.

If we measure each of the three kinds of uncertainty, we will be able to quantify each component by subtraction of the included components from the inclusive one and thereby obtain by calculation the value representing the total measurement uncertainty of the system.

The measurement of uncertainty is done through the use of multiple measurements and by the determination using statistical techniques of the 95% confi dence interval, which would be the equivalent to a two sigma expansion factor in a normal distribution. The range of that value when added and subtracted from the measured value gives a range in which 95% of the variation is expected.

8.4 Tables of results

In the tables, values may be found for the calculated (subtracted) uncertainties for the component values of CIE XYZ tristimulus values; X, Y, and Z, as well as the CIELAB component uncertainty values; L*, a*, and b*. These are given in columns from left to right under the following uncertainty components: calculated instrument uncertainty (CIU), calculated operator uncertainty (COU), and calculated levelness uncertainty (CLU). The total uncertainty is the vector length (the square root of the sum of the squares) of these three components. Because these components add under quadrature, the total uncertainty is the same as the largest component as in the case of Table 8.1, but this is not always the case.

The units of the table are those of the value itself. That is, the uncertainty of the CIE X tristimulus value is expressed in the same un-dimensioned units as

Levelness

Operator

Instrument

8.1 Relationship of uncertainty parameters.


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the value of CIE X component unit is expressed, and the uncertainty value of component value CIE b* is in CIELAB units.

In Table 8.1, the uncertainty values in units of the value itself for a single yellow smooth glossy plastic plaque for Instrument 100, we fi nd the results of an uncertainty assessment of a yellow smooth glossy plastic plaque on two instruments of the same manufacturer and geometry. Instrument 100 has a sample aperture of about 7 mm and Instrument 500 has an aperture of 25 mm. Notice that the instrument uncertainties are both small and of the order that is reported to us by the instrument manufacturers at the time of purchase as the instrument repeatability specifi cation. In the subsequent columns of the table, we take into account the operator’s ability to replace the specimen in the exact same location for each measurement. There is added uncertainty involved with this operation. Finally, we take into account color difference that may arise by the sample’s lack of levelness over the entire surface to be measured. We cannot determine where on the specimen the aperture might be placed if a duplicate measurement were to be made by another person. Consequently, measuring all over the surface is a legitimate component of uncertainty for any measurement.

All expressed uncertainties in the tables are 95% confi dence intervals determined from 30 measurements. While as few as ten measurements is generally suffi cient to characterize these values adequately, the uncertainty values are a little larger when a small number of replicate measurements are used due to elements of uncertainty introduced by the fewer measurements. Beyond, say, 32 measurements, a point of diminishing returns takes over and no further additional number of measurements will improve the precision of the values obtained.

In Table 8.2, we fi nd the uncertainty components of a second plastic plaque. This time the plaque is a baby blue smooth glossy plastic. Similar results pertain.

In Tables 8.3 and 8.4 we fi nd the uncertainty values for the same plaques measured by the same operator on a different instrument. In this case, the instrument is Instrument 500 with a larger aperture.

Variations in the results emphasize that the uncertainty results are dependent upon all elements of the measurement system and the way in which they interact.

Table 8.1 The uncertainty values in units of the value itself for a single yellow smooth glossy plastic plaque for Instrument 100

CIU COU CLU Total uncertainty

CIE X 0.04 0.00 0.40 0.40CIE Y 0.04 0.00 0.47 0.47CIE Z 0.01 0.00 0.56 0.56CIE L* 0.03 0.00 0.32 0.32CIE a* 0.01 0.00 0.35 0.35CIE b* 0.05 0.03 1.39 1.39CIE ΔE* 0.06 0.04 1.46 1.46


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One might expect that the larger aperture of Instrument 500 would average the color levelness of the baby blue plaque in the same way it appears to be doing to the yellow plastic. In both cases, this averaging appears to take place resulting in an approximate halving of the uncertainty measurement on the larger instrument.

8.5 Conclusions: determining uncertainty

In the case of these plastic plaques the major component of uncertainty of measurement is the levelness contribution. It is incumbent upon the spectroscopist,

Table 8.2 The uncertainty values in units of the value itself for a single baby blue smooth glossy plastic plaque for Instrument 100



Table 8.3 The uncertainty values in units of the value itself for the same single yellow smooth glossy plastic plaque for Instrument 500



Table 8.4 The uncertainty values in units of the value itself for the same single baby blue smooth glossy plastic plaque for Instrument 500


CIE X 0.00 0.01 0.10 0.10CIE Y 0.00 0.01 0.10 0.10CIE Z 0.01 0.00 0.07 0.07CIE L* 0.00 0.01 0.07 0.07CIE a* 0.00 0.01 0.08 0.08CIE b* 0.00 0.01 0.14 0.14CIE ΔE* 0.00 0.01 .016 0.16


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quality control and quality assurance personnel, colorists, and laboratory managers to assess the total measurement system uncertainty under their conditions. Having obtained the measurement uncertainty values one may decide to reduce the most signifi cant error, which is usually a difference between instruments.

8.6 Improving accuracy: the absolute correction of

instrumentally generated spectrometer values

Generally, for modern instruments that are close in performance, that is less than 1 CIELAB DE unit, 1 DE*ab, different from one another, one can expect that the errors will be reduced nearly an order of magnitude to an average of less than 0.10 DE*ab unit across 14 BCRA8 tiles. This agreement or correlation between two instruments can be relative, that is one instrument to another, or absolute, that is, correlating one or more instruments to a reference instrument or reference values.

ISO9 and CGATS.510 recommend methods to improve inter-instrument agreement. The software uses traceable artifact standards to do the training. Implementing this program allows conformance to in-house certifi cation programs, ISO requirements, and CGATS. This is of particular importance to those who utilize ICC profi ling.

Typically, there are multiple implementation methodologies available. For instance, software can be incorporated into your existing software through a Dynamic Linked Library (DLL). It can be used externally in post processing modes with an MS Excel spreadsheet, or it is usually available as part of a full-featured, quality control package. These programs operate transparently to the user. One can expect that the software, the support programs, and documentation required to utilize the program are included.

8.7 Introduction to improving accuracy

The authors are professionally engaged in the instrumental measurement of color. The correlation of instrumental measurements is desired and yet has been elusive. There are at least fi ve situations where improvement of instrumental spectrometer values would be benefi cial.

The fi rst case involves customers with multiple manufacturing sites. In this case, customers want identical color values from multiple color measuring spectrometers around the globe so that data can be compared and analyzed. In many industries manufacturing facilities are off-shore while the central laboratories are located in the US, for instance.

The second case involves customers who in this business climate of corporate acquisition acquire companies that have spectrometers of different modalities. For instance, there are six popular modalities: SIN11, SEX12, d/8°13, d/0°14, 45/0°15 and multi-angle16. Not included but also appropriate are a plethora of


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non-standard modalities. It is desirable to correlate values from any instrument with any modality to those values generated by a central laboratory instrument.

The third case is customers who have large product databases. In this case, the customer wants to preserve the integrity of the database. Often they are told that the database has to be ‘scrapped’ because the values obtained with the new instrumentation do not correlate with the values obtained with the old instrument. Utilizing the old database values with the new instrumentation enables them to utilize standardized methods without introducing confusion.

The fourth case involves customers who utilize a single universal database. Colorant manufacturers and large manufacturers require data compatibility from around the globe as they use the colorant information for computer color matching and product colorization.

The fi fth case involves companies who regularly utilize computer color matching or batch correction. These colorizing methods involve the preparation of multiple calibrations (let downs) for each colorant in a formula. Typically three to six colorants will be used to create each color. In this case the K/S values are calculated from measured spectral refl ectance values at each wavelength. The K in the K/S represents the absorption component of the mixture and the S represents the scattering component of the mixture. A form of Kulbeka-Munk17 equations uses these values to compute the desired pigment concentrations.

Statistical studies of the customer’s databases for these cases show a weak correlation between different modalities, confi gurations, and different manufacturer’s instruments. A model that could provide such a vehicle is of interest to the color community and has commercial value. Today as we engage in globalization and standardization, such as ISO, correlation becomes increasing important. The ability to utilize digital color values transmitted over the internet such as Gates described in Business @ the Speed of Thought18 has enormous competitive advantage by shortening the supply chain and reducing the time to market.

8.8 Experimental modeling

The parameters for a proprietary algorithm and their coeffi cients were put together in a computer program that allowed us to input data (such as a database or library), process that data, and analyze the results generated by the executable program. This allowed us to validate and adjust empirically if necessary the program, thereby optimizing the results. The results of the executable program are reported in two sets of specimens. The fi rst set consists of less than (<) 1700 samples representing a library sampling a very large portion of reproducible color space. The distribution of samples in color space is shown in Fig. 8.2. This fi gure is the CIELAB data of the library plotted in the CIE a*–b* plane of CIELAB color space. The same sample set is shown in Fig. 8.3 in the L* versus a* plane of the CIELAB color system. The library samples most of color space extensively. We then plotted the spectral refl ectance factor and Fig. 8.3 shows the


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distribution of 255 samples randomly selected. The quantity of samples presented is a limitation of MS Excel.

It is seen that the sample set contains high chroma samples. Samples with high chroma are diffi cult specimens to measure and correct because a small variation in the steep slope of the refl ectance curve causes a large error in CIELAB color space. The sample set contained a large number of neutral and near neutral samples and these samples tested the effi cacy of the photometric correction. The surface fi nish on the sample set is primarily matte and the samples are paint applied on a stiff paper substrate. There were no special precautions taken when performing the measurement of the library, but these were performed by experienced operators

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Mean plus sample location inCIE a* - CIE b* space

CIE a*

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8.2 Specimen location in CIE a*–b* color space.

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Sample test setCIELAB L* a* plane

120

8.3 Distribution of specimens in the CIELAB L* versus a* plane.


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and those skilled in the art. The instruments were standardized at regular intervals throughout the measurement process in accordance with the manufacturer’s recommendations. All the measurements of a library on one instrument were made in one day and were performed under reproducibility conditions; that is, different operators, different sample sets, different locations, and different instruments performed on different days. Reproducibility data are the most diffi cult type of data to correlate since all known variables need to be considered. The effectiveness of this algorithm was tested by comparing values obtained using industrial spectrometers, the Macbeth (MCB) 7000™19 and the BYK-Gardner (BG) Color View™20. The MCB is a D/8° sphere operating in the SEX modality with a 10 nm bandwidth instrument reporting from 360 to 780 nm, with a 30 mm LAV aperture. The BG instrument is a 45°: 0° geometry, LAV 25 mm aperture reporting from 400 to 700 nm with a 10 nm bandwidth at 10 nm intervals. The spectral bandwidth and center band frequency are not constant over the wavelength region of interest.

Both the instruments use a white tile for standardization supplied by the manufacturer, and were standardized according to the manufacturer’s instructions. The manufacturers’ calibrations are traceable to different national standardizing laboratories, resulting in signifi cantly different full scale 100% settings, approximately 2%. No other adjustments to the spectrometers were performed. The spectral refl ectance factor data for each sample was collected and analyzed by another program in MS Excel that converts the data from spectral refl ectance values to CIELAB values for the CIE 10° Observer Function under Illuminant D65.

We analyzed the resultant data by a technique called binning. That is, we created bins or buckets of 0.3 units CIELAB DE* from 0.00 to the maximum value. For the fi rst bin we counted the number of samples whose values are between 0.00 and less than (<) 0.29 unit. For the second bin we counted the number of samples whose values were greater than (>) 0.30 to <0.59 unit, and so forth up to the maximum value. This is the data reported for the uncorrected values. Then we ‘trained’ the BG Color View to emulate the MCB 7000 unit. We analyzed the results and they are shown graphically in Fig. 8.4. The program could perform the reverse correlation, that is correlating the BG Color View to the MCB 7000 as well, with similar results.

The data shows that in the corrected state >90% of the samples are in bins less than 1.2 (<1.2) CIELAB DE*ab units with a maximum value of 2.7 (<2.7) CIELAB DE*ab units. This is contrasted to the uncorrected state where approximately 90% of the samples are contained in bins less than 3.3 (<3.3) CIELAB DE*ab units with the maximum being 6.6 CIELAB DE*ab units.

8.9 Applications

The applications for this program are applicable to those who measure color in a variety of applications and who are searching for an implementation method, a


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different method, or a different business model. This program will correct reported values by instruments made by different manufacturers, improve performance of instruments made by the same manufacturer, allow cross instrument modalities to be correlated, correct existing databases to conform values obtained from new instrumentation, and allow customers to preserve existing databases. The program is also appropriate for color measurement of objects, such as coatings, inks, paints, plastics, and textiles.

8.10 Conclusions: improving accuracy

The model for the correction of absolute spectrophotometric errors has been developed using numerical methods and proprietary modeling techniques. The correction model is based in large part on the work that was begun in the 1980s. The math model deployed shows that systematic effects for known errors can be substantively reduced. The model shows that errors occurring in inter-instrument agreement, between a manufacturer’s family of instruments; instruments of different modalities; and intra-instrument agreement, different manufacturer’s instruments, can be substantially reduced thus providing the close agreement within or between factories around the globe.

The Mean Plus™21 algorithm compensates for variances that occur as a result of design, calibration, and manufacturing. The sources of error include specular port error, photometric full scale error, photometric zero error, photometric non-linearity, wavelength band pass, wavelength bandwidth, and sample aperture size.

Any model deployed does not improve imprecision in the form of repeatability of an instrument or correct the effects caused by translucency blurring,22 which is an effect found in translucent materials. Improved correlation is shown between sampling apertures of different sizes used on the same instrument; these apertures sizes are called Large Area View, (LAV) and Small Area View (SAV). Models have been performing well industrially for many years.

BYK-Gardner spectrogard 45:0 original values and corrected values binnedusing Macbeth 7000 SEX as reference

Quantity ofoccurrences

OriginalCorrected

Bin Bin interval = 0.3 unit.0.3

0

100

200

300

400

500

600

700

800

0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3.0 3.3 3.6 3.9 4.2 4.5 4.8 5.1 5.4 5.7 6.0 6.3 6.6 6.9 7.2 7.5

Original Corrected

8.4 Original uncorrected values and corrected values versus bin size.


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8.11 References

1 J. L. Gardner and R. B. Frenkel, Correlation coeffi cients for uncertainties in chromaticity, Metrologia, 21, 459–472 (1999).

2 J. L. Gardner, Uncertainty estimation in colour measurement, Color Res and App, 25, 349–355 (2000).

3 E. A. Early and M. E. Nadal, Uncertainty analysis for refl ectance colorimetry, Color Res and App, 29, 205–216 (2004).

4 A guide to the expression of uncertainty in measurement. International Organization for Standardization; 1993. Available in the United States as US Guide ANSI/NCSL Z540-2-1997.

5 ISO 17025, General Requirements for Accreditation of Laboratories. International Organization for Standardization, American National Standards Institute, New York (1999).

6 ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA, 19428-2959 USA.

7 E284. ASTM E12 Color Appearance Terminology. 8 BCRA tiles are obtained from CERAM Research, Great Britain. CERAM was formerly

the British Ceramic Research Association (they may or may not have been calibrated by NPL).

9 ISO 13655 – ISO Central Secretariat, 1 rue de Varembe – CP 56, 1211 Geneva 20 Switzerland.

10 CGATS, NPES, The Association for Suppliers of Printing, Publishing and Converting Technologies, 1899 Preston White Drive, Reston, Virginia 20191-4367.

11 SIN is an acronym for Specular Included modality.12 SEX is an acronym for Specular Included modality.13 D/0° refers to diffuse illumination, viewing at 0 degrees relative to the sample normal.

See ASTM 1331 for a complete explanation. ASTM International, West Conshohocken, PA, USA.

14 D/8° refers to diffuse illumination, viewing at 8 degrees relative to the sample normal. See ASTM 1331 for a complete explanation. ASTM International, West Conshohocken, PA, USA.

15 45/0° refers to bidirectional geometry. Refer to ASTM E 1349 for a complete explanation. ASTM International, West Conshohocken, PA, USA.

16 Multi-angle refers to instrumentation designed to characterize and measure gonioapparent coatings. See ASTM E 2194 for a complete explanation. ASTM International, West Conshohocken, PA, USA.

17 Judd & Wyszecki, Color in Business, Science & Industry, second edition, John Wiley & Sons, New York, 1952.

18 Business @ the Speed of Thought, Bill Gates, Warner Books, May 2000.19 Trademark of X-Rite Corporation, Grandville, MI, USA.20 Trademark of BYK-Gardner, Columbia, MD, USA.21 Mean Plus is a registered trademark of Resource III, Tatamy, PA, USA.22 Spooner, D.L, Translucent blurring errors in small area refl ectance spectrophotometer

& densitometer measurements. In: TAGA Proceedings 1991. Rochester, New York, USA: RIT Research Corporation, 1991, pp. 130–14.

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196

9Colour measurement and fastness assessment

M. BIDE, Department of Textiles, Fashion Merchandising and Design, University of Rhode Island, USA

Abstract: Changes in the colour or the transfer of colour from coloured items, especially textiles, is undesirable. Standard tests have been developed particularly by ISO and AATCC to reproduce the challenges that coloured items face and thus predict such poor colourfastness in use. Assessing the colour changes and staining that occurs in these tests is traditionally carried out visually by reference to standard grey scales, but instrumental assessment of colourfastness with equations that generate data corresponding to the grey scales is becoming more widely used. More rapid and accurate assessments are suggested by the use of camera systems and improved equations respectively.

Key words: colour fastness testing, grey scales (gray scales).

9.1 Introduction: colour and colourfastness

Humans have colour vision, appreciate the colour that occurs in nature, and use a range of technologies to produce colour in their constructed environment. Thus we paint our cars and houses, read coloured magazines and buy articles in coloured packaging, watch colour television and fi lms, and dye textiles used for our clothes and household furnishings.

This coloured environment can be generated by additive and subtractive colour mixing. With additive colour mixing (such as televisions, computer monitors, etc.) the colour is typically transient, and rarely is the image compared directly to real life. Poor control of colour is only obvious when the same image is seen simultaneously on several screens (in a store, or on an aircraft, for example). The colour is fl eeting enough, and constantly regenerated, so that change is part of the picture, and any dissatisfaction with the results can be varied by the viewer with brightness and chromaticity controls.

Subtractive colour mixing using light absorbing colorants is used to produce coloured objects with expectations of longer lasting colour. In some instances, the item is designed to have a very limited life (a coloured newspaper, or packaging material, for example) but often the lifetime may be months or years. In those cases, the consumer has an expectation that the colour will have some degree of permanence, the expectation varying with the cost of the item, the cost of replacing it (or its colour) and the effect of the colour loss on other items.

The colorants used in subtractive colour mixing are, with the minor exception of interference pigments, chemical dyes and pigments whose structures effi ciently absorb electromagnetic radiation in the visible region of the spectrum (i.e. light).

Colour measurement and fastness assessment 197

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These strongly coloured materials lend their colour to an item. Ignoring temporary changes of colour (if an item becomes wet, for example, or thermochromic and photochromic phenomena) the colour of the item may change, for one of two reasons:

• the colorant remains in place but is changed or destroyed, or• the colorant is removed. In this latter case, the removed colorant can generate

unwanted staining elsewhere.

The distinction between dyes and pigments has notable implications when it comes to fastness and fastness testing.

A pigment’s main characteristic is its insolubility: it exists as discrete particles, usually in the micron range, each particle consisting of billions of atoms or molecules. The insolubility will make its removal by dissolution in a solvent unlikely. If a few atoms or molecules on the surface of a pigment particle are removed or changed, the overall colour of the particle is little affected. For both these reasons items coloured with pigments inherently tend to have durable coloration. Pigments may be both organic and inorganic: the latter are particularly resistant to change.

A dye is soluble in its application and often, too, in its ultimate use. It has substantivity for the substrate to which it is being applied. While mineral ‘dyes’ were used in the nineteenth century, dyes are almost exclusively organic. The solubility of a dye, and its presence in a substrate in monomolecularly dispersed form render it more susceptible to dissolution and removal. The (continued) substantivity of the removed dye renders it liable to stain other materials. Additionally, the destruction of any of the monomolecularly dispersed individual particles will have a greater effect on the colour than on a pigment particle.

Textile items undergo a greater range of colour challenges than do most other coloured objects: textiles are fl exible and clothes move with the body, and are rubbed against each other. Decorative items may hang in sunny windows. Clothes are regularly cleaned and must withstand the rigours of hot aqueous detergent solutions or dry-cleaning solvent.

Dyes and textiles are, to a large extent, mutually inclusive. Dyes are little used for non-textile items, and pigments are less used for textiles than are dyes. Thus the problems of colourfastness are strongly associated with dyed textiles. The testing of colourfastness and the evaluation of the changes involved has been studied most widely for textile items and this chapter thus concentrates on colourfastness of textiles.

9.2 The use and usefulness of colourfastness testing

It is unrealistic to expect that an item will be serviceable without some form of assurance: no manufacturer or retailer will let an item be sold and trust that consumers will fi nd it acceptable, for in doing so they risk loss of money, reputation and future business.


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The textile supply chain begins with fi bre (natural or manufactured) and continues through yarn, fabric, fi nishing, garment construction and ultimate distribution and sale. A fi bre may undergo many different processes, and see the inside of many different textile mills, perhaps in different countries. Ideally, inadequate goods should not proceed along the supply chain, and poor quality materials should not appear on store shelves. Given the pressure to get goods to market on time, this is obviously not always true!

The most realistic means of determining acceptability is to produce sample garments and conduct a wear trial with selected consumers to see when and how failure occurs. Such real-life wear trials are occasionally performed. However, fi nding volunteers who are typical, organizing the distribution of items, writing good instructions to the volunteers, recovering the garments, analysing the results and drawing conclusions are highly expensive of time and money. Laboratory testing provides an economical and useful alternative. Standard test methods are developed that are designed to approximate to a single real-life property, and to predict how an item will respond when faced with that individual challenge. Such tests are typically economical to perform and provide results rapidly.

A good product is not characterized by a single property, but a list of minimum requirements in several tests can come close to predicting overall satisfactory performance, and such a list forms a ‘product specifi cation’ against which a product’s test results can be compared to determine general acceptability.

Testing may be conducted for one or more of many reasons. At the various stages of the supply chain quality control testing is conducted on incoming raw materials, to monitor a given process, and on the fi nished product in a well-coordinated quality assurance programme. Ultimately, all the various components are assembled and the fi nished product should be tested to predict its performance in use. At the product development stage, testing will avoid impossible fastness demands, and can assess a company’s product against those of its competitors. The use of hang-tags on garments that inform the consumer of a particular advantage in a garment must be backed by testing.

Some unsatisfactory items do get made and appear on store shelves. When, inevitably, they are returned by the customer, testing is a valuable tool in analysing the cause of the failure.

As discussed below, tests are developed in different parts of the world, and the choice of which particular test to employ to measure a property may depend not on where the test is being conducted, but where the ultimate customer is located. Thus textile items in India may undergo ISO tests if they are for export to Europe, or AATCC tests if they are for the US market.

Issues of environmental, health and sustainability have come to the fore in recent years. Several organizations offer certifi cation schemes that are designed to assure the consumer that an item has been produced in an environmentally friendly manner, that it does not contain (or will release) substances of concern, or that it is ‘organic’. Such certifi cations are largely based on testing, and many of


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the standard colourfastness tests are used in these schemes. Thus, for example, a range of colourfastness tests are included in the Oeko-tex 100 certifi cation (Oekotex 2009), and in the Global Organic Textile Standard (GOTS 2009).

9.3 Colourfastness test method development

Test methods are developed to determine a particular property of interest, and are designed to reproduce in a laboratory some real life challenge that may result in loss or change of colour. The laboratory setting allows for carefully controlled conditions and in many cases achieves the colour change in an expedient way: tests are often ‘accelerated’. The match of a test to real life may be assessed by comparing results on a range of fabrics with some real-life wear trial. As new fi bres, fabrics and fi nishes are developed, or new machines and technologies are introduced, new tests are introduced.

Tests originate in many different ways. Commonly, a commercial company may develop a test in its own laboratories to resolve a particular problem or investigate a property. If the test is a useful one that has wider applicability, it will tend to be adopted by other labs. The original company may require its use by its suppliers. The test has broader acceptance when it is not proprietary and is not the property of a private concern. In order to avoid real or imagined bias based on the test’s origins, tests that are widely used typically become the provenance of an independent, non-commercial standards organization. When a test becomes standardized, lab to lab procedures are consistent and results can be fairly compared.

The organizations are discussed in section 9.4, but the general method of working is common to all, and makes for interesting reading (Thiry 2009). The work of the organization in adopting and maintaining the test method is done by interested members, who volunteer their time to perform this valuable work. A proposed test is assigned to the relevant committee of the organization which will usually include experts in the area of the test: if not, additional expertise can be recruited. Rules ensure that no constituency (country, company) has more than one voting member of the committee. If the general concept is accepted, the test is performed at several different labs on a range of fabrics. Any problems in performing the test, and the variability in results on the same fabric, are considered as the test goes forward. When any problems are ironed out, the test is written in standard format and passed through the upper level committees of the organization for further comment or revision. At each stage of the process, it is important that the tests are accepted by more than a simple majority of those voting. A test that goes forward with reservations of a substantial minority is unlikely to achieve widespread acceptance and use. Consequently, most organizations require that a test be accepted by a substantial majority, and that negative votes and comments, if they have merit, are dealt with as far as possible.

As discussed earlier, a good test should be valid: the property it measures should have some real life relevance, and the test should predict in-service


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suitability. Additionally, a good test should be simple in terms of how it is performed, and how easy the instructions are to understand. It should also be reproducible giving the same results from operator to operator and lab to lab.

9.4 Colourfastness test standard setting

organizations

Formal colourfastness testing dates back to the early twentieth century, and as discussed in the introduction, its emphasis has been chiefl y in textiles. Its development has been concentrated in the traditionally large industrial countries, chiefl y those in Western Europe, USA, and Japan. Depending on the nature of the country’s governance, the standards-setting organizations have been either professional associations, or government agencies. The individuals who work in test development obviously need to be those with an interest in good tests, and so they might either act as members of those professional associations, or advise government agencies. Thus in the UK, for example, members of the Society of Dyers and Colourists worked to develop standard colourfastness tests that appeared under the auspices of the British Standards Institute, while in the USA, AATCC developed standards that were published under its own name, and recognized by the American National Standards Institute.

With the rise in international trade, there has been a trend for tests, including those for colourfastness, to be organized more internationally, through the European Standards Organization (CEN) who develop European standards (EN) or International Standards under the auspices of the International Organization for Standardisation (ISO) (Smith 1994). The national standards bodies continue as member bodies of these larger organizations, and in turn provide the international standards within their own country in the appropriate language, and (to quote a typical national foreword to a published test method) these organizations ‘aid enquirers to understand the text; present to the responsible international/European committee any enquiries on the interpretation, or proposals for change, and keep the UK interests informed; monitor related international and European developments and promulgate them in the UK’ (BSI 2002). 160 countries are members of ISO: these fall into the categories of member bodies, correspondent member, and subscriber member, and ISO has a central secretariat in Geneva, Switzerland. Its tests cover a multitude of areas, each area being organized by a technical committee. ISO’s technical committee for textiles is TC38, and the subcommittee of TC38 responsible for colour fastness tests is SC1. These committees and subcommittees have a member body holding the secretariat (for TC38 it is jointly held by the national standard bodies of Japan and China, JISC and SAC respectively. The secretariat of SC1 is currently held by AATCC through its participation in the US standards body ANSI.)

ISO standards are developed by working groups (WG). Many of ISO’s current standard tests originated with individual countries/organizations and have been adopted (and in some cases modifi ed) by ISO. Where ISO has not adopted a


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particular test, the national test may continue in use: for example the German test for colourfastness to saliva, DIN 53160. Even where ISO does have similar tests, AATCC in the United States maintains its own versions and publishes them in English and in Chinese, refl ecting the importance of the US market to exporters around the world.

The emphasis here is clearly on textile colourfastness testing. Trade organizations and interest groups in non-textile areas may have their own tests, or modifi ed versions of more widely standardized tests. These may refl ect the use or particular challenges that are faced by, for example, an automobile interior, a paint, or a piece of leather.

9.5 Standard colourfastness test format

While the details may vary, a typical standard test includes the following information:

• Name and number which usually includes the year in which the test was last reaffi rmed, so that the tester may know that the latest version of the test is being used. This heading information may also include the original date of the test’s introduction, and a note of the committee that is responsible for its updating.

• Scope/purpose outlines the property being tested and any limitations of applicability.

• Principle – a simple outline of the test.• Defi nitions of any terms used in the method that may not be immediately

understood are included, or the reader may be referred to a glossary.• Safety precautions that need to be addressed in conducting the test are

described.• The apparatus and materials used to conduct the test are listed. The apparatus

may refer to a specifi c manufacturer, or if it is available from several makers, may be described generically.

• Test specimens – the size, shape, and number of specimens used in the test, and any special preparation required.

• The test procedure is described, step by step. In some tests a helpful fl ow chart may be included.

• Measurement. In the colourfastness tests that are the subject of this chapter, the procedure may generate a change in colour of the test specimen, or of another material included in the test. Thus the ‘measurement’ step in such a test is one that involves the assessment of a before–after colour difference. Since this is chiefl y why this chapter should appear in this book, the discussion of ‘measurement’ is dealt with separately in more depth.

• Calculation and interpretation of the results may be required.• Report. As well as the test result itself, the report may also indicate particular

test conditions used.


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• Precision and bias. Standard tests are designed to measure a particular property of a material. In some cases several tests can provide the same information, and it may be recognized that one test gives a ‘true’ value, albeit with greater cost or complexity. Other tests may give values that vary from this true value in a systematic way, and can be said to have ‘bias’. In such a case, the tester should know about this bias, and this section of the test would include such information. In colourfastness testing, the property being measured will not have such an independently measurable ‘true value’ and thus colourfastness tests do not have a bias.

Precision, however, is important and deals with the likely variability of the results when the test is repeated. The same test repeated will produce results that vary somewhat: precision is simply a measure of the extent of that variability, and a statement of precision informs the user of the test of how much an answer must differ (from a standard, or from a prior test, for example) before the difference can be considered signifi cant. Not surprisingly, the variability of a test is least when a single operator repeats the test in the same laboratory. Variability increases (precision decreases) with multiple operators in the same lab, and further when the test is conducted in different laboratories. Such variability is usually established as the test is being developed via inter-laboratory trials. Statistical analysis of the results will provide a precision statement, often in the form of critical values by which the test result must differ to be considered signifi cant. These may be provided for different numbers of repetitions, and for within lab-single operator, within lab multiple operators, and between lab cases. The situation is made somewhat more complex in colourfastness testing because the two steps in the process (conducting the test, and measuring the results) each have their own level of precision, and contribute to the overall precision of the test. If the means of measurement can be conducted in different ways (visually and instrumentally, for example) the overall test precision is affected. The variability of grey-scale assessment and its contribution to the variability of a test method has been examined in detail for visual grey-scale assessment (Jaeckel 1980). It was found that the contribution varied with the grey scale value of the result. As the choice is made between assessment methods (discussed in section 9.7) this effect should be considered.

• Notes that provide supplementary information that will help in conducting the test.

It is noteworthy that colourfastness tests do NOT include the result (level of performance) that represents a pass or a fail, simply the way to carry out the test and get a result. The pass/fail level is a matter for the marketplace to determine, and would typically be given in a performance specifi cation that a company develops based on its standards of quality, expectations of its customers, and its experience. Unlike test methods which, as discussed earlier, are of most usefulness when made public and shared widely, performance specifi cations are often proprietary.


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9.6 Testing for colourfastness: specifi c tests

As explained earlier, colourfastness tests are designed to reproduce the challenges that real life would impart to a textile. Certain tests relate to agencies that are met in the sequence of manufacturing processes that a textile item undergoes before it gets into the hands of the consumer. Most of the challenges, however, do occur after the item is sold. These challenges can conveniently be divided into those met when the item is being used, and those involved in the cleaning or refurbishment of the item. ISO’s tests for colourfastness are listed under ISO 105, with methods grouped according to a property designated by a letter as follows: A (General Principles), B (Light and Weathering), C (Washing and Laundering), D (Dry Cleaning), E (Aqueous Agencies), F (Adjacent Fabrics), G (Atmospheric Contaminants), H (Textile Floor Coverings), J (Colour Measurement), N (Bleaching Agencies), P (Heat Treatment), S (Vulcanising), X (Miscellaneous Agencies) and Z (Colorant Characteristics) (ISO 2009). AATCC’s methods are simply given a numerical designation (AATCC 2009).

9.6.1 Production processes

Mill processes that can affect colour include scouring. Textiles woven from coloured yarns may need to be scoured or soda boiled (ISO105-X06). Multicolour textiles may include a white portion that requires bleaching, and thus a test for colourfastness to bleaching may be needed (ISO 105 N01–4 are the tests for fastness to hypochlorite, peroxide and mild and severe chlorite respectively). AATCC 101 describes the effect of a commercial hydrogen peroxide formulation on most textiles Cotton fabrics may be mercerized and affect the colour of yarns: ISO105-X04 tests this. Dry heat can cause colour changes (ISO105-P01). Wool undergoes a variety of wet treatments. Tests determine the colour changes caused when wool is carbonized with aluminum chloride or sulphuric acid (ISO105-X01,2), boiled in water (potting, ISO105-E09), bleached with sulphur dioxide gas (stoving, ISO105-N05) set with team under pressure (decatising, ISO105-E10), steamed (ISO105-E11) and most commonly, milled, the colourfastness challenges of which are refl ected in the naming of certain acid dyes that will withstand this challenge as ‘milling acid dyes’. ISO105-E12 and E13 tests alkaline and acid milling fastness.

After make-up, garments may be pleated (ISO105-P02) or hot pressed (ISO105-X11): these can cause thermochromic or sublimation based colour changes and are refl ected in standard fastness tests. Silk fabrics may be degummed in hot alkaline soap solution, and a fastness test can predict any changes to coloured materials that undergo this process (ISO105-X08).

9.6.2 Consumer use

In use, garments may be rubbed, and their colour transferred to another item. This is a common fault, and one that has potentially expensive consequences (blue


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jeans that transfer colour to a white leather sofa, for example) and the tests to determine this property are widely conducted. A white cloth (which may be dry or wet) is attached to a peg and under a load is rubbed back and forth (ISO105-X12, AATCC8) or in a rotary fashion (ISO105-X16, AATCC 116) on the textile under test. The stain on the white cloth is evaluated. Despite this apparent simplicity, the test requires some care in its performance, and is indicative of the need to control conditions even in a well-established and widely used test (Patton 1989). AATCC has developed a chromatic transference scale that is designed specifi cally for the evaluation of such stains. Its use is described in AATCC EP 8 and is discussed further below.

A variety of wet agencies can change the colour of a textile. These tests may fall into two general categories. First, water or other liquid can leave a mark after it has evaporated. The application of a spot of the liquid, followed by drying, is the basis for test for water spotting (ISO105-E07, AATCC 104), solvent spotting (perchloroethylene, AATCC 157) or to acids and alkalis (AATCC 6, ISO105-E05 and 06). Alternatively, the test may involve soaking the material in the test liquid such as hot water (ISO105-E08) or boiling water (ISO105-E09). Other aqueous challenges include the possibility of transfer to a white material, and tests to predict the effects of water, sea water, perspiration (ISO105-E01, 2, 4; AATCC 107, 106, 15) typically involve moistening the test material and an adjacent fabric, sandwiching them under pressure between plates and storing them at a controlled temperature for several hours. Perspiration (acid or alkaline) and sea water are simulated by standard solutions.

Chlorinated pool water is a different challenge, and is usually a dynamic test (ISO105-E03, AATCC 162). In the AATCC test a carefully standardized solution of chlorine is tumbled with a test specimen and the change of colour assessed. The test is subject to enough variation that a parallel test is carried out with a standard control fabric: the test is only considered valid if that fabric changes colour by a certain amount (GS 2–3 or 3).

A variety of atmospheric contaminants prevalent in industrial areas and derived from combustion and sunlight are responsible for the change of colour of a textile. The main ones are ozone, and oxides of nitrogen (or ‘burnt gas fumes’). In the burnt gas test (ISO105-G02, AATCC 23) the specimen is exposed in a chamber in which a gas fl ame is burning. A two-step evaluation is used: a control fabric is included, and its change of colour is compared to a standard of fading (a fabric dyed to match the faded colour of the control). When the two are the same, the test specimen is removed and rated according to the grey scale. A similar test involves high humidities (in which some colours are notably sensitive to NOx), and uses NOx directly rather than a fl ame (ISO105-G04, AATCC 164). Similarly, fastness to ozone can be assessed under low and high humidities (AATCC 109 and 129 respectively, and ISO105-G03). These tests also involve a control fabric and a standard of fade.

Colourfastness to light represents a particular challenge to assess in a standard test and has been the subject of much research. The main problem derives


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from the great variability of real life light exposure which includes direct and indirect light, sun vs. cloud, changes of humidity, height of sun in the sky, and so on. Nonetheless, real life exposure is included as one means of determining this important property (ISO105-B01, AATCC 16 Option 6). In addition to its variability, natural exposure often takes a long time to produce a meaningful change of colour, so instrument manufacturers have produced a range of exposure devices that shine bright lights onto materials to simulate the effect of real life light. To achieve a fade in a reasonable time requires intense light, and for many years a carbon arc lamp was the only means of doing this. Today, these have largely been superseded by xenon arc lamps, and in some devices by fl uorescent tubes. The changes produced tend to be instrument-specifi c, and thus the tests are divided into subsections based on the kind of light used. ISO105-B02 and AATCC16 Options 3–5 describe xenon arc exposure. For certain situations such as car interiors, tests that use high temperatures of exposure are used (ISO105-B06, AATCC 181). The spectral power distribution of the light varies and can be further changed with the glass fi lters through which the light passes. As with atmospheric contaminants, the changes produced can also depend on the temperature and humidity of the exposure. Thus these are controlled in laboratory machines, and real life exposure may take place in dry or humid climates.

Modern instruments can (at a certain wavelength) measure the amount of energy impinging on the specimen, and for a given light and fi lter combination, serve to indicate the total amount of energy over the whole spectrum that the test specimen is exposed to. Before this facility was commonplace, and even now that it is, the use of some standard fabric of known fading characteristics has been widespread. The standard fabric has been blue wool. However, two different series of blue wool standards have been in wide use for many decades, and are used somewhat differently. The two series both consist of eight standards, numbered sequentially, with each successive member of the series requiring approximately twice the exposure to fade to the same amount. The AATCC blue wool fabrics are created from mixtures of wool stock dyed with low fastness CI Mordant Blue 1 and high fastness CI Vat Blue 8 (numbered L2–L9). These are used primarily as an exposure check, for example, when standard L4 fades to 4 on the grey scale, the test specimen has received 20 AATCC Fading Units (AFU) of light. The specimen is exposed until it, too, has faded to the same extent, and the number of AFUs required to produce that change is quoted as the light fastness. Thus a sample may have a fastness of 40 AFU. Most machines would usually achieve 1 AFU in 1 hour of exposure, and thus colloquially a fastness of 40 AFU would be referred to as ‘40 hours’. In contrast, the ISO Blue Wool standards 1–8 consist of wool dyed with eight different dyes described in ISO105-B08. In a light fastness test, a specimen of each of the eight wool standards is exposed along with the test specimen. When the test specimen has faded to step 4, it is assigned the grade of the standard that has faded to the same amount. This ISO light fastness is given on a scale of 1–8: given the early dominance of European dye manufacturers, the


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light fastness in dye pattern cards is usually given on this 1–8 scale. A particular problem with the combined effects of light and perspiration on textile colour has been identifi ed and is refl ected in standard tests (ISO/FDIS105-B07, AATCC 125).

The continued availability of the dyes used to produce both the AATCC and ISO blue wool standards is under question, and there is interest in the development of replacement standards (Guthrie et al. 1995, D’Andrea 2009).

9.6.3 Refurbishment

Textile items become soiled, wrinkled, and require periodic refurbishment. The processes to be used in such refurbishing are given in a care label (the information on which should be derived from testing!), and they should restore the item to an acceptable level while not causing any damage. However, such refurbishment represents another set of challenges to the colour.

Laundering is conducted in aqueous surfactant solutions with agitation. That said, the details can vary markedly. Variations include the detergent (type and amount used), the temperature, the amount of water and the extent of agitation (based on the type of machine used), the nature of the materials used to make up the bulk of the load (‘ballast’) and the presence or absence of bleach. The tests to assess colourfastness to laundering include many different details that refl ect the variation in practice. The tests may involve real-time, full-scale launderings, which may be repeated three or fi ve times before an assessment is made. This is a cumbersome procedure, however, since each different item should be tested individually to avoid possible confusion in results (did staining from another item contribute to the colour change?). More often, therefore, a specifi c sample size, often with a standard multifi bre adjacent fabric, is agitated in standard detergent solution in a small (150–1000mL) cylinder for 45 minutes or so. The conditions are not the same as those in real life, but the effect on the colour has been shown to approximate to that obtained in fi ve real cycles of laundering. The test is thus accelerated, and testing machines with multiple cylinders allow for many specimens to be assessed at one time. AATCC Method 61 for example, has fi ve sets of conditions that represent the effects of hand washing, home laundering (with and without chlorine bleach) and commercial laundering (with and without bleach). ISO105-C06 similarly has 16 variations to simulate a wide range of conditions used in practice. The adjacent material must take account of the presence of chlorine and ISO105-C08, AATCC 190 are parallel tests that include an activated oxygen bleach. AATCC 172 and 188 involve full-scale launderings designed to assess the effect of non-chlorine and chlorine bleaches respectively.

The appearance of some textile items is marred excessively by exposure to aqueous solutions and are cleaned by ‘dry cleaning’ in a solvent, usually perchloroethylene. A possible change of colour is the subject of tests ISO105-D01 and AATCC 132. As with some of the laundering tests, this accelerated test produces colour changes that would occur in three commercial procedures. The


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use of perchloroethylene raises health questions, and alternative methods of cleaning textiles distinct from laundering are being studied. These include other organic solvents, supercritical CO2, and ‘wet cleaning’ with minimal agitation. If any of these become widely adopted it is likely that new tests will be required.

Clothes are ironed and this can affect colour. AATCC 133 includes variations to examine the effect of dry, moist, and wet heat: ISO105-P01 tests dry heat.

ISO has developed combined tests that are designed to predict the overall change of colour that textile items might undergo. ISO/TR 12116:2008 describes four sets of colourfastness tests that simulate overall colour changes that occur in wear, applicable to sports clothing, shirt-like garments for use outdoors, indoor clothing and underwear, and military uniforms.

9.7 Colourfastness testing: assessment of results

(colour measurement)

One particular characteristic of a colourfastness test that tends to distinguish it from non-colour tests is the means by which the results are assessed and reported. The assessment is obviously a measurement of colour and is the reason for this chapter’s presence in this book.

Colour measurement can involve a single colour, or be concerned with the difference between two colours. In colourfastness testing the latter is important, since the tests are based on the difference between the original, untested material and the material that has undergone the test procedure, or the difference between an originally unstained (white, adjacent) fabric and the stained equivalent after testing. Colour difference measurement is a large subject. However, it has been studied more extensively for acceptability testing (the acceptance of a coloured item when compared to some standard colour) than it has for fastness testing (the determination of the results of a colourfastness test).

Like colour, colour difference can be communicated in one of three ways:

1 words,2 by reference to physical colour standards, or3 in numerical terms based on numerical expression of light, objective and

observer characteristics.

The expression of colour difference in colourfastness testing is no exception, but the emphases are somewhat different than in other applications of colour communication.

Words are inadequate. While trained observers can reliably use words to communicate the quality of a colour difference (lighter, duller, bluer, for example), providing the difference is not too small (Revels 2007), they cannot communicate the size of a colour difference in a way that would allow for good decisions on acceptability to be made, or to indicate whether a fastness result is a pass or fail.


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9.7.1 Physical colour standards: grey scales

The use of physical colour difference standard pairs against which to judge a colour difference provides a study in contrast between colourfastness testing and the judgement of the acceptability of a colour submitted for approval by a dyer. Historically, a standard-batch difference would be judged by the customer, and communicated to the dyer as a pass or a fail with comments (‘too dull’, ‘too blue’, etc.), since the colour difference has to be expressed more in a qualitative way, and only one quantity of difference (the pass-fail boundary) is involved. When a colour is produced across a range of formats, and in very large quantities (one may think, for example, of Coca Cola’s red colour that should be consistent on bottles, cans, trucks, advertisements and so on) it is reasonable to produce a standard colour sample along with ‘limit’ standards that represent the limit of lightness, dullness, blueness, etc., so that anyone supplying the colour may know if what they are making is acceptable. Most colours, especially those produced on textiles for fashion, are produced in limited quantities, so the development of limit samples is unrealistic, and the use of physical standards to defi ne colour difference in pass-fail judgement of colour submits has been rare.

In contrast, colourfastness testing is less concerned with the quality of the difference, and is more concerned with the size of the difference, and thus the use of physical standard colour difference pairs has long been standard practice in fastness testing. Furthermore, the critical size of difference from one situation to another may vary, so these physical standards need to include a range of difference pairs, from small to quite large. Thus the use of such physical standards of colour difference has been standard practice in colourfastness testing since the late 1940s as ‘grey scales’. They replaced verbal descriptions correlated with standard dyeings that had a range of fastness properties and against which the material under test could be compared.

ISO introduced their grey scales in 1948 and they represented a signifi cant technical breakthrough (SDC 1953). AATCC adopted these grey scales in 1954, and this version was distinguished by the inclusion of half-step difference pairs. The half-step version was adopted by ISO in 1974 (Hoban 1980).

There are two distinct grey scales. One is used to assess a change of colour, and the second is used to assess the extent to which a white material is stained. They are defi ned and their general use described in ISO 105-A02 and ISO 105-A03, and in AATCC’s Evaluation Procedures (‘EP’) 1 and 2. The grey scales for colour change (see Fig. 9.1.) consist of pairs of neutral grey chips, one of the pair being a constant grey colour, with the second ranging from the same grey through a series of lighter greys arranged in order of increasing difference. The pair with no difference is labeled ‘5’, and the increasingly different pairs are 4, 3, 2 and 1. The half-steps in the grey scales are labelled 4-5, 3-4, 2-3 and 1-2.

The Gray Scale for staining is similar, except that one of the pair is a constant white, and the second ranges from the same white through a series of increasingly


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darker greys. Again, the pair with no difference is labelled ‘5’, and the increasingly different pairs are 4, 3, 2 and 1. The half steps are labeled 4-5, 3-4, 2-3 and 1-2 (see Fig. 9.2).

The half-steps in the grey scales are often quoted with a decimal place (4.5 and so on). Such a designation was warned against (Hoban and Stone 1974) and is somewhat controversial, since it implies that the numbers on these scales are validly subject to mathematical manipulation (averaging, and so on). This is arguable, since a DE vs. GS plot (used originally to derive the colorimetric values of the half-steps) is not linear, and the numbers on the grey scales are more properly regarded as labels only. Nonetheless, when multiple assessments are made, results

9.1 The AATCC Gray Scale for Evaluating Change in Color (By permission, American Association of Textile Chemists and Colorists.)


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are often quoted as a numerical average of the ratings, and are occasionally given as ratings that do not occur on the scale itself, such as 3.8, for example.

The chips (and the differences between them) are carefully defi ned, so that the suppliers can perform quality control checks before they are sold, and users can check that their scales are still within tolerance and able to provide good data. Originally the differences were defi ned by their ANLab colour difference, but the development of the CIELAB formula in 1976 prompted its adoption by ISO and AATCC to defi ne the differences in 1977 (Hoban 1980).

In addition to the two basic grey scales, for colour change and for staining, AATCC has produced a ‘chromatic transference scale’ (see Fig. 9.3). This is used mainly in the assessment of the staining produced in crock fastness tests. These test specimens are stained in a circular pattern and the scale has several series of chips of different hues, each series starting with white and successive chips having greater chromaticity. Each chip pair has a circular opening through which the test specimen is displayed. Originally produced as a fi ve-step scale and described by AATCC EP3, a nine step (with half steps included) scale was introduced in 1996 and is covered by AATCC EP8. While this scale may seem an additional complication in the grey scale scene, the evaluation procedure that describes its use makes clear that the scale is based on the grey scale for staining, and that in the event of disputes, ratings using the staining scale should prevail.

9.2 The AATCC Gray Scale for Evaluating Staining. (By permission, American Association of Textile Chemists and Colorists.)


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The correct use of each of these scales is described in the ISO and AATCC documents. The angle of viewing, the quality and intensity of the illumination, the masking of the test specimen/grey scale pair are specifi ed to eliminate variables and increase inter-laboratory precision. Nonetheless, studies have shown that inter-laboratory results do vary systematically, especially from country to country.

Over time, minor changes to grey scales are introduced and are properly subject to studies to ensure that the changes have no effect on their use. The grey scales were originally in a slide rule format, but in 1974 were changed to a fold-out scale that allowed more direct alignment of the scale with the colour difference being assessed. A study found that evaluations with the new format were equally accurate and reproducible, and the format was preferred by those in the study (Hoban and Stone 1974). More recently, a black background and sleeve has been joined by versions with grey backgrounds and sleeves. A study prompted by this change found that grey scale results were not affected by the background colour of the viewing cabinet. When specimens were judged with the background colour of the mask in view, the black sleeve produced signifi cantly higher grades than the grey sleeve. However, when the grey scale pair and specimen pair were correctly masked by a sleeve of the same colour as that used with the grey scale, the sleeve colour does not affect the grades obtained (Westland 2001).

While the use of physical colour difference standards relies on comparison by an observer and is thus partly subjective, it is more objective than purely visual judgements with the results communicated verbally. Such partly objective evaluations using grey scales has been the norm for fi fty years. Meanwhile, the limited usefulness of physical samples in quantifying colour difference for

9.3 The AATCC Chromatic Transference Scale. (By permission, American Association of Textile Chemists and Colorists.)


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making pass-fail judgements of colour submits was recognized even before the development of grey scales, and is refl ected in the many efforts to develop numerical colour difference formulae. As reliable formulae (and better spectrophotometers) have been introduced, the colour acceptance world has moved to use them in preference to visual judgements, especially since they allow pass-fail judgements to be made over long distances. The colour fastness world, however, has largely continued with the use of grey scales and visual comparisons to express the results of a colourfastness test, despite the skill and experience required to perform the evaluations reliably and reproducibly.

9.7.2 Instrumental colour measurement of colourfastness test data

As is implied by the defi nition of the grey scales themselves in colorimetric terms, a colour difference equation can reproducibly express the colour change or staining resulting from a fastness test. It would seem both simple and logical to adopt one such equation directly to provide completely subjective assessment of the colour change or stain occurring in a colourfastness test. However, given the extensive and well-established use of grey scales, it is essential that a colour difference equation used in this way generates the same results as do visual evaluations with the grey scales so that the visual and instrumental assessment methods can exist and be used side by side. Unfortunately, this is not the case, and general colour difference formulae do not provide close enough correlation with visual grey scale ratings. Part of the problem is the large range of colour differences that grey scales include and must be handled by such an equation, when compared to the small range of colour differences involved in acceptance testing. As the technology for colour measurement has become more widely available and more readily accepted, extensive studies have addressed this problem, and have derived and tested a range of formulae that provide closer correlation to colour differences in fastness tests, and which are fully objective and thus more precise. Obviously, given the two different grey scales, for colour change and for staining, two separate formulae are required. The underlying need for these formulae was recognized almost thirty years ago (Hoban 1980). The effect of different means of measuring the results of a colourfastness test on the test’s overall precision is discussed in section 9.5.

The development of a colour change formula to correspond to grey scales was discussed in TC38 of ISO in the 1980s. Two formulae were considered in depth, the UK suggesting the use of CMC (currently ISO 105-J05, AATCC 173), and the Swiss delegation offering a purpose-made formula that eventually was adopted as the ISO standard 105-A05 in 1989. The formula was subsequently adopted by AATCC as EP 7 in 1995.

The formula is based on fi rst calculating the CIELAB Lch values for the reference specimen (L*R, C*abR and habR) and for the test specimen (L*T, C*abT and habT)


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to two decimal places using the CIE 1964 10° supplemental standard observer and illuminant D65, with the 1931 2° observer and illuminant C being permitted alternatives. The fastness colour difference ΔEF is then calculated using the formula:

ΔEF = [(ΔL*)2 + (ΔCF)2 + (ΔHF)2]½

where

ΔHF = ΔHK/[1 + (10 CM/1000)2]ΔCF = ΔCK/[1 + (20 CM/1000)2]ΔHK = ΔH*ab – DΔCK = ΔC*ab – DD = (ΔC*ab · CM · e-x)/100

(subscript M refers to the mean of the reference and test specimens)

x = [hM – 280)/30]2 if |hM – 280| ≤180 orx = [(360 – |hM – 280|)/30]2 if |hM – 280| >180.

The grey scale grade for colour change can then be calculated using the formula:

GSc = 5 – ΔEF /1.7

when ΔEF ≤ 3.4.

GSc = 5 – Log (ΔEF/0.85)/Log(2)

when ΔEF > 3.4.This ΔEf value requires interpolation to generate the appropriate one of the nine

possible grey scale values. Alternatively, the grey scale value can be read directly from a table of ΔEF values.

A later study compared the performance of this formula, the original UK proposal, plus CIELAB, and two new formulae developed by Teraji and co-workers (referred to as Nc

# and Fc) that give grey scale values directly (Sato et al. 1997a). The paper found that the existing ISO formula and the Fc formula were equally the best. Interestingly, it found that CMC(1:1) was better than CMC(2:1), suggesting that fastness testing is perceptibility based, rather than acceptability based.

A proposal for an ISO staining scale formula was put forward in 1983 (Anon 1983) and was reported to give 90% agreement (i.e. the formula provided a rating closer to the average observer rating than a given individual judgement). It was subject to a fi eld trial in which is was found that the results had a systematic dependence on the hue being assessed, and that overall, the formula produced satisfactory results in less than 60% of the samples examined by 14 observers in four locations, and it was suggested that further modifi cation was required (Kuehni 1984). An alternative formula proposed by a German committee was found to be more satisfactory and was adopted as ISO 105-A04 in 1987. It is currently under consideration by AATCC as an evaluation procedure.

The formula is based on fi rst measuring the refl ectance of the stained and unstained materials and calculating the CIELAB color difference ΔE* and the


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magnitude of the lightness difference ΔL* between the stained and unstained reference to two decimal places, using the CIE 1964 10° supplemental standard observer and illuminant D65, with the 1931 2° observer and illuminant C being a permitted alternative. The Gray Scale difference (ΔEGS) is then calculated from the following equation:

ΔEGS = ΔE* – 0.4 * [(ΔE*)2 –(– (ΔL*)2]½

The staining-scale grade (SSG) is then calculated to two decimal places using the following equation:

SSG = 6.1 –1.45 * ln(ΔEGS)

If SSG is greater than 4, it is recalculated using the following equation:

SSG = 5 –0.23 * ΔEGS

The Gray Scale for Staining Grade (GSs) to be reported is determined from Table 9.1.

Work continues to fi nd better formulae. As with the ISO colour change formula, it has been compared to the UK formula, CIELAB, and two more recently derived staining formulae, Ns and Fs (corresponding to Nc and Fc referred to above) (Sato et al. 1997b). The Fs formula was found to be marginally the best based on the data used in the study. While these studies suggest that improvement is possible, a modifi ed formula would probably need to provide substantial improvement and be subject to widespread trials before it is adopted.

9.7.3 The use of digital camera-based systems for colour fastness test measurement

The formulae above were developed for use with a spectrophotometer. A further reason for the slow adoption of instrumental measurement of colourfastness test results is the generally small size of multfi bre adjacent fabrics, the occasional

Table 9.1 Gray scale for staining step values

Calculated SSG Reported GSs

5.00 to 4.75 5–4.74 to 4.25 4–54.24 to 3.75 4–3.74 to 3.25 3–43.24 to 2.75 3–02.74 to 2.25 2–32.24 to 1.75 2–1.74 to 1.25 1–2Less than 1.25 1–0


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unevenness of the staining, and the length of time (per specimen) needed to make spectrophotometric measurements when compared to visual assessments.

A colour measurement system based on a digital camera (DigiEye) has been developed. The proposed advantages of such a system include speed, the ability to select for measurement the most uniform area of a stain, and the ability to assess multiple colours within a printed pattern, for example. Additionally, in one captured image may be multiple measurement areas that can give results, instead of carrying out multiple spectrophotometric measurements. It has been shown to measure colour and colour difference in a highly satisfactory manner. The extension of its use to measure colourfastness test results has been described (Cui et al. 2003a). In this study a range of colour change and staining sample pairs were measured both with a spectrophotometer and with the DigiEye, and also evaluated visually by multiple observers and multiple laboratories. The study found that the colour change pairs (many of which were heavily textured) gave different results when DigiEye and spectrophotometer results were compared, with the DigiEye results corresponding more closely to the visual observations. The DigiEye gave results that were close to the inter-observer error, and slightly worse than the inter-laboratory error, but this was achieved with single measurements: measurement averaging and optimization of measurement area selection would be expected to improve the performance. In the stained pair assessment, the DigiEye and spectrophotometer agreed well.

A further observation from this study was that the current ISO formula for the instrumental measurement of staining did not correlate well with visual data, suggesting that the formula is in need of revision. The study examined a simple corrected formula that performed better. A subsequent paper described in more detail the development of an improved staining scale formula (Cui et al. 2003b). Three formulae were developed and compared using three data sets with a total of 601 staining sample pairs. The conclusion was that a formula based on CIEDE2000 was the best, with errors of prediction less than inter-lab and inter-observer, and repeatability close to that of both the DigiEye and spectrophotometer. Its overall performance was 60% better than the current ISO formula.

Given the promise of this work, further studies (Cui et al. 2004a) examined the ISO colour change formula using the same technique of comparing inter-observer and inter-lab visual assessments with spectrophotometric and DigiEye instrumental measurements on a large data set. A new and simpler colour change formula was developed, like the proposed new staining formula described above, based on CIEDE2000.

The SDC’s Technical Coordination committee (TCI/81) sponsored an inter-laboratory study (12 labs and 38 observers) to examine these two new formulae, referred to as GRS and GRC, and the use of the digital imaging system (Cui et al. 2004b). Both formulae performed well, and the errors in digital measurement were encouragingly smaller than inter-observer errors. Based on this work,


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ISO/CD 105-A11 (‘Determination of colour fastness grades by digital imaging techniques’) is under development.

9.8 Conclusions

Colour is an important property of many items, and its durability in items with a life expectancy beyond a few weeks is a matter of concern. Textile colorants have the greatest potential to produce unwanted changes in the textiles they colour, and in adjacent materials, and textiles are subject to the greatest range of potentially damaging agencies. Thus colourfastness testing is most widely studied and performed on textiles. The tests refl ect real life challenges met in textile processing, in use, and in care, and many of them are accelerated. ISO and AATCC are the most active organizations in the development of such tests.

The tests necessarily include a measurement of the changes produced by the test challenge, and the stains caused by removed colour. For half a century, the measurement has been based on visual comparison with standard grey scales. Compared to other situations involving colour assessment, the adoption of instrumental methods to assess fastness results has been slow. Standard methods for such instrumental assessments are in place, if yet to be widely adopted. Meanwhile the use of systems based on digital cameras have prompted further study, indicating that rapid, effi cient, objective and precise fastness assessment is within reach, and, incidentally, suggesting better formulae for use in such assessments.

9.9 References

AATCC 2009. https://www.aatcc.org/testing/tm_main/index.htm – accessed July 2009.Anon, 1983. Instrumental Assessment of the Staining of Adjacent Fabrics: ISO Test Under

Consideration, J.S.D.C., Vol. 113 (3), 101.BSI 2002. British Standard BS EN ISO 105-X12:2002 Textiles – Tests for colour

fastness – Part X12: Colour fastness to rubbing.Cui, G. et al. 2003a. Grading textile fastness. Part 1: Using a digital camera system,

Coloration Technology, Vol. 119 (4), 212.Cui, G. et al. 2003b. Grading textile fastness. Part 2: Development of a new staining

fastness formula, Coloration Technology, Vol. 119 (4), 219.Cui, G. et al. 2004a. Grading textile fastness. Part 3: Development of a new fastness

formula for assessing change, Coloration Technology, Vol. 120 (5), 226.Cui, G. et al. 2004b. Grading textile fastness. Part 4: An inter-laboratory trial using

DigiEye systems, Coloration Technology, Vol. 120 (5), 231.D’Andrea, C. 2009. Colorfastness to light: fi nding an L4 equivalent, AATCC Review, Vol.

9 (6), 43–47.Guthrie, J. et al. 1995. A novel approach to lightfastness testing, JSDC, Vol. 111, 220–222.GOTS 2009. Global Organic Textile Standard. http://www.global-standard.org/ – accessed

July 2009.Hoban, R.F. 1980. Color measurement in fastness evaluation, Text. Chem. Col.,

Vol. 12, 33.


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Hoban, R.F. and Stone, R.L. 1974. New Gray Scales simplify colour assessments, Text. Chem. Col., Vol. 6 (9), 38.

ISO 2009. http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_tc_browse.htm?commid=48172

Jaeckel, S.M. 1980. The variability of grey scale assessment and its contribution to the variability of a test method, JSDC, Vol. 96 (10), 540.

Kuehni, R. 1984. Field trial of the British ISO proposal for the instrumental assessment of the degree of staining, Textile Chemist and Colorist, Vol 16 (4), 22–23.

Oekotex 2009. International Oekotex Association. http://www.oeko-tex.com/OekoTex100_PUBLIC/index.asp – accessed July 2009.

Patton, J. 1989. Crock test problems can be prevented, Text. Chem. Col., Vol 21 (3), 13.Revels, C. 2007. Describing color differences: How good are your color comments?

AATCC Review, February, 40–44.Sato, T. et al. 1997a. Comparison of instrumental methods for assessing colour

fastness. Part 1 – Change in colour, JSDC, Vol. 113 (1), 17.Sato, T. et al. 1997b. Comparison of instrumental methods for assessing colour

fastness. Part 2 – Staining, JSDC, Vol. 113 (12), 356.SDC 1953. Colour Fastness Test Coordinating Committee, JSDC, Vol. 69, 404.Smith, P. 1994. Colour fastness testing methods and equipment, Review of Progress in

Coloration, Vol. 24, 31–40.Thiry, M. 2009. The anatomy of a test method: The development of AATCC test methods,

AATCC Review, February, 26–31.Westland et al. 2001. Effect of sleeve colour and background colour on change in colour

assessments, JSDC, Vol. 117, (3), 123–126.

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Part II

Colour measurement and its applications

219


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221

10Colour measurement methods for textiles

N. S. GANGAKHEDKAR, Compute Spectra Colour Pvt. Ltd., India

Abstract: In textile dyeing application one has to produce consistent lots of dyeing. Colour measurement technique helps us to quantify colour in terms of numerical values. The need for a numerical pass/fail program arises from the fact that visual evaluations of shade difference are not consistent when establishing the fi ne line between acceptable and unacceptable dyeing. In this chapter, we will discuss the role of colour measurement in textile application with reference to colour measurement techniques, acceptability limits for pass or fail, importance of spectral match, use of on-line and non-contact colour measurement systems, colour of dry and wet fabrics, and inspection of colour of fi nished fabrics.

Key words: colour measurement techniques for textile application, numerical pass/fail program for textile dyeing, non-metameric matches, colour of dry and wet fabrics, inspection of colour of fi nished fabrics.

10.1 Introduction

In the production of textile products generally a designer creates designs using available colour palettes and the colours in the design are converted into easily communicable numbers. These are then supplied to textile mills or production units along with physical samples for producing the required quantities of material in the specifi ed colours. The textile production unit then creates lab dips that are sent for approval where the criteria for pass/fail based on possible colour differences in the dyed lots are worked out and agreed upon. For production of lab dips the production unit uses the recipe prediction methods and also works out the recipes of lowest cost with the least or no metamerism. The bulk supply is then tested as per the agreed pass/fail criteria, using the specifi ed colour difference equation, and the fabric is then sent to the converters for production of products that may be garments or made-ups, etc. The converter uses shade sorting methods to classify the supplied fabrics to avoid perceptible colour differences in the fi nal textile products.

Hence, for the textile industry it is important to understand how to measure colour so as to specify it in terms of communicable numbers, work out the least metameric recipes and quantify colour difference and set up a pass/fail criteria. Some of these aspects have been covered in previous chapters. This chapter will mainly focus on the colour measurement techniques for textiles, on-line colour measurement for in-process quality control, colour of dry and wet fabrics, expression of colours as numbers and its specifi cation, problems of metamerism, measurement of colour difference and setting up of pass/fail criteria and present a case study. Metamerism is one of the main reasons for not obtaining an acceptable


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matching and we will see how to get a non-metameric match. We will discuss recent advances in on-line and non-contact colour measurement systems which are being used for colour of wet fabric. As we know that fi nishing chemicals and processes affect the fi nal colour of the fabric in production batch, colour variations are noticed between lab dip sample and production batch. We will present a case study which explains the role of the fi nishing process.

10.2 Colour as numbers

Colour is light. Colour is dye. Colour is eye. Colour is sensation. Colour is information. Colour is mystery. Colour is subjective. Poor colour memory, eye fatigue, colour defi ciencies (colour blindness) and viewing geometries cause problems in visual colour assessment. Visual judgement of colour is based on physical, physiological and psychological aspects. Munsell was the fi rst who specifi ed colour in terms of three attributes: hue, value and chroma (Munsell 1929). Our eye cannot detect differences in hue, chroma or lightness equally, which means our eye is not equally sensitive to all colours.

The purpose of colour science (Wyszecki and Stiles 1967) is to express colour as numerical values. For arriving at these colour numbers, we have a triplet composed of the light source, the object and the observer.

Measured colour of the object is a function of light source (E), the characteristics of the material of the object (%R) and colour response functions (r, g, b) of the observer.

Colour = f(E, R, r-g-b) [10.1]

where E = energy of the light source R = refl ectance of the object r-g-b = tristimulus response of the human observer.

Objective measurement of colour is now based on quantifi cation of light source (E), refl ectance of the object (%R) and colour response functions r-g-b of the observer (Wright 1958). This triplet is the basis of colour science and CIE mathematics describes colour in terms of tristimulus values (X,Y, Z) derived from the source-object-observer triplet and CIE L,a,b or CIE L,C. h values express colours in terms of colour as numbers. This is discussed in detail in earlier chapters. The colour attribute of material is nothing but refl ectance and which is re-emission ratio of re-emitted energy (ER) and incident energy (EI).

Colour Attribute %R = (ER/EI). [10.2]

It is the fi ngerprint of a colour and every colour has its characteristic refl ectance signature. Two colours are perfectly matched if their refl ectance curves are exactly the same or the tristimulus values are the same. The Munsell system (Munsell 1929) is a visual system and the CIE system is independent of observers. The correlation is now found between the two and Munsell hue, value and chroma

Colour measurement methods for textiles 223

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can be correlated to CIE dominant wavelength, lightness and purity (Billmeyer and Saltzman 1981). This colour theory is discussed in details in earlier chapters.

10.2.1 Colour and appearance

The overall appearance of any object is a combination of its chromatic attributes and its geometric attributes (like gloss, shape, texture, shininess, haze, and translucency). Thus, both types of attributes should be measured and accounted for when making visual or instrumental assessments of colour and appearance (Hunter 1975).

10.2.2 Chromatic attributes

Chromatic attributes are those attributes associated with colour. They are divided into three components: lightness – L, hue – h and saturation – C.

A spectrophotometer measures the chromatic attributes of appearance, but the chromatic attributes of an object can never be completely separated from its geometric attributes.

10.2.3 Geometric attributes

Geometric attributes are those attributes associated with distribution of light from an object. For instance, a fl at cotton weave fabric is very different geometrically from corduroy. Gloss, haze and directionality are three main geometric attributes. Orientation effect in yarn and loose fi bres and different weave structure cause colour assessment problems and it is the characteristic of a textile sample which causes it to look different, depending in which direction it is turned.

10.2.4 Colour versus appearance

Specular refl ection (light directed in an angle that is exactly opposite to the incident light or when the angle of incidence is equal to the angle of refl ection) is generally less than 4–5% of the total and is perceived by an observer to be the ‘shininess’ or ‘glossiness’ of the sample. However, in order to see the apparent colour of the sample, the observer must move their eye away from the specular and concentrate on examining the diffuse (scattered) refl ectance from the sample. These necessary viewing conditions have led to standardized test methods such as ASTM D 1729 ‘Standard Practice for Evaluation of Colour Differences of Opaque Materials’, where 0°/45° (or 45°/0°) (lighting/viewing) geometry, which avoids the specular, is recommended (Hunter 1975).

Therefore, the appearance of the colour has been affected by the scatter of the specular light. To a normal observer, a high gloss specimen would appear to have more chroma and be a darker colour than an identically pigmented specimen with lower gloss or increased surface texture.


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The role of a colour measuring spectrophotometer is critical and one has to look into the design features and select a suitable instrument for textile application.

Many materials used as standards for shade matching appear glossy when viewed from a particular angle. These materials include shiny paint chips, plastic panels, and magazine pages among others. When these types of materials are measured for new shade formulation or when samples are measured behind a glass plate, it is important to exclude the glossy refl ectance from the sample measurement. The glossy refl ectance – or specular gloss – is automatically removed when using a 45/0 instrument and can be removed from a diffuse/8 instrument by use of a specular gloss port. The gloss port is located at an eight degree angle opposite the lens port and opens automatically when selected during the calibration routine on most software programs. The specular gloss can be safely included when comparing two identical materials but may be excluded when comparing two materials with signifi cantly different gloss levels as long as the same measurement technique is used.

10.3 Colour specifi cation

All colours can be characterized by hue, that is, the dominant shade; by saturation, that is, how much colour of any hue is present; and by lightness, that is, the degree of lightness or darkness of a particular colour (Billmeyer and Saltzman 1981). Therefore, it is necessary to describe a different hue, saturation and lightness for each unique set of illuminant or observer conditions. By using a standard illuminant and a standard observer, the amount of light refl ected from any one object can be converted into the hue, saturation, and lightness descriptions for any colour. Additionally, a sample can be compared to any standard with these same three attributes. In 1976 the CIE adopted a standard method of calculating colour attributes, known as 1976 CIE L*a*b* (or CIE Lab) Colour Space (CIE 1976a). There are two other terms that are occasionally used to describe colour: a red/green colour difference or a yellow/blue colour difference. CIE Lab Colour Space assigns the designation Da* for a difference in red/green value and the designation Db* for a difference in yellow/blue value. The total colour difference between two samples is termed DE* and is equal to the square root of the sum of the squares of DL*, Da* and Db*. It uses the designations Dh* to signify a hue difference between a sample and a standard, DC* to signify a difference in saturation (or chroma) between a sample and a standard, and DL* to signify a difference in lightness between a sample and a standard. Thus, by using these three terms – h (hue), L (lightness) and C (saturation) – we can describe the attributes of any colour, or the difference between a sample and a standard.

10.3.1 CIE Lab colour space

In the CIE L*a*b* Colour Space the colour coordinates in this rectangular coordinate system are:


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L* – the lightness coordinatea* – the red/green coordinate, with +a* indicating red, and –a* indicating greenb* – the yellow/blue coordinate, with +b* indicating yellow, and −b* indicating blue.

The CIELAB colour difference, between any two colours in CIE 1976 Colour Space, is the distance between the colour locations. This distance is typically expressed as DE*, where:

DE* = [DL*2+Da*2+Db*2]1/2 [10.3]

DL* being the lightness difference, Da* being the red/green difference and Db* being the yellow/blue difference.

10.3.2 CIE LCh Colour Space

CIE L*C*h* Colour Space is three-dimensional, with colours located using cylindrical coordinates as follows:

L* – the lightness coordinate (the same as in L*a*b*)C* – the chroma coordinate, the perpendicular distance from the lightness axish* – the hue angle, expressed in degrees, with 0° being a location on the + a* axis,

continuing to 90° for the + b* axis, 180° for – a*, 270° for – b*, and back to 360° (or 0°).

Many CIE system users prefer the L*C*h* method of specifying a colour, since the concept of hue and chroma agrees well with visual experience. For those preferring to express colour differences in the CIE L*C*h* system, the following terms are utilized:

DC* being the chroma differenceDh* being the hue angle differenceDH* being the metric hue difference.

The metric hue difference (DH*) is the colour difference, in distance units, due to the hue angle (Dh*) difference. DH* is used in the total colour difference computation, where all terms are distances (not angles), as follows:

Chroma C* = [a*2 + b*2]1/2 and Hue angle h = tan-1 (b*/a*) [10.4]

CIE 1976 DE* and DL* differences are the same for any pair of colours whether using CIE L*a*b* or CIE L*C*h*.

The CIE L*C*h* system describes a colour in the three-dimensional CIE 1976 Colour Space, based on the L*, C*, and h* coordinates. Colour acceptability management using this system is similar to using CIE L*a*b*, except that chroma (C*) and hue angle (h*) are used in place of the (a*) and (b*) coordinates.

The CIE L*C*h* acceptability volume conforms better to the visual evaluation ellipsoid than is the case to CIE L*a*b*. Alignment of the acceptability volumes are typically the same although the shapes differ signifi cantly. Colours near the


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edges of the L*C*h* solid will often be calculated as acceptable although visually judged unacceptable.

10.3.3 CMC colour difference and tolerances

The CMC colour difference formula is based on the colorimetric principles of the CIE 1976 system. It is typically employed as a colour tolerancing system in industrial applications. CMC colour difference (DECMC), a modifi cation (transformation) of CIE L*C*h* colour difference, has proven to be a useful measure of the commercial acceptability of coloured products (McDonald 1987; The Society of Dyers and Colourists 1981).

CMC colour difference is often employed in pass/fail colour production applications, where a single numerical tolerance can be established and utilized to make acceptability decisions. An important advantage of CMC is that once a tolerance has been successfully implemented for a product, the same tolerance may prove applicable for other colours produced under similar commercial conditions.

10.4 Metamerism

Metamerism (CIE 1976b) is the phenomenon where colours of two objects are perceived to be the same under one illuminant, such as daylight (CIE D65), but are not the same under a different illuminant such as incandescent (CIE A). The goal of most manufacturers and suppliers is to minimize metamerism. Imagine that you have a shirt where the sleeves and body are metameric? In daylight the shirt appears as a single colour, but in cool white and incandescent the sleeves are a ‘different’ colour from the body of the shirt. To accurately evaluate the presence of metamerism in sample pairs, the samples must be evaluated in multiple illuminants. This can be done both visually and instrumentally (Alian Chrisment 1998). For instrumental assessments there are methods for calculating the degree of metamerism using the Metamerism Index (MI).

There are four types of metamerism:

• Source metameric: two objects do not match when source is changed (D65, A, C, CWF).

• Object metameric: when dye combination is different in sample compared to standard.

• Observer metameric: when one observer says ‘match’ and another says ‘no match’.

• Instrument metameric: when you get different colour co-ordinates from two different instruments.

Metamerism always involves a pair of objects. The two objects can be described as ‘metameric objects’, or a ‘metameric pair’ and are sometimes said to be ‘metameric’, ‘exhibit metamerism’, and/or be ‘metameric matches’.


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10.4.1 Degree of illuminant metamerism: Indices

CIE Special Metamerism Index: Change in Illuminant – the colour difference (DE) between a pair of objects, under a test illuminant, assuming that the objects match (DE=O) under the reference (primary) illuminant.

10.4.2 Metameric and non-metameric matches

Colour is matched in daylight but it is not matched in artifi cial light. In Fig. 10.1 you can see that the X, Y, Z values are matched in D65 but not matched

TRISTIMULUS VALUES OF THE 10˚ OBSERVER

D65

Std

X 31.28

20.28

12.71

31.28

20.28

12.71

47.66

27.57

4..04

52.66

31.41

4.42

Y

Z

Sample Std Sample

A

100

50% R

0400

Sample

Std

Wavelength in nm 700

Spectral curve

Metamerism

10.1 Metameric match: colour is matched in daylight, but not matched in artifi cial light. The X, Y, Z values are matched in D65 but not matched in Illuminant A. There is no spectral match.


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in Illuminant A. Also look at refl ectance curves of the standard and the sample. As refl ectance curves are not matched colours are metameric.

Figure 10.2 and Fig. 10.3 show the metameric matches as the refl ectance curves are not perfectly matching even though colour difference (DECMC) is 0.61 (Fig 10.3). Even though it is a very small colour difference and within acceptable limits, visually colours are not acceptable. This shows that metamerism plays an important role and one should always get a spectral match.

Metameric objects exhibit the following:

• They have different spectral refl ectance values (spectral curves).• They match under at least one combination of illuminant and observer.• They do not match under at least one combination of illuminant and observer.

50

0400 500 600 700

Ref

lect

ance

in %

Spectral reflectance graph

Sample B

Sample A

Wavelength in nanometer

10.2 Metameric match: it is not a refl ectance match.

YellowdCIELab/CMC: D65-10

Spectral data

STD. BATCH

-FAWN STD -BATCH

–1.0

1.0

0.8

0.5

0.3

0.0

–0.3

–0.5

–0.8

–1.0

–0.8

ILL 1

Standard name: L* a* b* C* h°FAWN STD 61.21 4.20 5.51 6.93 52.70

I:c Ratio 2.00D65-10

–0.5–0.3 0.0 0.3 0.5 0.8 1.0Blue

Gre

en

Red

% R

(or

%T

)

3600

20

40

60

80

100

400 500 600 700 750

Trial nameBATCH

L*60.91

a*4.31

b*6.04

C*7.42

h°54.45

DL*–0.30 D

Da*0.11 R

Db*0.52 Y

DC*0.49 B

DH*0.22 Y

DEcmc0.62

DE*0.61

Wavelength (nm)

10.3 Non-metameric match of fawn colour fabric.


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Yellow

dCIELab/CMC: D65-10 -DK GREY STD -BATCH

–0.8

0.8

0.6

0.4

0.2

0.0

–0.2

–0.4

–0.6

–0.8

–0.6

ILL 1

L* a* b* C* h°29.23 1.52 1.65 2.25 47.39


–0.4–0.2 0.0 0.2 0.4 0.6 0.8Blue

Gre

en

Red

% R

(or

%T

)

360

Wavelength (nm)

0

20

40

60

80

100

400 500 600 700 750

L* a* b* C* h°28.71 1.19 1.89 2.23 57.69

DL*–0.52 D

Da*–0.33 G

Db*0.23 Y

DC*–0.01

DH*0.40 Y

DEcmc0.64

DE*0.66

Spectral data

Standard name:DK GREY STD

Trial nameBATCH

STD. BATCH

10.4 Non-metameric match of dark grey fabric.

10.4.3 Instrumental test for metamerism

1 Using a spectrophotometer, measure the objects, and confi rm that the objects match under a specifi c illuminant/observer combination (DE = 0).

2 Compare their refl ectance curves. If the curves differ, and cross each other at least three times, then the objects are metameric (Fig 10.2). You can see from Fig. 10.5 that refl ectance curves are not matched but colour difference is acceptable (DECMC = 0.60). Also see Fig. 10.6. It is a refl ectance match and

Yellow

dCIELab/CMC: D65-10 -OLIVE STD -BATCH

–1.3

1.31.00.80.5

0.00.3

–0.3–0.5–0.8–1.0–1.3

–1.0

ILL 1

L* a* b* C* h°43.06 0.65 6.96 6.99 84.63


–0.8–0.5–0.3 0.0 0.3 0.5 0.8 1.0 1.3Blue

Gre

en

Red

% R

(or

%T

)

3600

20

40

60

80

100

400 500 600 700 750

L* a* b* C* h°42.89 0.83 6.40 6.46 82.58

DL*–0.17 D

Da*0.18 R

Db*–0.55 B

DC*–0.53 D

DH*–0.24 R

DEcmc0.62

DE*0.60

Wavelength (nm)

Spectral data

Standard name:OLIVE STD

Trial nameBATCH

STD. BATCH

10.5 Non-metameric match with two crossings.


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Yellow

dCIELab/CMC: D65-10-DK BRWN STD -BATCH

–1.2

1.20.9

0.7

0.5

0.2

0.0

–0.2

–0.5

–0.7

–0.9

–1.2

–0.9

ILL 1

L* a* b* C* h°29.12 4.63 3.51 5.81 37.21


–0.7–0.50.20.0 0.2 0.5 0.7 0.9 1.2Blue

Gre

en

Red

% R

(or

%T

)

3600

20

40

60

80

100

400 500 600 700 750

L* a* b* C* h°29.18 5.04 3.86 6.35 37.46

DL*0.06 L

Da*0.41 R

Db*0.34 Y

DC*0.53 B

DH*0.03 Y

DEcmc0.55

DE*0.54

Wavelength (nm)

Standard name:DK BRWN STD

Trial nameBATCH

STD. BATCH

Spectral data

10.6 Spectral match – non-metameric match with DE 0.54.

Yellow

dCIELab/CMC: D65-10 -GREY STD -BATCH

–0.8

0.8

0.6

0.4

0.2

0.0

–0.2

–0.4

–0.6

–0.8

–0.6

ILL 1

L* a* b* C* h°31.03 0.05 –0.99 0.99 272.89


–0.4 –0.2 0.0 0.2 0.4 0.6 0.8Blue

Gre

en

Red

% R

(or

%T

)

360

20

0

40

60

80

100

400 500 600 700 750

L* a* b* C* h°30.18 0.40 –1.35 1.41 286.62

DL*–0.85 D

Da*0.35 R

Db*–0.36 B

DC*0.42 B

DH*0.28 R

DEcmc0.89

DE*0.99

Wavelength (nm)

Standard name:GREY STD

Trial nameBATCH

STD. BATCH

Spectral data

10.7 Spectral match but visually not acceptable.

colour difference is DECMC = 0.54. It is a good match. Figure 10.7 shows that it is a spectral match but colour difference DECMC is 0.99. It is a little higher and not acceptable. Visually the match is not acceptable as DL value is negative (DL = – 0.85). The colour being darker, it is not acceptable even though hue is matched. Spectral match is a non-metameric match, generally a good match but colour difference should be within tolerance limit. All above mentioned examples are of very small colour differences. A single-number pass/fail (DE) will not help us to decide acceptance or rejection. Spectral


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match and acceptable colour difference will correlate with visual assessment of colour (Gangakhedkar 2004b).

3 One has to see the metamerism and compute its amount, by calculating colour differences (DE > O) under different illuminant/observer combinations.

10.4.4 Reducing the effects of metamerism

Metamerism is a potentially important consideration in any colour control application involving the colour matching of two objects. Its effect can be minimized (or eliminated) in most colour production application (Gangakhedkar 1986b) by:

1 Utilizing exactly the same dyes in the formulation of the production object that have been used to produce the standard (target).

2 Selecting a production formulation that minimizes metamerism, whenever it is not possible to use the same dyes as were used to make the standard.

3 Substituting ‘working standards’ (made from the production formulation) for the original metameric standard, whenever possible in the control and acceptance processes.

4 Correcting production colours without adding any ‘new’ (different) dyes to the product.

In textile dyeing, this problem is the most common for getting a perfect match. One should not substitute or replace any dye in the original recipe.

10.5 Reasons why colours do not match

This is a question always raised. The following are the main reasons for not getting a good match.

• Colour is subjective: colour is a sensation, just like touch, and the colours you see are purely subjective, as interpreted by your visual system and your brain.

• Lighting affects colour: the colour of an item will vary depending on the light, so it will look different under incandescent light, fl uorescent light, and daylight.

• Colours affect colours: your perception of colour will change, depending on the colours around it, an effect called simultaneous contrast.

• Identical colours can be metameric: two colours are the same under one light but look totally different under another light.

• Different observers perceive colour differently: colour is within us and colour impressions are different for two different observers.

• The human eye is different: from spectrophotometer, camera, video display unit or scanner.

• Different devices have different colour gamut: colour monitors (video display unit of the PC) show colours that printers cannot print.


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• Different applications have different methods of colour matching: printing application is different from textile/paint application; colour television application is different from camera or scanning.

• Different devices use different colour models: printer, monitor, scanner or camera use different colour models such as RGB or CMYK model.

10.6 Visual versus numerical pass/fail

In “textile dyeing application” one has to produce consistent lots of dyeing. Colour measurement technique helps us to quantify colour in terms of numerical values, and using internationally accepted colour difference equations we can arrive at acceptable tolerance limits. A numerical pass/fail program is essential for communication between dyer and customer. The need for this program arises from the fact that visual evaluations of shade difference are not consistent when establishing the fi ne line between acceptable and unacceptable dyeing. Acceptability limits are to be defi ned using colour difference equations such as CIE DE2000 or CMC. The practical approach is to use acceptability limits defi ned by statistical analysis, correlated with visual acceptable matches.

A sample that is approved by one person may be rejected by another simply because they perceive colour differently. This is true not only for the dyer and a customer, but for dyers on different shifts, and even for the same dyer day to day. The end result is delay in the decision making process, not to mention the uneasiness produced when a borderline shade is approved. You can see from Fig. 10.5 and Fig. 10.6 that the colour differences are small and acceptable (DE < 1 unit) but visually samples are rejected. Samples in Fig. 10.3 and Fig. 10.4 show metamerism, so a numerical pass/fail is the objective solution for the colour matching problem (AATCC 1996; X-Rite 1994).

Another important question asked is what should colour tolerances be like? The solution is to use colour difference equations which quantify hue, brightness, and chroma. CIE LCh formula is to be preferred and one should opt for CMC or CIE DE2000 formulae (Luo et al. 2001). Once a customer has accepted a formulation (a lab dip sample) and production is started, it is necessary to control the process variables so as to maintain and deliver the colour desired. The decision whether the process is in or out of control demands the establishment of tolerances (lighter/darker, redder/greener and yellower-bluer). Acceptability limits are to be defi ned using colour difference equations CIEDE 2000 or CMC. It is established that the CIELAB colour difference equation is inadequate for many purposes – equal sizes of DE* correspond to different perceptual differences in colour. There is strong evidence to show that most of the modern optimized equations (such as CMC, M&S, BFD, and CIE 94) are more uniform than CIELAB.

The CMC (2:1) formula allows for the use of a single tolerance for all shades, so a pass/fail program based on CMC will be easy to implement or understand. The CMC tolerance will also correlate with visual assessment regardless of the


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colour being evaluated, making CMC an ideal choice for an instrumental pass/fail program (McDonald 1987).

The practical approach is to use acceptability limits defi ned by statistical analysis, correlated with visual acceptable matches. To successfully implement an instrumental pass/fail program, one must note that instrument pass/fail results must correlate with the visual perception of those involved in initially establishing an acceptable pass/fail tolerance and pass/fail results must be reliable and repeatable. To ensure that the tolerance formula that is actually implemented is accurate, it should be based on batch history gathered using repeatable techniques. Once an accurate pass/fail tolerance has been established, one can completely rely on numerical pass/fail (Ken Butts, datacolor).

Acceptability tolerances are usually established between supplier and customer, based on historical experiences and commercial factors. The CIELAB system is often utilized to help order and quantify the acceptability tolerances for each customer and colour combination.

10.6.1 Setting the pass/fail value

The pass/fail limit depends upon the equation that is used, but more importantly it also depends upon the application. The correct pass/fail value can only be determined from experience – pragmatically, the correct pass/fail limit is such that all pairs of samples with a colour difference less than this limit will be accepted by the customer.

When establishing acceptability tolerances, it is usually best to determine separate tolerances for DE* DL*, Da*, and Db* (or DE*, DL*, DC*, and Dh*). The separate tolerances allow the CIE system to be employed in acceptability applications, even as the customer acceptability criteria deviate from the uniform perceptibility of CIELAB Colour Space (Ken Butts, datacolor).

10.6.2 Effectiveness of CIELAB colour differences

Unfortunately several evaluations of CIELAB have shown that DE* is not a particularly good measure of the magnitude of the perceptual colour difference between two stimuli. The relatively poor ability of DE* to predict the magnitude of perceptual colour differences has led to more complicated ways of computing a colour difference from the CIELAB coordinates of two samples and some of these measures have been shown to be more reliable than the simple DE*.

Our eye cannot detect differences in hue, chroma or lightness equally, i.e. our eye is not equally sensitive to all colours. If we ask visual assessment of two closely matched shades of yellows, blues, reds or greens, we will fi nd that our acceptance limits are different for different hues. The author (Gangakhedkar 2003) collected two closely matched samples of different hues such as yellow, green, blue, purple. All these samples were visually passed by different observers responsible for colour


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assessment. When the same samples were measured and the CIE 1976 DE* value was obtained, it was noticed that acceptable limits varied from hue to hue (0.6 to 2.5 units, see Table 10.1). In the case of CMC formula DE, values were more or less close to 0.6 to 0.7 units. Generally, the observer will see the hue differences fi rst, chroma differences second and lightness differences last. This is the main reason for opting for colour difference formula based on L, C, h co-ordinates. CMC, M&S, BFD, CIE 94 and CIE DE 2000 equations are offering closer correlation with visual assessment as they are based on LCh colour space. If you would like to use CIELAB or FMCII colour difference equations, then you have to fi x three-dimensional tolerance limits. It works well. Some of the auto-tolerancing programs of colour system manufacturers refi ne the limits with statistical control based on input of visual judgement. The author has used such a program and found it satisfactory.

10.6.3 Deciding which colour difference equation to use

It is established that the CIELAB colour difference equation is inadequate for many purposes – equal sizes of DE* correspond to different perceptual differences in colour. There is strong evidence to show that most of the modern optimized equations (such as CMC, M&S, BFD, and CIE 94) are more uniform than CIELAB. It is not clear, however, whether any one of these new equations is signifi cantly better than the others. The CMC equation is a British Standard (BS 6923) and is being considered as an ISO standard.

Although there are several colour space options available for implementing a pass/fail tolerance program – none of which are absolutely uniform – results of observations indicate that tolerances based on (DECMC) produce computer pass/

Table 10.1 Acceptable limits of DE varying from hue to hue: a case study – pass/fail numerical standards for yarn/threads

Visually-close samples DE CIELAB DECMC

P. Green T-854 0.67 0.31Violet F-850 1.05 0.70Red T-22 0.96 0.42Maroon T-722 0.66 0.51Pink T-695 1.07 0.45Yellow F/511 1.84 0.67Golden Yellow F/533 3.82 1.30Lt. Grey T-821 1.03 0.45Blue T-401 0.28 0.15Tur. Blue F/629 1.29 0.67Orange F/629 1.07 0.53N. Blue F/627 0.65 0.64Black (1) 0.55 0.58Black (2) 0.42 0.48


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fail decisions that most closely duplicate visual assessment. To ensure that the tolerance formula that is actually implemented is accurate, it should be based on batch history gathered using repeatable techniques. Once an accurate pass/fail tolerance has been established, much of the guesswork may be taken out of day to day shade assessment. It is to be noted that one has to look at refl ectance curves for the non-metameric match.

Luo and co-workers carried out extensive work to test the performances of four colour difference formulae (CIELAB, CIE 94, CMC and CIEDE 2000) and their results showed that CIEDE 2000 formula outperformed CIELAB, CMC and CIE 94, and it is also more accurate than panels of observers (Luo et al. 2001).

10.6.4 The M&S equation

In the 1980s Marks & Spencer, in conjunction with Instrumental Colour Systems, developed their own in-house equations that are used in the textile industry. Research shows that there is little to choose between the CMC and M&S equations in terms of overall performance. The fact that the M&S equations have never been published has restricted their use (McDonald 1987).

Colour tolerances are specifi ed using the M&S colour difference equation M&S 89 with appropriate variations from the norm for particularly critical merchandise. All dyed colours are measured after conditioning in the Vindon Ultrasonic ‘conditioning cabinet’, a refrigerator-like device that standardizes the fabric to 65% relative humidity, 20° centigrade, with 30 minutes of exposure to D65 daylight. M&S also specify the lighting standards at the retail level so as to assure colour consistency throughout the process.

M&S asks suppliers to submit a refl ectance match between the given standard and the lot supplied, i.e. they would like to have a non-metameric match. M&S provide digital standard by giving refl ectance values the supplier has to use these digital standard to meet the numerical specifi cation of M&S. However, if the supplier is not using the same spectrophotometer which is used by M&S, it will create problems of instrument metamerism. M&S philosophy is to have a numerical specifi cation as an essential base. But virtual or digital sampling may never replace all physical samples, although it can help to eliminate a great deal of potentially wasted effort by concentrating and focusing attention on what they really want to buy.

10.6.5 Tips for instrumental pass/fail

Fred W. Billmeyer and Saltzman (1981) gave the following tips for adopting instrumental pass/fail.

1 Select a single method of calculation and use it consistently.2 Always specify exactly how the calculations are made.3 Never attempt to interconvert between colour differences calculated by

different equations through the use of average factor.


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4 Use calculated colour differences only as a fi rst approximation in setting tolerances, until they can be confi rmed by visual judgements and customer acceptability.

5 Always remember that nobody accepts or rejects colour because of numbers: the way the sample looks is most important.

6 Follow CIE recommendations.7 Look and Think.

10.7 Colour measurement techniques for textiles

The most accurate means of measuring colour requires a device called a spectrophotometer that detects small colour differences which are not registered by the eye. As we can accurately measure colour, measurement matters. Colour measurement is colour management and determines the colour attributes of the material. It is an instrument which specifi es colour in terms of a refl ectance curve. Spectrophotometers measure light refl ected from an object at each wavelength. This spectral data can be displayed numerically or in graph form (% R vs. wavelength). Spectrophotometers are ideal for measuring meta merism, the phenomenon in which two colours look the same under one light source, but different under another. Perfect colour match is a spectral match. If it is not a spectral match then it is called a metameric match. One can only identify this difference by looking at spectral curves of the standard and the batch.

The performance of any colour-matching system depends on the quality of the spectrophotometer used for the measurement for colour. Buyers and sellers of colour products are talking of numerical standards (refl ectance values) and expecting very tight colour tolerance limits (DE).

In a colour measuring spectrophotometer, there are three components – light source, light splitting device (grating) and light detecting technology (photo diodes). In earlier generation instruments, manufacturers were using different technologies. Today, almost all of them are using more or less the same tech nologies. One can measure refl ectance or transmission: generally, we measure colour by measuring refl ectance and only for dye solution do we use transmission mode.

10.7.1 Parameters for colour measuring instruments

When you are presenting any colorimetry data, you must mention (Gangakhedkar 1986a, RPI 1978a & b) the following instrument parameters

• Instrument model – make.• Geometry of instrument: d/8 or 0/45.• Specular component included (SCI) or Specular component excluded

(SCE) mode. The gloss covers a complex set of effects related to surface


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structure. The samples with intermediate gloss exhibit different measurement problems compared to those with very high gloss or no gloss, respectively. Colour matches of highly glossy samples are to be performed without gloss (SCE mode) and for SCI measurement, one uses gloss correction by subtracting the effect of gloss.

• Aperture size (large, small or ultra small area view).• UV included.• UV out.• UV fi lters: 400 / 420 / 440nm.• Gloss compensation (automatic/manual); surface correction (many programs

use Sauderson’s correction – using different values of internal and external refl ection coeffi cients).

• Instrument diagnostics.• Instrument metamerism.

Comparison of instrumental measurements is meaningless unless all above mentioned parameters are the same for the measurements being compared. There are instrument variables such as lamp technology, measurement interval, geometry, gloss, type of photo detectors and electronics. These parameters are mainly responsible for instrument metamerism.

10.7.2 Instrument considerations

Before measuring samples on the spectrophotometer, one must perform the following tests (Reid Clonts et al. 2006; RPI 1978a):

1 Instrument calibration with white and black tiles. 2 Calibration with white (100%) and black (0%). 3 Wavelength calibration is performed by the use of fi lters, exhibiting sharp

absorption peaks, and a set of known wavelengths. The calibration method depends on the chosen spectrophotometer. The check of photometric scale is performed by the use of neutral fi lters of known optical density. Photometric precision should be less than 0.1%.

4 Short term drift test: How well does the instrument measure what it supposes to measure? Infl uenced by the quality of the wavelength scale, optical components and photometric and wavelength calibration. Repeatability DE should be less than 0.15.

5 Long term drift test: How well does the spectrophotometer repeat the measurement of the same sample, usually over the time period of minutes and hours? Reproducibility should be less than 0.25.

6 BCRA tile test: At least once a month to check performance of instrument. Use of BCRA tiles or opal glass or didymium fi lters for determining the performance of any instrument will decide the performance quality of any instrument.


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7 Inter-instrument agreement: How well do different spectrophotometers of the same manufacture and model measure the same sample? This is very important while exchanging the data with the customers. Instruments of same type and same make should not show not-acceptable colour difference. There are large differences when instruments of different types are compared.

8 Sensitivity: Sensitivity of light for light and dark colours is to be checked properly.

9 Thermochromism : One has to check thermochromic effect if samples are heated due to light source such as tungsten lamp or photochromic effect due to UV fi lters for minimizing effect of UV energy of Xenon lamp. Red and orange dyed samples are especially sensitive to temperature variations and may cause diffi culties in precise colour measurements. The author has found a number of dyes and pigments showing this phenomenon during fi eld experience. Generally, increasing the temperature of a sample caused a fall in refl ectance values. It is worth pointing out that the refl ectance usually decreases as the temperature increases. Yellow, orange and red dyed samples are found to be affected most, leading to large colour differences, whereas grey samples usually do not show any clear thermochromism.

Because of thermochromism, new recommendations for colour measurements require the temperature during the measurement to be set to a predetermined temperature, e.g. (25+/−1) degree Celsius.

10 Absolute accuracy of instruments: Absolute accuracy is essential concerning the determination of tristimulus values.

10.7.3 Sample types: fi bre, yarn, fabric and knitted sleeves

Loose fi bre

Loose fi bre is especially diffi cult to measure repeatedly. A mass of fi bre placed at the port of a spectrophotometer tends to protrude into the sphere in much the same way as too many layers of a sheer material. Not only does this introduce error into the refl ectance measurement, but there is also the risk of loose fi bre falling into the instrument and interfering with the measurement process. One technique to eliminate these errors is to place a piece of optically clear glass against the view port before measuring (Fig. 10.8). It is critical that the instrument be confi gured to read in specular excluded mode (SCE mode) to remove the glossy refl ectance due to the glass (Hunter 1975, Hunterlab 2009).

Yarn

The sample form that allows the quickest yet potentially most variable measurement is yarn. The most common method for measuring yarn is to obtain a small skein and simply place it against a small measurement port and rotate the sample for two or three measurements.


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Other useful techniques for yarn measurement include winding the yarn around a card or tab (Fig. 10.7) and using specially designed devices with springs that clamp the yarn securely to a plate. Yarn tension is a concern in either case and must be controlled from sample to sample to prevent measurement errors.

Knitted sleeves

Knitted sleeves provide the most repeatable measurements because of their uniform construction and size. The samples can be read very easily with the spectrophotometer’s largest view port. A typical measurement technique involves four measurements using four layers of material. Tests may show that as few as two reads are suffi cient with fewer layers, but keep in mind that the deviation when repeating the sample measurement should be less than 0.15 (DECMC).

A repeatable measurement technique includes specifi cation of the number of layers of material to use, the positioning of samples, the number of measurements to make, instrument settings, and clear communication with the instrument operators.

It is known that one has to have multiple layers of samples so that colour data is consistent and not varying.

10.7.4 Sample preparation method

The author suggests the procedures and guidelines given by Hunterlab, a leading instrument manufacturer. You can see the details on their website. AATCC recommendations for textile materials are to be followed and are very useful standardization guidelines.

Yarn can be measured by using one of several sample preparation methods. One method for both bench top and portable colour measurement instruments is to measure the cone or package directly. In this case no sample preparation is

Card wound Compression cell

10.8 Sample holders: yarn holder, card winder, fi bre/powder sample holders card wound and compression cell.


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required; however a positioning device should be used at the instrument measuring port.

A second method is to prepare a wad of randomly oriented yarn large enough so that no light can penetrate through it and place it inside a container with a window in the bottom. Place a specifi ed weight on top of the yarn and measure through the glass.

A more consistent method would be to use a compression cell (see Fig. 10.8) in which a specifi ed amount of yarn is placed in the container and a specifi ed pressure is applied to a piston to compresses the yarn.

Since the yarn is being measured through a glass interface, the instrument will measure the sample as a duller colour than it actually is.

Another method that often gives measurements that correlate well with visual assessment is to wind the yarn closely and parallel on a card to a suffi cient thickness to prevent show-through.

Commercial card winders (see Fig. 10.8) are available for this purpose to ensure consistent tension and a constant number of windings. The card can be made of cardboard, plastic or metal and should be a uniform, neutral colour. Ensure that it does not contain fl uorescing agents.

Skeins of yarn can be prepared by clamping or taping one end to the edge of a rigid backing material, stretching the skein across the form keeping the strands of yarn parallel and clamping or taping to the other edge of the form. Commercially available skein holders can be used for this purpose.

The yarn can also be knitted into a sock for measurement. Since a knitted sock is fl at, it can be measured without a support or compression device. When necessary, to increase the opacity of the sample, the knitted sock can be folded such that multiple layers are produced. The number of layers can be increased until the reading does not change signifi cantly. One can see that colour data (L, a, b values) for two, four or six layers will change signifi cantly and will become constant after six or eight layers depending on the nature of the material. In the case of yarn measurement, sample directionality is the primary area of concern and one should rotate samples by 45, 90, 180 and 270 degrees and take average readings.

If a cone or package of yarn is being measured directly, and the sample is curved and does not lie fl at on the sample port, it is necessary to get reproducible readings with proper positioning technique. One can see the effect of orientation of yarn on colour values from Table 10.2.

Table 10.2 Effect of yarn orientation on colour values

Orientation L* a* b* DETaupe Horizontal 46.4 3.6 9.3Taupe Vertical 45.0 3.5 9.1 1.42Orange Horizontal 57.5 30.9 53.4Orange Vertical 56.4 30.4 53.1 1.24


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The best colour measuring instrument has no value if the samples presented for measurement are not accurate.

• If we prepare the samples the same way each time, then each sample will be the same as all previous and all subsequent ones. This is called the reproducibility of making samples. In no case did the duplicate samples exactly match one another. You may get the colour difference in the range of 0.04 to 0.6 CMC units. We must realize that in setting colour specifi cation we must have good repeatability of sample making.

• Sample surface differences: If there is a difference in surface appearance (e.g. gloss), then colour measurement will be inaccurate, gloss, texture and surface irregularity will create problems in colour measurement. Differences in surface gloss account for the colour difference.

• Samples made with different equipments will show large variations. Samples made by different operators will show considerable variations.

• Understand the limitations of sample preparation procedure. This should not be the cause of gloom. Instruments are unintelligent machines, which see only what we give them to see. In most cases an improperly prepared sample or sample preparation procedure has more errors in it than we realize. Careful sample preparation is a must and a prerequisite for correct colour measurement.

In textile colour measurement, orientation effect, unlevel dyeing, weave structure, light transmitting due to thin fabric, the sample size creates measurement problems and one has to look into sample preparation procedures (see AATCC 1996, Hunterlab 2009, Brockes et al. 1964).

10.7.5 Sample presentation method

Measurement technique

Before any permanent samples are measured and stored into the computer database, a repeatable measurement technique must be established and observed. Samples should always be measured multiple times with the largest area view available on the spectrophotometer being used as long as the samples are large enough to completely cover the viewing area. Sample conditioning should also be considered because variations in temperature and moisture content can contribute to variations in measurement data.

• All textile materials must be consistent and standardized for presenting to the port of the spectrophotometer.

• Fibrous materials must be carded into a suitable pad.• Top and tow should be straighten by combing treatment.• In case of yarn, it should be card wrapped, using a precision card winder.• For fabric samples, suffi cient layers should be made to prevent transmission

of light through a composite specimen.


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Sample thickness

Two to four layers will be suffi cient for most knitted and woven materials to achieve an opaque sample for presentation to the instrument. If the material is not opaque, light will pass through the sample and refl ect off the backing material or sample holder and produce misleading refl ectance data.

Sample positioning

Sample rotation and repositioning will reduce measurement variability due to fabric construction, directionality of yarns, and unlevel dyeing. A common practice in sample measurement is to place the sample at the instrument port and simply rotate the sample for four or more measurements. This technique is quick, but it will not account for variations due to unlevel dyeing and should be avoided.

Developing a repeatable technique

An optimum measurement technique has been established when a sample can be measured, removed from the instrument, and re-measured with a variation of less than 0.15 DE(CMC) units. Higher variation will decrease the confi dence level in the quality of the stored data and lead to less accurate match predictions.

Measurement repeatability is especially critical as it affects computer pass/fail programs in use in the quality control and fi nal inspection areas. If a sample is measured and the DE(CMC) is calculated as 0.80 but the measurement variability is 0.30, then the true reading can average from 0.50 to 1.10.

This may mean the difference between ratings of pass or fail if the pass/fail tolerance is less than 1.10. It is suggested that one should follow these recommendations:

• Wherever possible, the large aperture should be used to increase accuracy.• Aperture can be selected depending upon sample size.• Measure the sample at four different positions by rotating it by 90 degrees for

obtaining accurate average refl ection values.• Reproducibility of measurement should be within a colour difference of

DECMC =0.2 units.

10.8 On-line colour measurement

On-line colour measurement systems are recent systems for quality control of colorimetric features, production certifi cation, centre-selvedge/head-tail measurements and for lot formation. The measurements are implemented without interfering with the production process: an integrated system is able to implement, directly on production line, all the measurements relevant to the fabric colour in order to point out the processing mistakes as colour homogeneity, colour deformed areas, colour differences of ‘head–tail’ and centre-selvedge (see websites of X-Rite and Hunterlab).


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All leading instrument manufacturers such as Hunterlab, X-Rite, Orintex offer latest on-line colour measurement systems with different features. The author has worked on the Orintex on-line colour measurement system (Fig. 10.9) consisting of a GretagMacbeth spectrophotometer and Orintex software. This product is now discontinued.

Table 10.3 gives the data of a few samples for centre-selvedge. It gives all the colour parameters and remarks on pass/fail based on the tolerance limits specifi ed. Change in hue angle is to be watched if colour differences are very small and visual perception does not agree with instrumental pass/fail.

Salient features of any on-line colour measurement system are:

• Continuous, real-time measurement of any product on a process line, which allows us to respond to product colour changes when they happen, as they happen.

• Runs can be continuously monitored to identify colour variation and out-of-spec product. This information helps us make permanent process improvements,

10.9 On-line colour measurement: production equipment.

Table 10.3 Colour measurement of centre-selvedge

Sample L* a* b* h DE DL DC DH P/F hTAN STD 47.75 2.97 6.76 66.26TAN Left 0.29 0.28 −0.07 −0.00 PassedTAN Centre 0.29 −0.22 −0.05 0.17 PassesTAN Right 0.13 −0.04 0.06 −0.10 PassedKhaki STD 53.66 5.01 19.17 75.35Khaki Left 0.12 0.10 0.03 0.06 Passed 75.49Khaki Centre 0.07 −0.07 0.01 −0.02 Passed 75.31Khaki Right 0.08 0.06 −0.04 0.03 Passed 75.42Grey STD 38.47 1.61 −1.62 314.70Grey Left 0.43 0.10 −0.13 0.39 PassedGrey Centre 0.13 0.02 0.00 −0.13 PassedGrey Right 0.31 0.17 −0.14 0.22 PassedIlluminant D65 Colour Difference DE (CMC) P/F Limit 1.00


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while providing assurance that our product is within specifi cation and allowing us to make shipment and allocation decisions.

On-line systems help us in four major ways:

• Monitoring the fi nished product quality.• Making decisions about allocating product to a customer.• Monitoring the process variables that affect colour.• Helping us to control process variables.

10.8.1 Map control system

The latest on-line colour measurement system is a ‘map control’ system (Fig 10.10). The measurement instrument is a special spectrophotometer that is able to acquire remote refl ectance spectrum of the fabric surface to be controlled. The information data are sent to a PC that controls the plant and appraises the colorimetric data and gives all the necessary parameters to the operator.

Advantages of On-line colour measurement are:

• quality control can be programmed per client/product• automatic remote measurements• elimination of stop time

10.10 Map control.


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• automatic tolerances according to colour shade• operator’s objective testing via instruments and not subjective testing• testing according to international standards• documentation and certifi cates of colorimetric quality control.

It is possible to consider the colour difference at all measured points or to check the head-tail and/or centre-selvedge difference of the lot. The types of control that can be carried out by the system can be personalized by the user, as well as the various options of quality control, together with formulas CIELAB, CMC, DE2000.

• The operator can have complete history of measurement.• The user can also see at a glance the progress of measurements carried out and

deduces the presence of the zones, which are beyond tolerance.

10.8.2 Hunterlab SpectraProbe XE

This on-line colour measuring instrument has the following notable design features. It measures colour both across the product’s width and along its length. The colour of a product can vary in shade between width and length, which causes signifi cant product quality concerns. SpectraProbe XE increases your colour monitoring capability dramatically. Hunterlab’s leading-edge technology gives this remarkable colour measurement system the ability to simultaneously traverse the product and measure colour both across its width and along its length. It then reports any shade variation against a standard, giving you more product colour information than has ever before been available. In addition to the commonly used side-centre-side confi guration, many other traversing patterns are also available.

Due to its highly advanced optical design, the SpectraProbe XE spectrophotometer can measure the appearance of colour independent of sample surface texture, directionality and gloss.

10.8.3 Non-contact spectrophotometers

Leading manufacturers such as KonicaMinolta and X-Rite offer new generation non-contact spectrophotometers. They are very useful for wet colour materials, especially wet paint samples. In textile application they are used for on-line colour inspection, but as of date they are extremely costly and not much used in industry. However, on-line inspection is fi nding more interest. Some of the models available in the market are described below.

Minolta CF 1440 spectrophotometer

It is a unique non-contact spectrophotometer (Fig. 10.11) with large area view (40 mm), multi-point measurement and high speed. The optical system is highly


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adaptable to vertical movement or tilting of objects. Built-in auto-calibration functions guarantee stable measurements, even under long term use. Multi-point measurements are carried out in 0.6, 1.2 and 1.5 seconds and inter-instrument agreement for 12 BCRA tiles is De 0.3 units. Measuring distance is 100 mm from the bottom of the instrument, which uses a standard xenon arc lamp. The illumination light is also measured during measurement of the specimen, to correct the infl uence caused by minute fl uctuation in light quantity and spectral characteristics of the illumination light. The instrument is ideal for wet colour matching and widely used in paint application. Minolta has also introduced some more models. You are advised to see product details on the supplier’s website.

VeriColour Spectro

This new generation of on-line colour measuring instrument is offered by X-Rite and is accurate and affordable. The VeriColour Spectro is compact and durable for harsh industrial manufacturing applications and an important feature is no special lighting or shrouding required with this all-inclusive solution. It improves quality control and reduces operating expense, provides absolute spectral and colorimetric data for process control, and is easy to set up and manage. The VeriColour® Spectro system enables colour control in real time to contain and eliminate colour problems without disrupting production. X-Rite is also offering a wide range of models with many unique features. Notable models are VeriColour Solo, VeriColour System, and the TeleFlash 130 non-contact spectrophotometer. All the details can be obtained from the X-Rite website.

10.11 Minolta non-contact spectrophotometer.


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10.9 Colour of dry and wet fabrics

One can now measure wet samples and correlate them to dry sample measurements. This is a very recent development with notable success in the paint industry. It can be effectively used for textile materials, however one has to fi nd out the correlation between wet and dry measurements taking into consideration dyeing parameters such as the substrate-dye-process combination. Measurement of wet sample and colour quantifi cation is not a problem. It is well known that immersion of any dyed fabric into water creates a deeper shade. In order to explain this phenomenon, several researchers (Hero Tada Lida 1970, Goldfi nger et al. 1970, Allen and Goldfi nger 1971, Dalton et al. 1995) tried to solve the problem of colour appearance of fabrics in wet and dry states. They confi rmed that differences between wet and dry colour are due to the presence of the moisture content. Recently, extensive research was carried out at the UICT laboratory, Mumbai on correlation between wet and dry colour samples. Modifi cation of the Kubelka-Munk equation for colorant formulation for prediction of dry colour of a textile sample from its wet state was reported by Jahagirdar and co-workers (Jahagirdar et al. 2002). This study is very useful for understanding the correlation between the dry and wet colour measurement. In another study (Jahagirdar and Tiwari 2007), they reported prediction of dry colorimetric properties of dyed polyester fabric from its wet refl ectance values. Polyester fabric was dyed with six different disperse dyes at various concentrations. The refl ectance values of dyed polyester fabrics were measured over the visible range of 400–700 nm at two different conditions, viz. in wet and dry states. Using the refl ectance data, a numerical relationship between the wet and dry refl ectance was established. In this work, an attempt was made to predict the colorimetric properties of dyed polyester in the dry state directly from the corresponding wet refl ectance values. Jahagirdar and Tiwari predicted dry colour parameters of dyed polyester fabric from its wet refl ectance values. Figure 10.12 shows how one can use wet colour readings and predict refl ectance values of dry fabric by employing a 3D polynomial function. Refl ectance spectra of wet (Rw) and dry (Rd) are shown in the fi gure and refl ectance predicted from wet refl ectance values (Rd3D) using a 3D polynomial function is plotted and shows that it is very close to dry refl ectance value.

Jahagirdar and Tiwari developed an empirical relationship between dry and wet refl ectance values for the dyed polyester fabrics, so that the colour parameters of any dyed polyester sample in dry condition can be found out easily from their respective wet refl ectance values.

From their investigation, it was observed that the colour parameters of any dry polyester samples dyed with disperse dyes can be easily found from its wet refl ectance values by using the established 2-degree and 3-degree polynomial equation. But overall, the 3-degree polynomial equation holds good in comparison to the 2-degree polynomial equation. The empirical relationship between wet and dry refl ectance values can be applied as an analytical tool in order to display the dry colour of the polyester fabrics by monitoring its refl ectance values after dyeing.


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The work reported in their paper is found to be valid and tested only for polyester fabrics dyed with disperse dyes. Also, one can generalize such a relationship for any substrate by using the general equation:

Rd = AR3w + BR2

w + CRw + D [10.5]

where A, B, C and D are the variables and calculated from a curve fi tting method for the selected substrate. Rd and Rw are the refl ectance values of dry and wet fabric.

It is now possible to fi nd correlation between dry and wet fabric and predict the colour of the dry fabric from the wet fabric refl ectance measurement. One has to test these results for cotton dyeing with reactive and vat colours.

10.10 Inspection of colour of fi nished fabrics:

a case study

Generally, after the dyeing colour is assessed for pass/fail, fi nishing treatments are applied to the fabric such as enzyme wash, etc. It is noticed that there are considerable changes in the colour of fi nished products, as many times there is no correlation between original dyed fabric and fabric treated with fi nishing agents. One has to fi nd out the effect of the fi nishing agent on the colour of fabric and adjust the colour. It is very diffi cult to correct the colour if the fi nishing agent or fi nishing process is affecting the colour of a dyed fabric which was already approved before the fi nishing treatment was applied. Sometimes, it creates a problem of metamerism. Portable instruments or on-line measurement will help in controlling the colour of fi nished goods.

4000.0

0.3RwRdRd-3D

0.2

0.1

450 500 550 600 650 700

Ref

lect

ance

Wavelength (nm)

10.12 Refl ectance spectra of wet (Rw) and dry (Rd) predicted from wet refl ectance values (Rd3D) using a 3D polynomial function: polyester fabric dyed at 5% disperse blue 366.


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The author was associated with much work carried out at one of the leading textile mills in India. Some of the fi ndings are given here for understanding the role of fi nishing agents on fi nal colour. It was noticed that many international buyers approve the colour of the textile material before the fi nishing treatment is given. A considerable colour change was noticed after the fi nishing treatment was given to the fabric, which resulted in a not-acceptable matching which was not possible to correct as it had gone to the point of no return.

Table 10.4 gives details of enzyme wash and the Table 10.5 gives the colour data on textile colour samples – standard (un-wash) and trial (enzyme wash). These are

Table 10.4 Details of enzyme wash

MLR 1:5DesizeBactosol PHC I GPLWetting agent 0.25 GPLTemperature 50 deg CPh/Time 5.5–6.0/ 15 minutes

EnzymeBactosol ADF PDR 1 % OWM. Or 10 gm per kgPh 4.5–5.0Time 50 deg C

WASHCold wash 5 minutesHot wash 60–70 deg C / 10 minutesCold wash 5 Minutes

FinishCeraperm MW 15 GPLPh 5.0–5.5Time 10 minutes

Table 10.5 Finishing (enzyme wash) effect on colour data of textile material

Illuminant D65 (L:C) 2.00

Colour L a b C h DL Da Db DC DH DE(CMC)Khaki Std 53.71 2.87 10.06 10.46 74.06Khaki Trial 58.22 2.61 9.77 10.11 75.06 4.51 −0.27 −0.29 −0.35 0.18 2.04Grey Std 64.98 2.91 5.44 6.17 61.84Grey Trial 67.07 2.52 5.21 5.79 64.21 2.10 −0.39 −0.23 −0.38 0.25 1.01Khaki 2 Std 55.96 3.54 12.22 12.69 74.22Khaki 2 Trial 56.36 3.27 11.95 12.39 74.71 0.40 −0.19 −0.27 −0.31 0.11 0.33Carmel Std 56.07 8.81 18.30 20.31 64.30Carmel Trial 59.17 7.91 17.65 19.36 65.77 3.11 −0.86 −0.65 −0.95 0.51 1.63Olive Std 53.90 −0.56 6.34 6.36 95.03Olive Trial 54.75 −1,09 5.38 5.49 101.45 0.85 -0.53 −0.96 −0.87 0.66 1.29

Note: Std = Un-wash Trial = Treated with enzyme wash


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the materials supplied to international buyers, but the buyer specifi cations are very strict and they only accept the material if CMC colour difference is less than 0.7 to 0.8. The author has collected this data from JCT, Phagwara, Punjab, a leading textile process house in India exporting textile material to Europe and the USA.

All the standards were within acceptable limits before giving enzyme wash except the sample Khaki #2 and all other samples are showing more than one unit of CMC colour difference. It is in the range of 1.01 to 2.04. When one makes visual assessment, it is noticed that there is a considerable change in hue. One can see that olive and grey samples show considerable change in hue angle. DL, Da, Db values are showing directional changes. Total colour difference is not at all within tolerance limits and we noticed considerable spectral changes which clearly indicate that metamerism is introduced in samples after giving enzyme wash. It is not possible to make any colour correction at this stage. It can be concluded that fi nishing agents and the processes are to be carefully selected so that the metamerism problem is not introduced.

10.11 Future trends

As of date, on-line colour measurement and non-contact instruments are very costly, but new advances in optics and electronics will make spectrophotometers cheaper. New internet based computer colour matching systems will be available shortly and all the required colorant data will be stored on a central server, so match prediction of customer colour will be possible instantly. Low cost portable spectrophotometers integrated with internet based colour matching software will change the concept of colour production and colour marketing. ‘Colour on demand’ will be the marketing concept and using internet technology colour will be produced instantly. Acceptance of colour (pass/fail decision) will be communicated by the buyer and seller by ‘colour mail’ using the internet but the role of colour measurement will remain critical.

10.12 Conclusions

In this chapter, we have discussed the role of colour measurement in textile application with reference to colour measurement technique, relation between colour and appearance, on-line colour measurement systems, colour of dry and wet fabrics, inspection of colour of fi nished fabrics and how to get non-metameric matches. We have found the answers to frequently asked questions such as, ‘Why colours don’t match?’ and ‘What is the relation between colour and appearance?’

We discussed in detail various aspects of sample preparation methods, measurement techniques, instrument considerations and various parameters to be controlled for developing a repeatable technique for fi bre/yarn/fabric samples. We also discussed various aspects of visual assessment of colour and instrumental pass/fail criteria.


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Generally, colour is assessed for pass/fail after dyeing. For the majority of time, fi nishing treatments are given to fabric such as enzyme wash, etc. It is noticed that there are considerable changes in the colour of dyed fabric after the fi nishing treatment and one cannot make any correction if metamerism is introduced due to the process and fi nishing agent.


See web sites of colour instrument manufacturers such as datacolour, Hunterlab, GretagMacbeth (now X-Rite) and of the Musell Colour Laboratory of RIT, USA. Also refer to international journals such as Colour Research and Application and Journal of Society of Dyers and Colourists as well as various conference proceedings of AATCC, ISCC, AIC, CIE, SDC and different national colour societies. Research the Central Bureau of CIE, Kegelgasse 27, A -1030, Vienna, Austria (Email : [email protected]).

10.14 References

AATCC (1996), ‘1996 Technical Manual of the American Association of Textile Chemists and Colourists’, Volume 71, AATCC Test Method 173-1992: ‘CMC: Calculation of Small Colour Differences for Acceptability’, p. 315.

Alian Chrisment (1998), Colour and Colorimetry, Editions 3C, Paris.Allen E H and Goldfi nger G (1971), ‘The infl uence of moisture content on the colour

appearance of dyed fabrics’, Text. Chem. Col., 3, 289.Billmeyer, Jr., F W and Saltzman M (1981), Principles of Colour Technology,

2nd Edition, New York, Wiley.Brockes A, Stroca D, Berger A S (1964), ‘Colour Measurement in textile Industry’, Bayer

Farben Revue Special Edition No 3E, Farbenfabriken, Bayer-Leverkusen, Germany, Berger-Schunn.

CIE (1976a), ‘CIE Recommendations Uniform Colour Spaces, Colour Difference Equations and Metric Colour Terms,’ Supplement No. 2 to CIE Publication No. 15, May 1976.

CIE (1976b), ‘CIE Recommendation for Special Index of Metamerism’, Supplement No. 1 to CIE Publication No. 15.

CIE (1995), ‘CIE Technical Report: Industrial Colour Difference Evaluation,’ CIE Publication No. 116, Vienna, Austria, Central Bureau of CIE.

Dalton P M, Nobbs J H and Rennell R W (1995), ‘The infl uence of moisture content on the colour appearance of dyed textile material, Part 1’, J.S.D.C., 111: 285–287.

Gangakhedkar N S (1986a), ‘Colour Measuring Instruments (New & Old),’ Colourage, November 13, 1986.

Gangakhedkar N S (1986b), ‘Metamerism and Colour Difference’, Colourage, July 1986.Gangakhedkar N S (1991), Understanding Computer Colour Matching, Mumbai, Rutu

Prakashan.Gangakhedkar N S (2003), Understanding Science and Technology of Colour, Mumbai,

Rutu Prakashan.Gangakhedkar N S (2004a), ‘Colour difference-single number shade passing versus three

dimensional colour tolerance’, Jour. Col. Tech.& Mgmt., 1(1): 31.


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Gangakhedkar N S (2004b), ‘Colour clinic: metamerism’, Jour. Col. Tech. & Mgmt., 1(2): 33–37; 1(2): 51; 1(3): 32–33.

Goldfi nger G, Goldfi nger H S, Hersh S P and Leonard T M (1970), ‘The colour of absorbing scattering substrates, I The colour of fabrics’, J. Polymer Sci., C31 25.

Hero Tada Lida (1970), Senryo To Yakuhin, 3: 15.Hunter R S (1975), The Measurement of Appearance, New York, John Wiley

and Sons.Hunterlab (2009), Colour Measurements: Methods and Effects of Sample Presentation,

Hal Good, Reston, VA : http://www.hunterlab.comJahagirdar C J, Deshpande V D and Tiwari L B (2002), ‘Modifi cation of Kubelka-Munk

Equation for Colorant Formulation for Prediction of Dry Colour of a Textile Sample from its Wet State’, Colourage, 9: 51–58.

Jahagirdar C J and Tiwari L B (2007), ‘Prediction of Dry Colorimetric Properties of Dyed Polyester Fabric from its Wet Refl ectance Values’, Colourage, Volume LIV No. 1, pp. 37–44.

Ken Butts, ‘Colour Tolerances for Consistent Pass/Fail Decisions, Datacolor,’ http://www.datacolor.com

Luo M R, Cui G, and Rigg B (2001), ‘The Development of the CIE 2000 Colour Difference Formula,’ Colour Res. Appl. 26: 340–350.

McDonald R (1987), Colour Physics for Industry, Society of Dyers and Colorists – Dyers Company Publication Trust.

Munsell A H (1929), Munsell Book of Colour, Baltimore, Maryland, Munsell Colour Company.

Reid Clonts, Ranjith Thangavelu, David Hinks, Jennifer Dunn, Patricia Guzman, Ann Laidlaw, Warren Jasper (2006), ‘Inter-instrument agreement in the colorimetric measurement of textile materials’, AATCC Review, 6(8): 45–48.

RPI (1978a), Rensselaer Polytechnic Institute, Advances in Colour Technology, Topics, Experiments, Reprints, Bibliography, Rensselaer Polytechnic Institute, Troy, New York 12181.

RPI (1978b), Rensselaer Polytechnic Institute, Colour Technology for Management, Reprint, Bibliography, Department of Chemistry Rensselaer Polytechnic Institute, Troy, New York, 12181.

The Society of Dyers and Colourists (1981), ‘Colour Difference Measurement, A Reliable basis for Quality Control’, Papers of Conferences held at the University of Bradford on May 12.

Wright W D (1958), The Measurement of Colour, London, Hilger & Watts Ltd.Wyszecki Gunter and W S Stiles (1967), Colour Science, Concepts and Methods,

Quantitative Data and Formulas, New York, John Wiley and Sons, Inc.X-Rite (1994), A Guide to Understanding Colour Tolerancing, X-Rite, Inc. Publication,

X-Rite, http://www.x-rite.com

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253

11Grading of cotton by color measurement

B. XU, The University of Texas, USA

Abstract: Cotton color is an important measurement in judging fi ber’s spinning quality in the current cotton classing system. This chapter fi rst briefl y reviews the history and major factors of cotton color grading, and then discusses the instrumental methods, including HVI colorimeter and color imaging system, that has been developed for objective and reliable grading of cotton color.

Key words: cotton color, HVI colorimeter, imaging colorimeter, neural network, fuzzy logic

11.1 History of cotton color grading

The color of raw cotton is an important indicator of its overall quality because the lint color is related to processing performance and yarn quality.1, 2 Normally, cotton has a bright, white color but continued exposure to weathering and the action of micro-organisms may cause white cotton to lose its brightness. If frost or drought stops cotton growth prematurely, cotton may have a yellow color that varies in depth. The action of insects, fungi, and soil stains may result in cotton discoloration.3, 4 Traditionally, cotton colors are classifi ed by human classifi ers who are trained to compare raw cotton samples with the cotton reference standards, i.e. the USDA universal standards, to determine color grades on the samples.4 Because of the subjective nature and inconsistency in visual color classifi cations, the need for instrumental measurements of cotton color has been highly demanded since the early days of cotton color classing. In the 1930s, the USDA began developing instrument color measurements for cotton. The measurements of color Rd (grayness) and color +b (yellowness) became the standard cotton color attributes with the fi rst evaluations of the Hunter Colorimeter in 1950.5 In the 1970s, colorimeter technology was brought in for use in classifi cation, and was fully integrated into High Volume Instrument (HVI) systems by the end of the 1970s. Since then, 100% of U.S. cottons were tested by the HVI colorimeter and classifi ers, but the offi cial color grade was assigned by the classifi er. This practice maintained until the 2000 classing season. Since 2000, the offi cial color grades based on the Rd and +b measurements of the HVI colorimeter have been approved for use in determining on USDA classed Upland cotton.5, 6

11.2 USDA cotton color grades

In the USDA universal standards, cotton color grades are divided into fi ve major categories: white, light spotted, spotted, tinged, and yellow stained, and each is


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further divided into eight subcategories: good middling, strict middling, middling, etc..4 Cotton color notation consists of a double-digit number, e.g., 21, 32,… . The right digit indicates the fi ve major categories, and the left one indicates the eight subcategories. Table 11.1 lists the color grades for American Upland cotton. Note that in the USDA universal standards, there are only seven physical standards for cotton in the white category, fi ve in the spotted category, and three in the tinged category. The other two categories, light spotted and yellow stained, are presented by descriptive standards.

11.3 HVI colorimeter

The HVI colorimeter is an instrument that measures the refl ectance Rd and yellowness +b of cotton and provides offi cial color grading in the U.S. based on the Nickerson-Hunter color diagram,1, 4 which illustrates the relationships between HVI color measurements and color grades. In the HVI colorimeter, cotton samples are pressed against a window, under which a light source and two photo sensors are installed. The photo sensors are chosen to detect lights in the selected wavelength bands, and to output the Rd, +b and corresponding color grade.

Although the HVI colorimeter can yield repeatable cotton color grades, its disagreement with cotton classifi ers has been recognized since the beginning of its use in the classing system. Cotton Incorporated conducted a survey on colorimeter-classifi er color grade disagreements for 1995’s cotton at the USDA cotton classing offi ces, and found such disagreements exist commonly in all the classing offi ces. The disagreement was as high as 33.8%. The most noticeable disagreement is the discrepancy between ‘white’ and ‘light spotted’ categories. It is more likely for an HVI colorimeter to assign a ‘white’ grade to a sample labeled as a ‘light spotted’ grade by a classifi er, which could reach 35.4%. The HVI-classifi er disagreement on color grade can result in a signifi cant economic impact on both cotton producers and buyers.7

Table 11.1 Color grades of American Upland cotton

White Light spotted Spotted Tinged Yellow stained

Good middling 11* 12 13 – –Strict middling 21* 22 23* 24 25Middling 31* 32 33* 34 35Strict low middling 41* 42 43* 44 –Low middling 51* 52 53* 54 –Strict good ordinary 61* 62 63* – –Good ordinary 71* – – – –Below grade 81 82 83 84 85

* Physical standards. All others are descriptive.

Grading of cotton by color measurement 255

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During the 1998 classing season, the USDA Cotton Program conducted a pilot project in conjunction with the National Cotton Council Quality Task Force Committee for the purpose of making the HVI color grade determination closer to the Universal Cotton Standards and to classifi ers’ offi cial color grades. An adjustment in color grade divisions was made to better match the Rd and +b measurements with classifi ers’ color grades on the Nickerson-Hunter color diagram for the 1986 cotton crops. The Cotton Program evaluated the overall impact by analyzing all of the white and light spotted grades assigned during the 1999 classing season. The data revealed that 87% of the classifi er color grades and 90% of the HVI color grades were classifi ed as white. In addition, 12% of the classifi er grades and 10% of the HVI grades were classifi ed as light-spotted. The reproducibility for both methods remained consistent at approximately 74–75% for both the classifi er and the HVI color grades.5 As a result of the study, the National Cotton Council Quality Task Force Committee made a recommendation in early 2000 to adopt the HVI color grade as the offi cial color grade. The National Cotton Council’s Board of Directors voted in favor of the recommendation and the cotton industry agreed to accept the decision. The HVI color grade became the offi cial color grade effective July 1, 2000.

11.4 Factors affecting cotton color grade

Redness, yellow spots, and trash particles are three major factors that can infl uence the instrumental measurements of cotton color.7

11.4.1 Factor one: redness

It can be readily noticed from the Nickerson-Hunter color diagram that a ‘white/good middling’ sample can have the same yellowness as a ‘spotted/strict good ordinary’ sample. This means that the yellowness +b cannot exclusively refl ect the chroma of the cotton color. The third color attribute, redness a, should be taken into account in the instrumental grading of cotton color. The color values of the 15 USDA physical standards were measured using the imaging colorimeter, and the distributions of Rd, b and a against color grades are displayed in Fig. 11.1. Although Rd and b have clear trends to decrease with color grades both within and among color categories, a shows signifi cant changes only among color categories. In the same color category, a appears to be almost invariant with sub-categories (good middling, strict middling, …) except the point at grade 34. Each color category has a certain level of a, which does not overlap with its neighboring categories (Table 11.2). This uniqueness of a among color categories would make it a useful role in the cotton color grading.

To quantify the contributions of a to cotton chroma C,8 the percentage of a in C for each physical standard was calculated and displayed in Fig. 11.2. The a’s contributions take 10–16% of C for cotton in the white color category; 21–26% in


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the spotted category, and 28–33% in the tinged category. Overall, the percentage of a increases as the color grade goes down. The redness content in C suggests that the a’s contribution to cotton color cannot be ignored, and should be included in the color grading system.

55

60

65

70

75

80

Rd

6

8

10

12

14

b

011 21 31 41 51 61 71 23 33 43 53 63 34 44 54

2

3

4

5

6

a

1

SpottedLight spottedWhite

11.1 Rd, b and a of the USDA physical standards.


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11.4.2 Factor two: spots

Locally yellowed areas in a cotton sample are regarded as spots. The existence of spots in a cotton sample may make a classifi er call the sample ‘light spotted’. What is the infl uence of spots when the cotton’s color is measured by a colorimeter? In general, the infl uence depends on the depth of the spot’s color as well as the size of the spots in the viewing area of the colorimeter. When the sizes of spots are not negligible relative to the viewing area of the colorimeter, the existence of the spots may cause appreciable change in color measurements and thus in the cotton color grade. An experiment was conducted to explore the sensitivity of cotton color change to the spots present in the sample (see Table 11.3). Four light-spotted cottons, their classifi er’s grades 22, 32, 42, and 52 respectively, were selected in an effort to keep the amount of spots unchanged as the cotton brightness varies, and a Minolta colorimeter CR-210 was used to measure the colors of two selected areas on each sample, the areas with or without a spot. The viewing area of CR-210 is circular with a diameter of 5 cm. The change in one color attribute (Rd, a or b) caused by a spot is quantifi ed by the relative difference Δ defi ned as:

Δ = (x – x0)/x0 * 100%

Table 11.2 The a range for each color category

Color category a

White <1.5Light spotted 1.5~2.5Spotted 2.5~4Tinged 4~5.2Yellow stained >5.2.

10

15

20

25

30

35

11 21 31 41 51 61 71 23 33 43 53 63 34 44 54

Standard

%

aC

11.2 Contribution of a to cotton chroma C.


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where x is one color attribute of the cotton sample without spots, and x0 is the same color attribute of the sample with spots. A positive Δ (x > x0) indicates an increase in the color attribute without a spot being present. Table 11.3 summarizes the Δ in Rd, a or b of the samples. It can be seen that all the color attributes of these four samples are affected by the presence of spots, with the change in a being the maximum. The Δ(Rd) values show a positive change and Δ(a) and Δ(b) show negative changes, suggesting that the spots make the samples appear darker and more chromatic. As a result, the color grades of the samples are lowered when a spot is present inside the viewing area. Although the color change does not shift the color grade of sample 2, its sub-grade has changed from 31-4 to 31-3. Depending on the color and size of the spot, the infl uence on cotton color varies. Overall, the colorimeters with smaller viewing areas will be more sensitive to the existence of spots.

11.4.3 Factor three: trash particles

Trash particles, such as leaf, bark and grass, are foreign matters in cotton that have substantially different colors from lint. They will affect the output of a colorimeter if they are not either physically or computationally removed from the scene as the colorimeter takes the measurement, as the color alteration depends on the type and amount of trash particles that the sample contains.

Five cotton samples that have the same classifi er’s color grade but different leaf grades were selected to test the infl uence of leaf on the color measurements made by the CR-210 colorimeter. A higher leaf grade means a higher leaf content. Table 11.4 shows the color values and grades of the samples before and after the leaves were manually removed from the samples. After the removal of leaves, the Rd of the samples increases, a decreases, and b shows only a slight increase. These tendencies are clearer when the leaf grade increases. The change in a is greater than in b. This reveals that leaves contribute more a components to the cotton chroma.

For the samples whose leaf grades are not larger than 3, the color differences caused by leaves are not signifi cant enough to alter the samples’ color grades. For the samples whose leaf grades are larger than 3, the color differences bring

Table 11.3 Infl uence of spot on color measurement with CR-210 colorimeter

No Classifi er With spots Without spots Δ (%)

grade

Rd a b Grade Rd a b Grade Rd a b

1 22 71.9 1.8 12.1 23–3 75.6 1.5 11.2 22–1 5.2 –19.0 –7.22 32 73.9 1.4 9.4 31–4 75.9 1.2 9.1 31–3 2.8 –10.7 –3.13 42 61.0 3.2 10.6 53–4 69.9 2.2 9.9 42–1 14.5 –31.6 –7.04 52 62.4 1.9 8.7 52–2 64.7 1.7 8.0 51–4 3.7 –13.9 –8.4


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about some changes in the color grades, especially for sample 5 whose leaf grade is 7. The leaf content of a high leaf grade sample becomes more noticeable in the viewing area of CR-210. Hence, CR-210, or any other colorimeter with a small viewing area, is not suitable for grading the color of a heavily contaminated sample. The CR-210 color grades without including leaves are closer to the classifi er’s grades. This is because classifi ers are trained to make color grades independent of trash particles.

11.5 Color measurement using color image analysis

Advanced imaging devices have been used to construct a new image-based colorimeter to measure both cotton trash contents and color.9 As depicted in Fig. 11.3, the imaging colorimeter uses a 3-chip color CCD camera (JVC KY-F55B) and a xenon fl ash light (Vivitar Electronic Flash 1900) to capture images of cotton fi bers over a much larger area ((8.47 × 6.35 cm2) than traditional colorimeters. This camera has a high resolution and convenient ways to adjust white balance and other settings. The fl ash can uniformly illuminate the entire viewing area with high-intensity, short-duration pulses. It was found that the overall variation

Table 11.4 Infl uence of leaves on color measurements with CR-210 colorimeter

No Leaf Classifi er With leaf Without leaf

grade grade

Rd a b Grade Rd a b Grade

1 2 41 71.7 0.84 7.5 41–2 72.6 0.72 7.7 41–22 3 41 71.6 1.48 7.9 41–4 71.8 1.44 8.0 41–43 5 41 74.5 1.04 8.5 31–3 74.9 0.88 8.6 31–44 6 41 72.2 1.13 7.6 41–2 73.5 1.03 8.0 41–15 7 41 69.6 1.24 7.4 51–1 71.4 0.95 7.5 41–2

Flasher

Press foot

Cotton sample

CCDcamera

Lens

Window

11.3 Schematic set-up of the imaging colorimeter.


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11.4 Irregular regions in cotton image.

Shadow

a

Shadow

Spots

in intensity in a grabbed image of a white paper over the same viewing area is 2.3%.9

A specially designed circuit was added to the Vivitar fl asher to obtain a steady fl ashing intensity each time the camera captures the image, and therefore the repeatability of color measurement can be ensured. The computer triggers the fl asher when an image is being grabbed. The schematic set-up of this computer vision system is presented in Fig. 11.3.

11.5.1 Identifi cation of irregular regions

Chromatic areas, such as trash, spots and shadows, in the color image of a cotton sample are called irregular regions, which should be excluded from cotton color measurements (Fig. 11.4).

The imaging colorimeter uses not only lightness, but also other color attributes to identify these irregular regions before taking color pixel counts for cotton fi bers. Fig. 11.5 shows the typical CIE L*C*h distributions of the cotton sample shown in Fig. 11.4. The peaks of these three curves correspond to the L*C*h values of white cotton lint, which has a brightness around 90, a chroma of around 9, and a hue angle of 85° (yellow regions in a color wheel). The typical L*C*h values of trash particles, spotted and shadowed areas can be obtained by manually selecting a number of these features from various images, and the thresholds for screening these regions can then be selected. Although one threshold in either lightness or chroma is inadequate to discern these features from white lint, using thresholds in both dimensions and combining the two criteria with a logical ‘AND’ can achieve this goal more effectively. That is, if a pixel satisfi es:

L* < L*0; and C* > C*0

the pixel will be assigned to the irregular regions. Here, L*0 and C*0 are a set of the threshold. Image a in Fig. 11.6 shows identifi ed trash, spots, and shadows


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in the sample (Fig. 11.4). The irregular regions can be further separated by using a multi-dimension thresholding algorithm9, 10 illustrated in Fig. 11.7. L*1, C*1, h1 and C*2 are a set of threshold levels that are determined from the averages and standard deviations of L*C*h values in these regions. Images (b), (c) and (d) in Fig. 11.6 are the maps of separated spots, shadows and trash particles.

11.5.2 Color measurement

The imaging colorimeter system (IC) provides a comprehensive function for measuring cotton colors and distributions in various color coordinate systems.11 Twelve cotton samples (S1–S12) with various trash contents were used as the

11.5 L*C*h distributions of cotton sample.

0.5F

requ

ency

0.25

00

L*20 40 60 80 100

0.5

0.25

00

h (degree)60 120 180 240 300 360

0.5

0.25

00

C*20 40 60 80 100

11.6 Identifi ed irregular regions; (a) all, (b) spots, (c) shadows, (d) trash particles.

(a)(aa) (b)

(c)(c) (d)(d)


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experimental materials. For each sample, fi ve images were captured at different locations of the sample, and the results from the fi ve images were combined for the fi nal report. The samples were also tested by Spinlab HVI (SPL), and Motion Control HVI (MCI) and Minolta colorimeter CR-210 (Fig. 11.8). The results exhibit a high consistency between the systems, although the Rd and +b readings of the Minolta CR-210 are systematically lower than the corresponding readings of

Irregular regions

Spot and trash

Spot Trash Shadow

Y

Y N N

Shadow and trash

N

Y

Y

NC* > C1*? C* < C2*?

L* > L1* and h > h1? L* > L1* and h > h1?

11.7 Multi-dimension thresholding.

11.8 Rd and +b comparisons of the samples.

55

60

65

70

75

80

S1 S5 S4 S6 S7 S2

IC CR-210 SPL MCI

S8 S11 S12 S3

Rd

4

5

6

7

8

9

10

11

S7 S5 S9 S1 S10 S3

+b

S11 S2 S8 S12 S6 S4

S9 S10


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the other colorimeters. This is because any difference in light source, color sensor and set-up geometry may contribute to the differences in the results that colorimeters output. This fi gure indicates that the imaging system (IC) has been adjusted to generate Rd and +b values very similar to those of SPL and MCI colorimeters.

The system is also able to yield the distributions of the color attributes since it measures color of every pixel in a relatively large area. Figure 11.9 shows the Rd ab distributions of S1, indicating the dispersions of the color measurements in the sample. The red-green attribute a concentrates in a range from –5 to 5 with larger distributions being in the positive range. A negative a indicates a green constituent in the sample. Usually, the average a of a cotton sample falls in the range 1–2, and the average b in the range 6–10. Because of a relatively small portion of a, only Rd and +b are taken into account in the previous cotton color measuring systems.

11.6 Using neural networks12

The disagreements of HVI and classifi er color grades were examined specifi cally in the major and sub-categories of cotton colors. Table 11.5 shows the distributions of the disagreements among the fi ve major categories. A substantial amount (44.3%) of the samples were graded ‘white’ by the HVI, but disputably graded ‘light spotted’ by the classifi er. However, almost no ‘light spotted’ samples graded by the HVI were graded ‘white’ by the classifi er. Hence, there is a biased trend in the disagreements between the white and light spotted categories. The disagreement in these two categories is a determinant (about 82%) in the total disagreement.

0

0.1

0.2

0 20 40 60 80 100

Rd

Fre

quen

cy

0.2

0.4

0.6

0 10 20 30 40–10

a

0

0.2

0.4

+b

50403020100

11.9 Rdab distributions of a cotton sample.

Table 11.5 Disagreement (%) in the major categories

Classifi er HVI White Light spotted Spotted Tinged Yellow stained

White 44.3 0 Light spotted 0 0.2 Spotted 0 0.08 Tinged Yellow stained


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Between the light spotted and spotted categories, the disagreements are nearly negligible and unbiased. The disagreements among other categories are not available from this sample set, but they are expected to be low. Table 11.6 shows the distributions of the disagreements among the seven subcategories. Overall, the disagreements in the subcategories are much lower, and more widely spread than in the major categories. The HVI has a slight tendency to give higher grades to the samples than the classifi er in the subcategories. The possible sources attributable to HVI–classifi er disagreements can be both systematic and random. Systematic disagreements mainly occur among the major color categories, particularly between ‘white’ and ‘light spotted’, and are the dominant component in the total disagreements. A neural network classifi cation provides an effective solution for solving this disagreement problem.

11.6.1 Multilayer perceptron (MLP)

A neural network (NN) is a computational system that can provide sophisticated mappings from a set of input variables to a set of output variables according to the relationships learned from the training data.13, 14 An NN usually contains massive processing units (neurons) organized in successive layers. The neurons between two adjacent layers are connected with adjustable parameters governing the form of the input–output mapping. To perform an explicit mapping, the connections of neurons must be feed-forward. One of the most common feed-forward networks is a multilayer perceptron (MLP), which normally composes one input layer, one or more hidden layers and one output layer (Fig. 11.10). To design an MLP for solving a specifi c classifi cation problem, the developer needs to determine the inputs, outputs, number of hidden layers, number of neurons in each layer, and the training algorithm that is suited for the problem.

Table 11.6 Disagreement (%) in the subcategories

Classifi erHVI GM SM M SLM LM SGO GO

GM 2.79 SM 0 3.34 M 1.07 2.19 SLM 0.56 0.52 LM 0.12 0 SGO GO

GM: good middling, SM: strict middling, M: middling, SLM: strict low middling, LM: low middling, SGO: strict good ordinary; GO: good ordinary.


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11.6.2 Neural network classifi er for cotton color grading

To classify cotton color, the inputs of the MLP should utilize the statistic information, such as the means and standard deviations, of Rd, a and b of samples, and the imaging colorimeter is capable of measuring these data. In this research, however, we were unable to obtain enough cotton samples that had been graded by the classifi er and HVI for us to use the imaging colorimeter to collect these data needed for training the NN classifi er. We had to use only the Rd and b means from the HVI as the inputs. The principle and procedure established by using these two inputs are directly applicable to the one using more inputs.

Normally, the MLP uses one output neuron to represent one respective category, such as a color grade. Since there are 25 offi cial color grades and fi ve below-grades in the USDA universal standards, a neural network should have 30 output neurons to differentiate these grades. However, the color grades of U.S. cotton heavily concentrate in the white and light-spotted categories. The sample set randomly selected for this research cannot equally represent all the color grades. There would be a negative bias over less represented grades if all the grades were judged simultaneously. Therefore, a two-step approach was adopted in developing a neural-network-based classifi er. This classifi er consists of multiple neural networks that perform the classifi cations of the major and sub-color categories separately (Fig. 11.11).

The color data of a sample are fi rst classifi ed by an MLP to determine the main color category, and then sent to a separate MLP to determine the sample’s subcategory within the identifi ed main category. The classifi er has one main MLP that has two inputs, two hidden layers, and fi ve output neurons corresponding to the fi ve main categories (1–white, 2–light spotted, …). The two hidden layers have six and twelve neurons, respectively. For each main category, there is a sub-MLP that may have three to eight output neurons depending on how many subcategories (1–good middling, 2–strict middling, …) are available in this main

Input Hidden Output

W ij(1)

x1

x2

y1(1)

y2(1)

yM(1)

1

xM0

W ij(n)

y1(n)

y2(n)

y1(n+1)

y2(n+1)

yM(n)

n

yM

n+1

(n+1)

11.10 MLP topology.


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category. A sub-MLP also uses a two hidden-layer structure, with six neurons being on the fi rst layer and 15 neurons on the second layer. Each output of the main MLP is also used to control a switch that permits the color data to be sent to the corresponding sub-MLP when it is turned on. After two categories are identifi ed, a color grade is generated by placing two digits together. The connecting weights Wij

(n) of two adjacent layers in each MLP were determined through a supervised training procedure called the error back-propagation algorithm.13, 14

11.6.3 Cotton color grading results by NN classifi er

The training data used should be those obtained from the universal standards for Upland cotton. Unfortunately, the universal standards do not include the physical samples (biscuits) for all the color grades, whose colors can be measured by an instrument. We had to use the classifi er’s color grades as targeted grades in the network training, since they are currently ‘offi cial’. The same sample set (2489 color data of 1996 crops) was used as the training set. 1385 more samples from 1997 U.S. crops were used as a validation set to check the generalization performance of the classifi er.

It was found that the NN classifi er reduced the machine–classifi er disagreements from 54.08% to 16.35% for the training set. The NN–classifi er disagreement seems to have reached a minimal level (around 20%), because the classifi er’s reproducibility is generally 80%. Figure 11.12 presents the distributions of the

1

MainMLP

Rdb

Maincategory1 2 3 4 5 1 2 3 4 5 6 7 8

Subcategory

Sub-MLP1 Sub-MLP2 Sub-MLP3 Sub-MLP4 Sub-MLP5

2

3

4

5

11.11 The neural network classifi er.


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11

11

0

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20

30

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100%

12 21 22 23 31 32 33 4142 43 51 52 53

HVI–classifier

6162 63 8312

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ifier

HVI

4243

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5361

6263

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(a)

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NN–classifier

6162 63 8312

2122

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3233

41Class

ifier

NN

4243

5152

5361

6263

83

(b)

11.12 Distributions of color grade disagreement: (a) HVI–classifi er, (b) NN–classifi er.


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color grade disagreements of the training samples between HVI–classifi ers (a), and NN–classifi ers (b). Compared with the HVI–classifi er disagreements, the NN–classifi er disagreements are much smaller and more evenly spread across all the grades. That means that the NN classifi er can drastically reduce systematic disagreements with the classifi er. The NN–classifi er disagreements of the validation samples decrease from 62.09% to 22.89%, which are consistent with those of the training samples. The result from the validation set shows that the NN classifi er provides a good generalization for new cotton color data.

11.7 Using fuzzy logic15

11.7.1 Boundaries of cotton color grades

The partition of the color space in the Nickerson-Hunter (Rd, +b) diagram was based on the experimental data of cotton crops in the 1950s. The boundaries between color blocks may not accurately represent color differences in today’s cotton. The crisp, abrupt separations of color grades in the diagram do not refl ect the clustering nature of cotton color classes, which often have blur boundaries and neighboring classes always overlap to some extent. Thus, the belongingness of a sample point in an overlapping region is inherently ambiguous.

The above analysis can be further evidenced by the color data of 2489 bales of cotton selected from the 1996 crop. To facilitate the discussion, we focused on two major color classes, white and light spotted, which are also the two most disputable color grades. Fig. 11.13(a) shows the distributions of the white and light spotted classes labeled by classifi ers for 2489 bales of cotton selected from the 1996 crop. Both the white and light spotted classes seem to follow a two-dimensional Gaussian distribution. Note that the real boundaries separating three major color classes, white (W), light spotted (LS) and spotted (S), were also drawn on the Rd–b plane. Although the two classes have distinct populations, they overlap extensively and their intersection does not seem to coincide with the W–LS boundary. It is evident that the W–LS boundary does not provide a realistic separation between the white and light spotted classes. This mismatch brings a systematic error into the HVI’s color grading, as shown in Fig. 11.13(b). The clear split between the white and light spotted classes arises from the crisp boundary used by the HVI. However, the W–LS separation by the HVI does not indicate the natural grouping of the cotton color data in these two classes. It is logical to consider that the two peaks of the distribution represent two separate populations in the color data as seen in Fig. 11.13(a). The HVI, however, did not allocate these two populations properly. This is the reason why the HVI tends to grade cotton colors for the white class more likely than for the light spotted class. In order to make the machine grading more realistically refl ect the natural grouping of cotton colors, the Cotton Program of the USDA and the cotton community agreed to adjust the boundaries of the Nickerson-Hunter color diagram in 2000.


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However, the modifi ed color diagram still does not deal with problems associated with overlapping boundaries of color classes.

11.7.2 Fuzzy inference system (FIS) for cotton color grading

Fuzzy logic uses the fuzzy set theory and approximate reasoning to deal with imprecision and ambiguity in decision-making.16–19 It provides intuitive, fl exible ways to create fuzzy inference systems for solving complex control and

46

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Rd

LS S

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quen

cy

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o − White

* − Light spotted

46

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W LS S12

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70

80

900

0.1

0.2

b

Rd

Fre

quen

cy

(b)

o − White

* − Light spotted

11.13 Distributions of white and light spotted colors: (a) graded by classifi er, (b) graded by HVI.


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classifi cation problems. For classifi cation applications, fuzzy logic is a process of mapping an input space into an output space using membership functions and linguistically specifi ed rules. In this study, we take the output of the HVI colorimeter, the Rd, b data, as the input, and color grades as the output. Our discussion in this paper will be limited to the classifi cation for fi ve major color classes, white (W), light spotted (LS), spotted (S), tinged (T) and yellow stained (YS). Figure 11.14 presents a schematic diagram of the fuzzy inference system (FIS) for cotton color grading.

11.7.3 Fuzzy sets and membership functions

Elements in ordinary or crisp sets have full membership in one set and zero membership in others. A fuzzy set contains elements only with partial membership ranging from 0 to 1 to describe uncertainty for classes that do not have sharply defi ned boundaries. For each input and output variable of an FIS, fuzzy sets are created by dividing its universe of discourse (entire space) into a number of sub-regions and are named in linguistic terms. Fuzzy sets’ linguistic terms are useful in establishing fuzzy rules. In designing an FIS for cotton color grading, fi ve fuzzy sets were selected for the input variable Rd and six for b. The fuzzy sets for Rd represent fi ve levels of brightness varying from very low (I), low (II), median (III), high (IV) to very high (V), and the fuzzy sets for b represent six levels of yellowness ranging from very low (I) to extremely high (VI). Table 11.7 lists the ranges and other distribution parameters of the input fuzzy sets. Each fuzzy set overlaps with its adjacent fuzzy sets. The reason for adding one more fuzzy set for b is that b seems more critical than Rd in determining major cotton

Fuzzification Fuzzy rules Defuzzification Input(Rd, b)

Output(W, LS, S, T, YS)

11.14 Fuzzy inference system for cotton color grading.

Table 11.7 Parameters for the fuzzy sets of the input variables

Fuzzy set Rd b

Range m σ Range m σ

Very low (I) 40–52.5 46.5 3.5 4–7 4.0 1.00Low (II) 45–65 55 3 4–11 7.2 1.00Medium (III) 54–75 64 3 7–12.5 9.5 0.80High (IV) 60–82.5 71.5 3.5 9–17 12.4 1.19Very high (V) 67.5–87.5 77.5 3.5 11–18 15.1 1.19Extremely high (VI) 14–18 18.0 1.19


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color classes (white, light-spotted, etc.). In general, the more intermediate levels are used, the higher accuracy the classifi cation would be. However, increasing the fuzzy sets will signifi cantly increase the number of fuzzy rules in the next step. The fi nal selection on the number of fuzzy sets and their range may be determined by trial and error. Since this FIS was designed to classify fi ve major color classes, the output variable was split by fi ve fuzzy sets named as white (1), light-spotted (2), spotted (3), tinged (4) and yellow stained (5). The range of the output variable was equally divided into fi ve sections for the fi ve fuzzy sets.

Once the fuzzy sets are chosen, a membership function for each set should be created. A membership function is a curve that maps an input element to a value between 0 and 1 showing its degree of belongingness to a fuzzy set. The curve can have different shapes, such as bell (Gaussian), sigmoid, triangle or trapezoid, for different types of fuzzy sets.16, 17 In this study, the Gaussian distribution curve was used to build the membership functions for the input fuzzy sets Rd and b:

µ(x) = e–(x−m)2/2σ2

where m and σ are the mean and the standard variation of one fuzzy set in x (Rd or b). Finding the right parameters for the functions is a major task, which may be selected arbitrarily and then tweaked by using a known set of input–output data. The m and σ values used in this FIS are included in Table 11.7, and the membership functions are displayed in Fig. 11.15. The extent of overlap between the membership functions of two adjacent sets indicates the nature of the soft boundary between two color classes.

For the simplicity of defuzzifi cation, a triangular shape was used to construct the membership functions for the output fuzzy sets:

= 0, x < a or x ≥ cµ(x) = { = (x – a)/(b – a), a ≤ x < b = (c – x)/(c – b), b ≤ x < c

The shape and size of the triangular function depend on the values of a, b and c. To make the output clear and unbiased, the symmetric, non-overlapping and equal-size membership functions were used for all the output sets (Fig. 11.16).

11.7.4 Fuzzifi cation

Fuzzifi cation is a step to determine the degree to which an input data belongs to each of the appropriate fuzzy sets via the membership functions. For a given input point (Rd0, b0), the memberships of all the fuzzy sets are calculated, and only the fuzzy sets with non-zero memberships are forwarded to the next steps. In Figure 11.15, an example of determining the relevant fuzzy sets was shown for an input data (Rd0, b0) = (67.5, 9.0). Rd0 belongs to the medium (III) and high (IV) sets of Rd with the memberships being 0.51 and 0.52, while b0 belongs to the low (II) and


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medium (III) sets of b with the memberships being 0.20 and 0.82. There are four combinations with the selected fuzzy sets:

[µIII (Rd0), µII (b0)] = [0.51, 0.20],

[µIII (Rd0), µIII (b0)] = [0.51, 0.82],

[µIV (Rd0), µII (b0)] = [0.52, 0.20],

[µIV (Rd0), µIII (b0)] = [0.52, 0.82].

These four combinations will be evaluated by fuzzy rules to determine the output fuzzy sets and the weight of each rule infl uencing the output.

11.7.5 Fuzzy rules

In an FIS, fuzzy rules provide qualitative reasoning that links input fuzzy sets with output fuzzy sets. They are a collection of linguistic rules of the form:18

0

0.5

1

0 5

1White

2Light

spotted

3Spotted

4Tinged

5Yellowstained

10 15 20 25 30 35 40 45 50

11.16 Triangular membership functions of output fuzzy sets.

1

0.5

0

1

0.5

0

40

4 8 12 16

45

IVery low

IILow

IILow

IIIMedian

IIIMedian

IVHigh

IVHigh

V Very high

V Very high

VIExtremely

high

50 55 60 65 Rd0

b0

Rd

b

70 75 80 85

IVery low

11.15 Membership functions of input fuzzy sets.


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Ri: If Rd is Ai AND b is Bi, then color is Ci, i = 1, 2, …, k

where Ai, Bi and Ci are the fuzzy sets for the inputs Rd and b and the output color in the ith rule Ri, and k is the number of the rules. The values of Ai and Bi are the linguistic terms such as very low (I) and very high (V), and the values of Ci are the linguistic terms such as white (1) and light spotted (2). An example of such a rule may be given as follows:

If Rd is very high (V) AND b is very low (I), then color is white (1).

The if-part of the rule is called the antecedent, and the then-part of the rule is called the consequent. Since the antecedent in this FIS always involves two conditions (one for Rd and one for b), fuzzy operators are needed to specify the relationships of the fuzzy sets in the antecedent. AND (intersection), OR (union) and NOT (complement) are the three common fuzzy operators. Because Rd and b should be simultaneously observed in selecting a color class, the fuzzy operator in all the antecedents must be AND. For two fuzzy sets A and B, the fuzzy AND is defi ned as:16,18

A AND B: min{µA(x), µB(x)}.

Fuzzy AND aggregates two membership functions by outputting the minimum value at a given input x (Rd or b). The result of fuzzy AND serves as a weight showing the infl uence of this rule on the fuzzy set in the consequent.

The fuzzy rules should be established based on both visual grading experience and the basic relationships between color data and color grades in the HVI color diagram. Since there are fi ve fuzzy sets in Rd and six fuzzy sets in b, there are 30 possible combinations in the antecedents when only fuzzy AND is applied. Thus, the maximum number of the fuzzy rules that can be established is 30. To simplify the fuzzy rule expressions, we designed a chart that illustrates (Fig. 11.17). In the chart, the fi ve sets of Rd are arranged vertically and the six sets of b are arranged horizontally. A knot (a black dot) between a horizontal line and a vertical line indicates an antecedent formed by the connecting fuzzy sets from Rd and b, and leads to a consequent that in turn gives an output fuzzy set. The aggregated membership of the antecedent is then used as a weight factor to modify the size and shape of the membership function of the output fuzzy set in a way of either truncation or scaling. Truncation is done by chopping off the triangular output function, while scaling is done by compressing the function. In this FIS, the truncation operation was used. If the membership function of an output fuzzy set is µ(x) and the weight generated from the antecedent is w, the truncated functions are:

µT(x) = max{µ(x), w}.

As given previously, the input fuzzy sets containing sample (Rd0, b0) have four combinations, which satisfy the four following rules:


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R1: If Rd is median (III) AND b is low (II), then color is white (1);R2: If Rd is median (III) AND b is median (III), then color is spotted (3);R3: If Rd is high (IV) AND b is low (II), then color is white (1);R4: If Rd is median (IV) AND b is median (II), then color is light spotted (2).

The four output fuzzy sets are circled in Fig. 11.17. The weights of the rules on the four outputs are 0.20, 0.51, 0.20 and 0.52, respectively. The truncated membership functions of the output fuzzy sets are presented in Fig. 11.18.

11.7.6 Defuzzifi cation

After all the fuzzy rule evaluations are done, the FIS needs to output a crisp member to represent the classifi cation result (color classes) for the input data. This step is called defuzzifi cation. As seen in the (Rd0, b0) example, one input data may generate several weighted output fuzzy sets. The multiple sets need to be aggregated into a single set in preparation for the defuzzifi cation. If those output fuzzy sets are different, the aggregation can be done simply by placing all the truncated functions together to form the fi nal fuzzy set. If two of the output fuzzy sets are identical, they can be combined by using fuzzy OR, which is defi ned as:16,18

A OR B: max{µA(x), µB(x)}.

Fuzzy OR gives the maximum value of the two membership functions at any given point. For example, sample (Rd0, b0) has two ‘white’, one ‘light spotted’ and

1

Output

VI

V

VIV

IV

III

III

II

II

I

I

Rd

(Rd0 =67.5)

(Rd0 =67.5)

(b0 =9.0) (b0 =9.0)b

2345

12345

12345

12345

12345

11.17 Fuzzy rules.


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one spotted output functions. After the fuzzy OR operation, the two trapezoidal ‘white’ functions merge into one so that the aggregated curve becomes the one shown in Fig. 11.19.

The most popular method for defuzzifi cation is the centroid calculation, which returns a grade weighted by the areas under the aggregated output functions. Let a1, a2, … an be the areas of the truncated triangular areas under the aggregated function, and c1, c2, … cn be the coordinates of their centers on the x-axis. The centroid of the aggregated area is given by:17,19

G = Σn

i=1aici / Σ

n

i=1ai

The location of the centroid indicates the color class to be designated to the input data. For sample (Rd0, b0), G is 17.1, which falls in the light spotted set (see Fig. 11.19). The location of this centroid inside the identifi ed fuzzy set can suggest a fi ner rating.

11.7.7 Cotton color grading results by FIS

The constructed FIS was tested using cotton samples in 1996, 1997 and 1998 crop years. Since the majority of the U.S. Upland cottons are ‘white’ and ‘light spotted’ and the most disputable grading occurs between these two classes, samples only in these two classes were selected for the experiment. There were totally 2489 samples from the 1996 crop, 1375 samples from the 1997 crop and 658 samples from the 1998 crop in the selection. The samples were fi rstly graded by offi cial cotton classifi ers, and then measured by the HVI colorimeters. The (Rd, b) data

11

White1

White3

Spotted2

Light spotted

0.5

0.20

00 5 10

1

0.51

020 25 30

1

0.5

0.20

00 5 10

1

0.52

010 15 20

11.18 The weighted membership functions of output fuzzy sets.

0

1

0.5

1White

Centroid

G

2Light spotted

3Spotted

05 10 15 20 25 30

11.19 Aggregation of the weighted output functions.


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were converted into color grades using the HVI color diagram and the FIS. We used the classifi ers’ grades as a reference to check the consistency of the HVI and FIS data, since the classifi ers’ grades were the offi cial grades at that time and they coincided with the natural grouping of the color data. Table 11.8 presents the data showing the disagreements among the testing methods. Percent disagreement was calculated by dividing the disagreement count with the number of the tested samples. For the white class, 54.1% of the samples in the 1996 crop, 61.5% in 1997, and 51.2% in 1998 were graded differently by the classifi ers and the HVI. The amount of disagreements between the classifi ers and the FIS has decreased considerably, and the FIS results are consistent for consecutive years. The classifi er–FIS disagreement was much more evenly spread between the white and light spotted classes, suggesting that there is no systematic bias in the FIS’s grading.

Figure 11.20 shows the distributions of the white and light spotted color classes of the 1996 and 1997 samples classifi ed by the FIS. The two color classes in both years’ data were reasonably separated. Based on the two newly separated data clusters, a best-fi t polynomial curve was calculated (curve 2 in the fi gure). This curve provides a new boundary that more properly refl ects the segregation between the white and light spotted color classes. The distribution for the 1998 crop was not included because the number of the available samples from that year was not suffi cient for drawing an effective distribution.

11.8 Conclusions

Color is an essential indication of the overall quality of raw cotton. Colorimetry has replaced human classifi ers to become an offi cial method for cotton color grading in the current U.S. cotton classing system. The HVI colorimeter performs the grading based on the Nickerson-Hunter color diagram, which illustrates color grade divisions in a two-attribute (Rd, +b) color system. For more precise and

Table 11.8 Disagreements between different grading methods

Color class White Light spotted

Crop year 1996 1997 1998 1996 1997 1998

By classifi er (C) 1240 416 464 1249 959 194 (49.8%) (30.3%) (70.5%) (50.2%) (69.7%) (29.5%)By HVI 2350 1200 628 139 175 30 (94.4%) (87.3%) (95.4%) (5.6%) (12.7%) (4.6%)By FIS 1161 355 447 1328 1020 211 (46.6%) (25.8%) (68.0%) (53.4%) (74.2%) (32.0%)C-HVI 1347 846 337 9 14 3disagreement (54.1%) (61.5%) (51.2%) (0.4%) (1.0%) (0.5%)C-FIS 154 87 40 234 144 72disagreement (6.2%) (6.2%) (6.1%) (9.4%) (10.5%) (10.9%)


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reliable results, the infl uence of other factors, such as the third attribute – redness, localized discoloration, and extraneous matters – should be taken into account. Color imaging technology seems to be an ultimate solution to solve the sophisticated problems that still remain in cotton color grading, especially when coupled with advanced classifi cation methods (e.g., artifi cial neural networks, fuzzy logic) which can better interpret a human’s understanding of cotton colors.

11.9 References

1. Nickerson, D., Color Measurements of Standards for Grades of Cotton, Textile Research Journal, 16, 441–449, 1946.

2. Xu, B., Fang, C., Huang, Y. and Watson, M.D., Chromatic Image Analysis for Cotton Trash and Color Measurements, in press.

3. Anthony, W.S. and Mayfi eld, W.D., ‘Cotton Ginners Handbook’, USDA, December 1994. 4. USDA, The Classifi cation of Cotton, Agricultural Handbook 566, Washington

D.C., 2001. 5. Knowlton, James L. Improving Cotton Color Classifi cation, 2004 EFS® Systems

Conference Presentations, Cotton Inc. 2004. http://www.cottoninc.com/2004ConferencePresentations/.

6. Earnest, Darryl W. HVI Color Grade and Other Cotton Program Issues, 2000 EFS® Systems Conference Presentations, Cotton Inc. 2000. www.cottoninc.com/2000ConferencePresentations

7. Xu, B., Fang, C. and Watson, M.D., Investigating New Factors in Cotton Color Grading, Textile Research Journal, 68, 779–787, 1998.

8. Minolta Co., Ltd, ‘Precise Color Communication’, 1994. 9. Xu, B., Fang, C., Huang, R. and Watson, M.D., Chromatic Image Analysis for Cotton

Trash and Color Measurement, Textile Research Journal, 67(12), 881–890, 1997.10. Jain, A. K., ‘Fundamentals of Digital Image Processing’, Prentice-Hall, Inc. 1989.11. Xu, B., Fang, C., Huang R. and Watson, M., Cotton Color Measurements by An

Imaging Colorimeter, Textile Research Journal, 68(5), 351–358, 1998.

12

46

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60

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80

900

0.1

0.2

1

b

RdRd

46

810

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60

70

8090

0

0.1

0.2

b

1

1– W–LS boundary in HVI2– W–LS boundary in FIS

22

o— White (W) *— Light spotted (LS)

11.20 Distributions of white and light spotted colors classifi ed by the FIS.


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12. Xu, B., Su, J., Dale, D. and Watson, M.D., Cotton Color Grading by Neural Network, Textile Research Journal, 70(5), 430–436, 2000.

13. Bishop, C. M., ‘Neural Networks for Pattern Recognition’, Clarendon Press, Oxford, U.K., 1995.

14. Lin, Chin-Teng and Lee, George C.S., ‘Neural Fuzzy Systems’, Prentice Hall, Upper Saddle River, NJ, 1996.

15. Xu, B., Lin, S., Dale, D. and Watson, M.D., Cotton Color Classifi cation by Fuzzy Logic, Textile Research Journal, 72(6), 504–509, 2002.

16. Cox, E., ‘Fuzzy Systems Handbook, 2nd Edition’, Academic Press, Chestnut Hill, MA, 1999.

17. Jang, Roger J.S. and Gulley, Ned, ‘MATLAB/Fuzzy Logic Toolbox,’ MathWorks, Inc. Natick, MA, 1997.

18. Hguyen, H.T. and Walker, E.A., ‘A First Course in Fuzzy Logic, 2nd Edition’, Chapman & Hall/CRC, Boca Raton, FL, 1999.

19. Lin, Chin-Teng and Lee, George C.S., ‘Neural Fuzzy Systems’, Prentice Hall, Upper Saddle River, NJ, 1996.

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279

12Colour measurement of paint

fi lms and coatings

N. S. GANGAKHEDKAR, Compute Spectra Color Pvt. Ltd., India

Abstract: Paint quality is colour quality. We specify colour in terms of three numbers (X, Y, Z) and closeness of the match is described in a single number (ΔE). A colour computer system is used for quality control of colour, formulation, production batch correction and quantifying colour related properties such as whiteness and yellowness indices, opacity, contrast ratio, pigment load and hiding power. Preparing a good sample for colour measurement is very important and internet based colour management systems were recently introduced for point-of-sales and are operating successfully. A non-contact spectrophotometer for wet paint colour matching and a multi-angle spectrophotometer for matching metallic in automotive paints are now available.

Key words: quality control of paints, preparing good samples for colour measurement, colour management systems for paint shop, wet-paint colour matching, measurement of metallic colours in automotive paints.

12.1 Introduction

In paint making, product quality is colour quality and one of the major items in colour quality is closeness of the match. We specify colour in terms of three numbers (X, Y, Z or L, a, b) and closeness of match is described in a single number ΔE. In instrumental pass/fail of colour, we have to set-up three-dimensional tolerance limits (ΔL, Δa, Δb) or a single number colour difference (ΔE). We can also quantify whiteness index, yellowness index, colour strength of pigment, opacity, contrast ratio and the hiding power of paint. Appearance of the paint sample is mainly affected by gloss and we have to understand the importance of colour measurement with or without gloss. Quality control programs determine lot-to-lot variations in pigments and fi nished paints as measured against the standard and colour strength of in-process tinters (bases)/colorants. A computer colour matching system consisting of a spectrophotometer–computer combination is used for quality control of incoming pigment, pass/fail decision of outgoing paint product, colour formulation, production batch correction, quantifying colour related properties such as pigment load and opacity. How to prepare a good sample for colour evaluation is very important. We will discuss various aspects such as quality control of pigment, selection of colour measuring instrument for wet-paint colour matching, colour matching systems for paint shop, internet based colour management system for point-of-sales, measurement of metallic colours and roll of special effect pigments, measurement techniques for automotive paints and advances in colour instrumentation.


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12.2 Quality control of paints

Essentially, colour is something which we feel or perceive. Colour is a sensation and the eye is the real detector which gives us the total picture and feel. Perception of colour will vary from person to person. However, it is possible to quantify colour using a refl ectance measuring spectrophotometer. From a purely physical point of view, colour is measured by spectral curves of refl ectance plotted as a function of wavelength in the visible spectrum 400–700 nm. The physical quantities important in specifying colour are: spectral power distribution of light source (Energy, E), the spectral refl ectance of the sample (%R), and the spectral response of the eye in the form of colour matching functions (CIE Standard Observer, designated by colour matching functions r, g, b). One has to reduce the spectrophotometric data into colorimetric data using CIE mathematics (Judd and Wyszecki 1952: Wyszecki and Stiles 1967). This special physical analysis is designed to give colour as numbers (tristimulus values, X, Y, Z) correlating with visual impressions of the three colour response mechanism of the human eye (Billmeyer and Saltzman 1966).

12.2.1 Colour as numbers

Based on the fact that the human eye has three different types of colour sensitive cones, the response of the eye is best described in terms of three stimulus (tristimulus) colour matching functions r, g, b derived from three sensitivities. Using the r, g, b colour matching functions, the X, Y, Z (tristimulus values) system was introduced by CIE in 1931 (see Fig. 12.1). It is based on scientifi c data (Jude and Wyszecki 1975; Wyszecki and Stiles 1967). Only three numerical values X, Y, Z are suffi cient to describe colour:

X = Σ E × R × r [12.1]

Y = Σ E × R × g [12.2]

Z = Σ E × R × b [12.3]

where X, Y, Z are tristimulus valuesE is spectral energy distribution of light source (400–700 nm)R is spectral response curve of the object (400–700 nm)r, g, b are colour matching functions of a human observer.

100D65

Standard Observer

X = 62.04

Y = 69.72

Z = 7.34

0

% R

400 700

Wavelength nm

Ener

gy

400

× ×700

Sen

siti

vity

400

bg r

700

Wavelength nm Wavelength nm Tristimulus values

=

12.1 Colour as numbers: computation of tristimulus values – CIE mathematics.

Colour measurement of paint fi lms and coatings 281

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12.2.2 Recommendation CIE 1976 (L*, a*, b*) colour space

The CIE 1976 uniform colour space (CIE 1976) is a simplifi ed version of the Adams Nickerson space. It is produced by plotting in rectangular coordinates (Fig. 12.2) the quantities L*, a*, b* defi ned by

L* = 116 (Y/Y0)1/3 – 16Y/Y0 > 0.01 [12.4]

a* = 500 [(X/X0)1/3 – (Y/Y0)1/3] [12.5]

b* = 200 [(Y/YX0)1/3 – (Z/Z0)1/3] [12.6]

The tristimulus values X0, Y0, Z0 defi ne the colour of the normally white object-colour stimulus. Usually, the white object-colour stimulus is given by the spectral radiant power of one of the CIE standard illuminants, for example, D65 or A, refl ected into the observer’s eye by the perfect refl ecting diffuser. Under these conditions, X0, Y0, Z0 are the tristimulus values of the standard illuminant with Y0 equal to 100. The total difference DE* between two colours each given in terms of L*, a*, b* is calculated from

ΔE *= [(ΔL*)2 + (Δa *)2 +(Δb *)2]1/2 [12.7]

12.2.3 Recommendation CIELCH colour space

When seeking to identify the components of colour differences in terms of approximate correlates of lightness, chroma, and hue, or to express colour specifi cations in terms of such approximate correlates, the following colour terms

White

+Yellow

Green

a*

–

+

–Blue

b*

L*

c*

h

+Red

Black

12.2 CIELAB colour space and CIELCH colour space.


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•••• ••••

•••

•• •••••

b*

c*

a*

S

12.3 CIELAB colour co-ordinates: tolerance limits are three-dimensional. Tolerance limits for a and b form a rectangle: all samples inside the rectangle are passed while those outside are failed.

should be used: metric lightness (L*), metric chroma (C*), metric hue-angle, and metric hue-difference (ΔH*). These terms are defi ned by using the parameters L*, a*, b* of the CIE 1976 (L*a*b*) colour space, and when these terms are used, it must be made clear which of these two colour spaces has been chosen (see Fig. 12.2).

12.2.4 Colour tolerance

Note that tolerance limits are different for different applications. We have to have acceptable limits for each product or else decide on the perceptibility limit of the human being. For industrial application, we must opt for acceptable limits for colour difference. We can opt for single number shade passing (DE) or three-dimensional tolerance limits based on L, a, b or L, C, h colour space. Colour tolerance is an allowable deviation in colour from the standard sample and is expressed in terms of colour space dimensions or colour difference units (DE) (CIE 1995).

In CIELAB colour tolerancing, we use three-dimensional colour tolerances: ΔL lightness, Δa (Red/Green) and Δb (Yellow/Blue). We may not have uniform tolerance limits for all the parameters. Let us say we have fi xed ±Δa and ±Δb limits, which will give us a tolerance box which will be rectangular (see Fig. 12.3). All samples


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which will fall within the box limit will be numerically passed. On the other hand samples which are within the ellipse are visually accepted (see Fig. 12.4) and those samples which are outside the ellipse but still in the rectangular tolerance block are failed (not acceptable). This is one of the reasons that the CIELAB (1976) colour difference formula fails and does not agree when compared with visual tolerance. In the case of CIELCH, the difference limit for ΔL* (lightness), ΔC* (chroma), and Δh (hue) are considered (see Fig. 12.5). This method gives a wedge-shaped tolerance box (non-rectangular) and offers better correlation between visual assessment and instrumental pass/fail (Clark et al. 1984). The JPC-79, CMC and CIEDE 2000 formulae are based on L, C, h and they offer better correlation with visual assessment (Luo et al. 2001; Luo 2001). This leads to single number shade passing (only ΔE value) and we can do away with three-dimensional tolerance settings. It is confi rmed with statistical data that the CMC colour difference unit based on CIELCH colour space is more uniform and independent of any hue and agrees very well with visual assessment. The CIELAB 1976 formula has given very serious problems in paint sample evaluation and the only solution was to fi x the three-dimensional tolerance limits for each colour. It has been successfully used in industry and it is still quite popular. If you want to use single number shade passing then you have to use

•••• ••••

•••

•• ••••b*

c*

a*

Hue angle

Cha

nge

in h

ue a

ngle

S

12.4 CIELCH tolerance limits. Elliptical tolerance limit: C and h – all samples falling outside ellipse will be failed as hue changes are not acceptable to eye. In this case, samples which are failed in CIELAB tolerance limit are passed in CIELCH and vice versa.


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CIELCH formula (McDonald 1987) and if you want to continue to use CIELAB 76, then use three-dimensional tolerance limits for pass/fail.

Colour difference tolerances are designed as boundaries in colour space within which acceptable colours of the product fall. The boundaries do not necessarily correlate with perceptibility of difference but rather with the limits of acceptability. The standard colour may not be in the centre of the bounded region but may be displaced to one side. For example, where subsequent yellowing may occur, the tolerance of the yellow-blue dimension might be +0.1, –0.8 units. For defi ning such colour difference tolerances, a three-dimensional description of the colour difference allowed can be considerably more helpful than a one-number tolerance such as ΔE (total colour difference). A colour difference expressed in terms of ΔL, Δa, Δb, provides a better guide to the needed formulation correction. Such three-term tolerances are readily reducible to graphs for showing acceptability. A very small change in a tristimulus value, particularly in the red-green dimension, as indicated by the X-value, can represent a fairly substantial colour difference.

12.5 CIELAB vs. CIELCH – CIELAB tolerances are rectangular in nature and CIELCH tolerances are elliptical. Samples outside the ellipse are not passed as hue changes are not acceptable to the eye and this agrees with visual observations.


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For many colours of practical interest a 0.02 variation (in X) causes a 1.0 CMC unit change.

We use the tristimulus system (X, Y, and Z) to represent visual colour difference evaluations. Before the introduction of the CMC and CIE 94 formulae, the author had worked on a decorative paint system based on alkyd resins. The single number shade passing was not giving satisfactory results or good visual correlation and the CIELAB 1976 formula was not very satisfactory, although three-dimensional tolerance limits using FMC-II formula were acceptable. After measuring a number of batches and correlating with visual observations, three-dimensional tolerance limits were fi xed for each colour. The tolerance limits for a few colours are given in Table 12.1. We can see from the tolerance limits of different colours that each colour has specifi c preferred tonal variations (Gangakhedkar 2003).

It is observed that:

• In paint applications FMC-II agrees well with visual assessment. For small colour differences, CIELAB is not in agreement with visual assessment. For yellows and bright reds, FMC-II offers a better solution. Now we can use CMC, CIE 94 or CIE DE 2000 colour difference equations which are much better. Some more work is needed in this area.

• The single number shade passing concept (total ΔE) based on CIELAB does not work properly. We must use three-dimensional tolerance limits while using the CIELAB equation.

Table 12.1 A case study – acceptance tolerance limits (FMC-II)

S. no. Colour DCRG DCYB DL

1 P.O. Red 0.80 0.50 0.20 −2.50 −0.60 −0.80 2 Golden Brown 0.20 1.00 0.50 −1.00 – −0.50 3 Satin Blue 0.80 0.10 0.20 – −0.80 −1.00 4 Royal Ivory 0.20 1.00 0.50 −0.50 −0.20 −0.10 5 Oxford Blue 0.80 0.80 – −1.00 −0.20 −1.00 6 Jade Green 2.00 0.50 0.50 – 0.50 −0.50 7 Brilliant White 0.60 0.50 0.50 −0.20 −0.50 −0.50 8 Olive Green 0.50 0.50 0.50 −0.50 −0.50 −0.50 9 Pale Rose 1.80 0.60 0.50 −0.30 −0.40 −0.5010 Off White 0.20 1.00 – −1.00 −0.10 −1.00


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12.2.5 Colour strength

The strength of any colorant (dyestuff / pigment) is related to the absorption property. We measure refl ectance, not absorbance and it is known that when refl ectance is more, absorbance is less. The Kubelka-Munk theory gives the relation:

{K/ S = {(1- R)2 / 2 R} [12.8]

where R is refl ectance, K is the absorbance and S is the scattering. The K/S curve always has characteristics of every colorant and colour strength is defi ned as

Strength = [{(1- R)2/ 2 R} batch / {(1-2R)2/ 2R} standard] × 100 [12.9]

We can determine colour strength using the following method:

1 R min (absorbance maxima) We can fi nd the lowest value of R (which is the maximum value of

absorbance) and obtain K/S values of the sample and standard and compute the strength. This is generally accepted and more or less agrees with visual observation. In commercial software packages, it is automatically done by the program.

2 At given wavelength When comparing two dyestuffs or pigments, they may have different R

minima. In such cases we have to compute the strength based on the wavelength of the standard having lowest refl ectance values. This will give correct assessment of the strength.

3 Based on tristimulus values X, Y, Z, X & Y (average), Z & Y (average) If samples are Red/Green, then use the ratio of X-tristimulus value and if

samples are Yellow/Blue, then use ratio of Z-tristimulus values. If we want to consider the light/dark property then it is best to use combinations such as average of X and Y (for Red/Green) and Z and Y (for Yellow/Blue).

4 Integrated wavelengths Commercial software packages use an integrated wavelength approach for

computation of colour strength. In this case, strength is calculated at each wavelength and the average is taken as real strength of the colorant. This is now widely used in the paint industry. However, sometimes it may not give the correct strength of the colorant.

5 Strength and colour difference relationship We have to look at the strength and colour difference ‘as it is’. Strength may be

acceptable but colour difference is too much or strength may not be acceptable but colour difference is within the tolerance limit. Generally, when the strength of the pigment is high, we can adjust it by loading less. Taking this practical approach into consideration, mathematically we can predict the refl ectance/absorbance curve of any given sample and predict the expected colour difference ‘as it is’ and strength and colour difference ‘after adjustment of strength’.


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Figure 12.6 illustrates the computer output for strength (Gangakhedkar 2003). We can see that the strength of the pigment is 110.69 with a colour difference of 1.97. After adjusting to 10.69% of the strength, the colour difference will reduce to 0.95 units from 1.97. This may be acceptable. Sometimes, we may fi nd an increase in the colour difference value after adjustment of strength. We must remember to take into consideration both the parameters, strength and colour difference. Table 12.2 illustrates the strength calculations using the different methods mentioned above (Gangakhedkar 2003). Jade Green Standard and batch samples of 1% concentration were measured and strength was calculated based on different methods. In this case, variation was not signifi cant but it is noticed that the strength value will be different for different methods. The author’s experience indicates that strength at Rmin is always a correct representative.

12.2.6 Tinting strength of white pigments

In principle, instrumental tinting strength (TS) measurement may be achieved by varying the weight of colorant used with the standard until it matches the sample in lightness, now considered as R∞. In practice, it is easier to use the same weight of colorant with both standard and sample and calculate by K-M equations (from refl ective values) the weight needed to produce a match. This concept is

LAV Colour Difference : CIELab

7.13

Illuminant : D65 Observer : 10deg

Color Wavelength(nm) %R K/S As Is Adjusted

Green Std 420 7.18 5.4656

Green Batch 420 6.0499 110.69 100.00

Illu/Obs DE DL Da Db

As is Colour Difference 1 1.97 –1.73 –0.83 –0.43

Adjusted Colour Difference 1 0.95 –0.22 –0.84 –0.40

12.6 Computer output of colour strength ‘as it is’ and ‘after adjustment’.

Strength – Rminima.

Table 12.2 Strength calculations using different methods

Calculations based on Strength DE ‘as it is’ DE ‘after adjustment’

Rmin(600 nm) 84.95 2.32 0.85Selected wavelength (580 nm) 86.67 2.32 0.47X value 84.62 2.32 0.25Y value 84.47 2.32 0.25Z value 83.54 2.32 0.25


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used in the ASTM method and the basis for the calculation is discussed in the Pigment Hand Book, Vol. III (Paton 1973). For an opaque fi lm consisting chiefl y of vehicle, the white pigment under test and a little tinting colorant, the S-value arises (essentially) from the white pigment and the K-value from the colorant. Therefore the TS of the white pigment is proportional to its S-value. That is,

TS white pigment = [{(1-R)2 / 2R} STD / {(1-R)2 / 2R} Batch] × 100 [12.10]

For the colorant, the magnitude of the K is proportional to the amount (weight) present. That is, remembering the symbols for weight of colorant used. The tinting strength is defi ned as

[{(1- R)2/ 2 R} Batch / {(1-2R)2/ 2R} STD.] × 100 [12.11]

This equation shows that the TS of white pigments may be measured instrumentally by making use of calculations while still maintaining the classical concept of the visual TS (Committee on Colorimetry 1953). The use of an instrumental TS method has many advantages over the classical visual method besides requiring only a single preparation of sample and standard. The instrumental test is objective. There are sometimes advantages to defi ning this TS value as equal to its S-value measured in the same paint without the colorant. This produces TS values of the test sample in the same physical units used for the S-value, thus allowing direct calculation of the hiding power from tinting strength measurements.

12.2.7 Whiteness and yellowness indices

As with any other colour, three numbers are necessary for the complete identifi cation of any white. When using L, a, b, it is almost always shown that the –b dimension measuring yellowness is the most critical of the three. In fact, the use of bluing, which decreases L but increases blueness, produces a visually whiter product. In studies, –b is typically found to be three or four times as important as L, which is next important. The -a dimension is the least important in normal practice. White colours are to be represented on graphs having the two dimensions (L and b).

Whiteness as a single number index rather than three colour scale parameters is preferred for whiteness assessment. Various whiteness indices exist and are used for numerous applications (Hunter 1958 & 1960). The most common whiteness index available is WI E313, which is defi ned by ASTM Designation E313–73 (Reapproved 1993), ‘Standard Test Method for Indexes of Whiteness and Yellowness of Near-White, Opaque Materials’. This index may be applied to most opaque materials. Whiteness values assigned to colored items are meaningless.

WI CIE is described in CIE Publication 15.2 (1976), pages 36–38. It is recommended for use with CIE illuminant D65 and may be used with either the 2° or the 10° observer. This index is often used by the textile, paint and plastics industries for measuring product whiteness.


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The CIE Whiteness

The CIE Whiteness formula is to be used for comparisons of the whiteness of samples evaluated for CIE standard illuminant D65/10° measured on the same instrument at nearly the same time.

* WI = Y + 800 (xn – x) + 1700 (yn – y) [12.12]

which involves three dimensions (CIE Y, x y) of colour of specimen, with xn and yn being the chromaticity co-ordinates of the achromatic point for the chosen observer (2° and 10°).

The higher the value of W, the higher the whiteness and a perfect refl ecting diffuser will have WI = 100. For Fluorescent Whitening Agent (FWA), W >> 100.

T = 900 (Xn – X) – 650 (Yn – Y) ¾ Tint.

In 1987, the CIE Whiteness formula was incorporated in ISO, Part J02 and in 1989 it was written into AATCC Test Method 110 – 1989. The CIE Whiteness index generally seems to agree well with visual assessment but different researchers reported different formulae (Ganz 1975). One can use the formula based on agreement between buyers and sellers of colour products. ASTM (E313) and ASTM 1925 are the most popular whiteness indices used in industry (Hunter 1958 & 1960).

Yellowness index

Yellowness is a property important in paint industry. One can always fi nd yellowing of paint, particularly white paint. The purity of white pigments may be determined based on the amount of yellowness present. Also, some paints degrade and yellow with exposure to sunlight, temperature, or other environmental factors during use. Thus, yellowness has become an important variable to measure in the paint industry.

There are different types of yellowness indices available, depending on the type of product being measured. Two of the most common are ASTM D 1925 Yellowness and ASTM Designation E313-73 (Reapproved 1993), ‘Standard Test Method for Indexes of Whiteness and Yellowness of Near-White, Opaque Materials’.

ASTM D 1925 yellowness

YI (1925) = {(128X – 106Z)}/ Y [12.13]

ASTM E313 yellowness

YI (ASTM-313) = 100 (Y-100Z/Z0) / Y [12.14]


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where Y and Z are tristimulus values and Z0 is the Z value of a perfect diffuser. This is applicable to nearly white or colourless samples. (For details of whiteness and yellowness measurements see Chapter 4.)

12.2.8 Opacity/contrast ratio and hiding power

Opacity

The opacity of a material is an indication of how much light passes through the material. The higher the opacity, the lower the amount of light that can pass through. Generally, opacity is calculated from refl ectance measurements of the material with a black backing and the material with a white backing.

Opacity = [Yblack backing / Ywhite backing] [12.15]

where Y is the CIE tristimulus value.

Contrast ratio (CR)

The ratio of luminous refl ectance of a specimen backed with black material (RB) specifi ed refl ectance to refl ectance of the same specimen backed with white material (RW) specifi ed refl ectance is

CR = RB/ RW. [12.16]

Hiding power

Determination of the hiding power of non-coloured paints

The hiding power (HP) of paint is understood to be its ability to eliminate the contrast between a black and a white substrate to the extent that the refl ectance obtained over a black substrate is 98% of that obtained over a white substrate. The hiding power of paint measures its ability to obscure a background of contrasting colour. White pigments scatter incident visible light at all wavelengths whereas coloured pigments absorb incident visible light at characteristic wavelengths.

Measurement of hiding power for white versus coloured paints

We have to get HP for pastel and dark-coloured paints. It is easier to absorb light with a coloured pigment (and thus gain hiding) than it is to increase the amount of light scattered in a white paint. In fact there is little interest in measuring the HP of very dark-coloured paints because they have more than enough HP for conventional usage. Only TiO2 will produce high-hiding white paints whereas many coloured pigments will produce high-hiding, dark coloured paints. Computation of hiding power of white paint is given in detail in the Pigment Hand Book, Vol III (Paton 1973). It is based on computation of scattering


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coeffi cient (S) of TiO2. Using K-M equations, we can calculate absolute scattering (S) and absorbance (K) coeffi cients. We can determine the hiding power of white (TiO2) based on S which is representative of the hiding power of white. The mathematical formulae are available in the above mentioned book.

12.2.9 Pigment load

For pigment load calculation (Parker 1973) we have to use the concept of absolute K and S (not relative). Contrast ratio, thickness and pigment load decide the quality of paint or plastic material and optimization is very important for determining the loading of pigment. This is taken into consideration in the pigment loading program based on calculations of absolute K and S (see Paton 1973). Table 12.3 gives the hiding power data of two white pigments taking into consideration thickness and contrast ratio. In answer to ‘How to arrive at the optimum concentrations of pigments so that the desired shade gives complete hiding or opacity?’, pigment load calculations can be used (Gangakhedkar 1992; Paton 1973).

12.3 Sample preparation for colour measurement

Tips for preparing paint samples (Gangakhedkar 2003) for colour measurement are given below.

• The pigment must be fully developed, preferably by pre-dispersion in concentrated form. Be on the lookout for streaks, specks and other indications of incomplete dispersion.

• For colour measurement by instrument, an opaque sample is recommended.• Side by side comparison with the standard, either by drawdown or by

press-out, is preferred. This is even more important if transparent samples are being measured so that differences due to fi lm thickness are minimized.

• Make certain that there are no surface blemishes, bronzing or differences in gloss.

• Be extremely cautious about the effects caused by temperature.• Be on the lookout for indications of incompatibility such as fl occulation,

crazing or slight solubility of pigment.• Design the test to examine one property of the pigment.• Use one test to examine colour. Use another test to examine for ease of

dispersion and so on.

Table 12.3 Hiding power calculation (white paint)

Sample RG RB RW Tmil CR a b ST S HP (sq/lit)

(A) White 7100 9000 9140 2.5630 0.9851 0.00334 0.08181 12.1559 4.7426 775.04(B) White 7250 9100 9250 2.0582 0.9770 1.00052 0.03413 10.6121 5.1555 735.74


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• Note that coloured samples are subjected to the vagaries caused by normal sample preparation errors. Take the time to prepare duplicate samples.

• First check for reproducibility and repeatability. Measure the colour differences and strength of duplicate samples. While fi xing the tolerances, consider the variations observed in the duplicate or reproduced sample.

• Even the most carefully conceived, simplest, and most carefully run test will exhibit some difference in sample preparation.

• Duplicate samples had an average measured colour difference of 0.2 units.• In no case did the duplicate samples exactly match one another.• Despite our best efforts to control variations, we will fi nd that strength

differences between duplicates are as much as two or three percent.• Test sample surfaces must be the same with regard to gloss, texture and

surface regularity.• Surfaces must also be free from fi sh eyes, pinholes, bronzing, crazing, etc.• Differences in surface gloss account for the colour difference.• The unthinking instrument might be showing us where we have gone wrong.

We must remember that instruments are intelligent machines which see only what we give them to see.

• In most cases, the source of the problem is an improperly prepared sample or a sample preparation procedure that has more errors in it than we realize.

12.4 Pigment quality control

Note the following tips for application techniques:

• Always use a fi lm applicator to achieve uniform fi lm thickness. Apply standard and sample side by side. Check repeatability and reproducibility of laboratory technique.

• Make visual observations and compare with physical (instrumental) measurements.

• Establish tolerance limits for each hue, i.e. red, green, blue, etc. Always remember ‘Look and Think’. The smaller the tolerance limit, the more problems in reproduction. Be reasonable and select the tolerance limit based on supplier and manufacturer information.

• Take maximum care before presenting samples for spectroscopic measurements.• As a computer reading can be taken only after complete drying, the approval

time is considerably increased. The solution to this problem is to compare sample and standard side by side during wet or semi-dry conditions. Approval time can be reduced if comparison is made under identical conditions. Use of the IR drying technique is recommended as it does not produce colour drift.


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• Laboratory data is to be statistically analysed and correlation between laboratory and production batches is to be established by creating analytical history.

• For coloured pigments, determine the strength at maximum absorption (Rmin). In case of yellow and red pigments, the absorption region is a fl at spectral curve. So, we may select the danger point. We cannot correct for the deviations in chromaticity of incoming pigment but can always correct for strength by adding required pigment or clear.

• Use strength correction factors for assembling the production batches (Gangakhedkar 2003).

In the paint development laboratory, we have to test pigments and make primaries of colorants for the database required for the match prediction. We should also remember the following points.

• Remember, you are not matching formulae you have already established. Your objective should be to get the least metameric and low cost formulation.

• Primaries for database: We have to prepare separate primaries based on the production process, i.e. separate primaries for sand mill processing and ball mill processing for the same pigment (1 pass–2 pass). We have to make new primaries if colour development (strength and chromaticity) is very much affected due to the process. Then we have to collect the data of optical properties for each pigment based on the pigment-medium-process combination.

• Water base paints: We can use the pastes of unknown pigmentation, but care should be taken while mixing with the black: in no case is bronzing to be observed and clear or proper pigmentation is to be prepared. The concentration of the mix with white is to be selected so that refl ectance of the mix with black will not be more than that of the mix with white. This will depend upon the strength/depth of colour paste. In the case of colours where the TiO2 percentage is at 4%, a separate white base is to be prepared.

• Specular excluded (SCE) mode: Gloss plays a very important role. In a computer program, proper calibration values of SCE are to be stored and after getting the correct data we can use this SCE mode of operation to run a match program with SCE data.

• Metamerism/Non-metamerism: We will see the colour difference (ΔE) in A, D and CW F illuminants and look at Metamerism Index.

• Colour difference unit: Our eye is non-linear and equal colour differences in different hues are not equivalent to each other. In one of the author's case studies, we selected three paints of three different hues (red, yellow and green). Visually matches were very close between the sample and standard of these three different hues, but instrumental measurements indicated large deviations (see Table 12.4). This case study indicates why there are differences in visual and instrumental assessment of colour.

• Colour difference in metallics: If we measure the metallic colour at four different places and compare with the standard, considerable colour differences


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are noticed. While fi xing tolerances, we should note down these infl uences and deviations.

• If we prepare the samples the same way each time, then each sample will be the same as all previous and all subsequent ones. This is called reproducibility of making samples. We must realize that in setting colour specifi cation, we must have good repeatability of sample making. Most carefully run tests will exhibit some differences in sample preparation. In no case did the duplicate samples exactly match one another. We may get a colour difference in the range 0.04–0.6 CMC units.

• The pigment manufacturers usually allow a maximum batch to batch strength variation from standard of plus or minus three percent and total colour difference ΔE = 0.5 CMC units. This is to be strictly controlled.

12.4.1 Pigment evaluations

For proper evaluation of pigment, bases and white, note the following points (Gangakhedkar 2003).

• Quality of pigment or bases is based on pigment dispersion. The shade or mass tone of any pigment will often vary signifi cantly, depending upon how well the pigment is dispersed. The maximum strength of pigment can be obtained with complete dispersion but the presence of streaks and specks indicate incomplete dispersion of pigment. This will result in inaccuracies in colour measurement.

• There is a relationship between shear viscosity and processing temperature which affects pigment colour development. For example, phthalo blue milled at 275°F and 300°F will have signifi cantly different tone and strength.

• System failures: Flocculation, bleeding, spewing, crazing, mottling and migration are due to system failures. Colour measurement will be a futile exercise if system failures are observed in pigment dispersion and paint making.

• Sample surface differences: If there is a difference in surface appearance (e.g. gloss), then colour measurement will be inaccurate. Gloss, texture and surface irregularity will create problems in colour measurement while differences in surface gloss account for the colour differences.

Table 12.4 Color difference in FMCII and CIELAB unit for three different hues

Shade Visual observation FMCII CIELAB

1 P.O. Red Close match 1.6 0.82 G. Yellow Close acceptable 1.8 0.63 Green Close match 3.1 1.4


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12.4.2 Uncertainties in sample preparation

To avoid uncertainties, we must note the following.

• Prepare a standard sample along with a batch sample together to minimize small variations.

• In the case of paint/pigments, samples have the same drawdown and should show complete hiding.

• In the case of paint samples, wide variations are observed in duplicate drawdown when drawdown is made at either fast or slow speed. This is due to the different fi lm thickness of the drawdown. We can reduce the variations to a minimum by using opaque samples.

• Samples made with different equipment will show large variations.• Samples made by different operators will show considerable variations.• In paint applications, we have to remember variations occur due to different

variables such as dispersion technique, temperature effect, system failures, sample surface differences and anomalous pigment behaviour.

• Geometric metamerism plays an important role in metallic colour matching.

12.5 Problems in match prediction: paint applications

Variables in paint processing make the problem complicated and areas of poor control are to be considered along with the evaluation of process areas (Gangakhedkar 2003). Some of the factors affecting the prediction of matches are:

• application errors in sample preparation brushing/spraying/human errors,• role of dispersion and dispersibility (machine variables – resin/pigment

behaviour),• role of white TiO2 and extender in hiding,• orientation of metallic fl akes.

Variables such as changing standards, effi ciency of grinding machines with respect to required dispersions, colour and strength variations in in-coming pigment batches, fl oating, fl ooding, fl occulation, settling of pigments, skinning, strength of tinters (bases and whites), substrate variations on thickness developed, paint application methods such as brushing, spraying, stoving create colour matching problems.

12.6 Computer colour matching for paints

Colorant formulation or what is commonly but inaccurately termed colour matching, is the determination of colorant concentrations required in the application on a substrate so that the combined colour of these will be the same as that of the standard. Recent developments in the chemical technology of dyes and pigments have given the modern colorist an almost endless number of colorants


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with which nearly any colour that can be perceived can be reproduced on a variety of substrates. If this is to be done faster and at lower cost, then we have to replace the trial and error method by a more scientifi c method based on colour measuring instruments integrated with personal computers.

Davidson and Hemmendinger (1966) revolutionized the concept of colour matching by introducing the analogue Colorant Mixture Computer (COMIC) in 1965. In computer colour matching, we have to fi rst make an attempt to quantify colours by virtue of a unique refl ectance pattern that each colour exhibits and then to match this unique pattern by a blend of various pigments. The blend that gives an identical refl ectance pattern is an exact match for the desired colour. For this, we have to collect the spectral refl ectance data for both the standard colour and the pigments. This data is then to be analysed by using the Turbid-Medium theory (Kubelka and Munk 1931). K-M equations do work well when properly applied, depending upon the type of the system. Optical properties of pigments are described by famous Kubelka-Munk equations which are used to determine the absorption coeffi cient (K) and scattering coeffi cient (S) of the colorants used. The basic relationship of the Kubelka-Munk equation can be expressed in the following equation:

K/S = [{(1 – R)2/ 2 R}] [12.17]

where R is the refl ectance value of samples at a given wavelength. This equation is valid for an opaque paint fi lm at any wavelength in the visible region. K-M equations do work well when properly applied, depending upon the type of the system. For example in a paint system, while applying the K-M theory, we have to make an assumption that the total absorption coeffi cient for a paint fi lm is the sum of the absorption coeffi cients of each colorant weighted by its concentration and similarly for the scattering coeffi cients. We have to determine separate K and S values and use the two constant Kubelka-Munk theories. Using additive concept, we can write:-

K = C1 K1 + C2 K2 + C3 K3 + Cw Kw [12.18]

S = C1 S1 + C2 S2 + C3 S3 + Cw Sw [12.19]

where C1 C2 C3 are concentrations of coloured pigments K1 K2 K3 are absorption coeffi cients S1 S2 S3 are scattering coeffi cients Cw is concentration of white pigment Kw and Sw are absorbance and scattering coeffi cients of white.

Using characteristic optical properties of colorants (K and S) we can compute the colorant concentrations in a desired colour and colour mixture theories based on different mathematical models are available (Kuehni, 1975). An enormous amount of calculations are involved in solving colour matching equations but a digital computer provides the fastest solution to this problem because it can be interfaced directly to the colour measuring instrument.


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The basic equations used in computer colour matching are based on the Tristimulus Match and Two Constant Theory. Match prediction mathematics is given in details by Eugene Allen (1966) and Rolf Kuehni (1975), in his book Computer Colorant Formulation, reviewed all the earlier work and presented the complete mathematical know-how for the colour matching problem. Gangakhedkar (1991, 1992), in his two books, worked out the practical problems and explained how to write the software program based on all earlier works. We have to prepare calibration samples for creating a database for match prediction and the colour computer system is a powerful tool for predicting paint formulation based on low cost and least metameric matches. The ‘right fi rst time match’ is the dream of any paint formulator and new developments in the computer colour matching (CCM) technique offer a complete solution to colour matching problems.

12.7 Colour control system

A colour control system consists of:

• spectrophotometer,• computer hardware and system software,• colour programs, and• application technology.

The real backbone of a colour control system is the colour software and application technology required for specifi c colour problems. The colour software necessary for the solution of most colour problems is relatively complex and there are a number of commercial colour systems available on the market based on different spectrophotometers and colour matching software. Computer colour matching (CCM) today is quite different from what CCM was in the 1970s and ‘computer tinting’ is a reality at the point-of-sale (POS) terminal. One can make any given colour quickly by using automatic paint dispensing machines interfaced with colour matching computer systems, a most important trend in the paint industry. Portable spectrophotometers are also now available with very high accuracy and at reasonably affordable costs.

‘Do it yourself’ is a concept used in the paint industry in the USA while a ‘POS’ system equipped with a mobile computer colour matching system is now a reality and will help to expand the coating business.

12.7.1 Colour measuring instruments

A spectrophotometer, colour measuring instrument can make the same kinds of judgement that human observers do. Our main objective in the use of a colour-measuring instrument is to fi nd a meaningful correlation with visual perception, but please note that an instrument has nowhere near the versatility of the eye.


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Design features of modern colour spectrophotometers are discussed in detail in other chapters, but we will mention some important points which will help us in the proper selection of an instrument.

12.7.2 Today’s colour measuring instruments

It is to be noted that the race between competing designs seems to be over – at least for the time being. Today’s industrial instrument is most likely to have a pulsed xenon fl ash source and a replica grating monochromator with diode array detector. The most important characteristics of the instruments are:

• precision (repeatability, reproducibility and accuracy)• measurement geometry and the size of measured area• UV calibration of the light source• different sample holders.

Additional features desired are:

• automatic identifi cation of aperture• 45/ 0° geometry with UV calibration facility• LED based instrument• numerical gloss control• numerical UV control.

We have to make the choice of instrument based on real needs and type of application. Most of the top of the line instruments have excellent agreement with the National Physical Laboratory and the Hemmendinger Colour Laboratory (RPI 1978; AIC 1981).

12.7.3 Instrument performance

The author has experienced many diffi culties as his customers were pigment suppliers and paint manufacturers with multiple production plants using colour measurement methods for quality assurance. Users were very critical for setting-up numerical tolerance limits for pass/fail (paint manufacturers as buyer and pigment manufacturers as sellers). Performance and functioning of instruments at each location was critical and we had to look into problems related to:

• standardization• instrument calibration• short term repeatability• long term repeatability• inter-instrument agreement• sample preparation procedures.


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12.7.4 Problems of instrument metamerism

If a company with multiple plants uses different models and different makes of instrument and uses colorimetric data for quality assurance, instrument metamerism can cause serious problems. It is related to geometry, aperture size, light source, design of integrating sphere, dispersing elements, detector system, standards used in calibration and the manufacturer’s design approach.

Ideally speaking, we should have the same instrumentation everywhere if colorimetric data is to be transferred and used in multiple plants. BCRA tile data will indicate the performance of instruments but cannot identify the problem of instrument metamerism. The problem is more serious if instruments are of different make or different models from the same manufacturer. It is to be noted that the role of instrument metamerism confuses the language of digital communication, but nowadays, inter-instrument agreement is fairly good and manufacturers are supplying special programs for this. There are problems when using instruments from different manufacturers for the reasons given above.

12.8 Measuring colour properties of wet paints

Wet-paint colour matching is a new trend in paint matching. A non-contact spectrophotometer is effectively used for wet-paint colour matching with suitable software for quality control and match prediction. We have to fi nd out the correlation between wet and dry paint and there are some limitations. However, colour measurement of wet paint is not a problem. How to make a database of wet-paint and fi nd correlations with dry-paint measurements is a major concern. Accuracy will be based on preparation of the database and storing of the wet and dry standards. We also have to make colour fi les for each product line, which is tedious. Wet-paint colour matching certainly saves considerable time and increases productivity but some more work is needed for industrial use. The most important component of a wet-paint colour system is the non-contact spectrophotometer for wet-paint measurement.

12.8.1 Non-contact spectrophotometer

Recently on-line colour measuring instruments were made available for wet-paint colour matching. The Minolta CF-1440 is an ideal non-contact spectrophotometer widely used in the paint industry (one can get details from the manufacturer’s website). Konica Minolta recently introduced a new portable non-contact model CR-241 which measures colour without touching the sample.

X-Rite offers a range of instruments, namely the TeleFlash 130, TeleFlash 445 and VERY COLOR spectrophotometers.


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12.8.2 Wet-paint colour matching

A number of wet-paint colour matching systems are in operation in industry with latest new generation software for wet-paint application, quality control and formulation; the description of the wet-paint software is the same as for dry-paint matching except for the non-contact spectrophotometer (Gangakhedkar 2003). Some of the features are as follows:

• W et-paint software is suitable for oil based alkyd paints/water based paints and industrial paints.

• Wet-paint software is used for quality control, fi rst trial matching, and correction of a batch. It is necessary to use a non-contact spectrophotometer, which measures colour from a distance of 10 cm.

• One has to prepare the colour fi le (data fi le) on a wet basis and standards must be measured on a wet basis.

• Wet matching helps save time, control paint product and increase productivity.

• On the contrary, with traditional methods by contact, for an oil paint to be controlled after its application on the substrate, we must wait for at least 12 hours for drying and may have to do up to two corrections. If everything is OK, still production stops for 24 hours, i.e. three working days of eight hours each. With wet-paint matching, we may match the shade in one hour.

• Precision and accuracy will depend on the data fi les and standardization, i.e. preparation accuracy.

• It is possible to work with all types of paint products. We make colour fi les by the same methods.

• Note that if the colour fi le is performed on wet then the standard must be measured in wet and all controls must be performed in wet.

12.9 Instant colour matching at the paint shop

A computer colour matching system is a must for any paint company for handling day-to-day colour problems of formulation, batch correction and perfect matching. This system is required for plant matching new colours, production correction and quality control. It is called the ‘mother’ system and has a complete database. A ‘daughter’ system with portable low cost instruments and limited colour software is installed at the paint dealer’s shop, commonly known as the Point of Sales (POS) system. Some of the features of mother/daughter colour systems are given below.

• Any mother system software consists of a complete package for formulation, batch correction and quality control.

• Formulation is based on calculation of load of pigments in consideration of desired covering for the applied thickness.


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• Correction of the fi rst imitation is expressed in addition and reformulation.• Automatic saving of the formulation in the Formula Book and automatic

search.• Choice of spectrophotometers: One can select any low, medium or top-of-the

range spectrophotometer from a leading manufacturer for the mother system and also a portable, low cost spectrophotometer for the daughter system. For example, the Tethered spectrophotometer from X-Rite is lightweight, fl exible, simple to use, and allows the user to take spectral measurements. It is based on 45°/ 0° geometry and is ideal for lower-volume paint retailers (see Fig. 12.7). The PocketSpec Spectrophotometer is a colour reader used by Benevue Colour Management System. It is accurate, low cost and reliable instrument (see Fig. 12.7).

• Wet-paint colour matching is a new trend in paint matching. It is necessary to buy a non-contact spectrophotometer and software suitable for wet-paint colour matching.

• Hardware and software interface with the POS system: Mother system software should be integrated to any tinting machine. There are a number of

Pocket spectrophotometer

035/067/149035/063/144

+0 –4 –5%diff = 3.59

Tethered spectrophotometer (X-rate)

12.7 Low cost spectrophotometers for POS.


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commercial paint dispensing systems on the world market. Mother system software and tinting machine hardware and software integration is a must for any good system.

• A POS system consists of three components: (1) base paints – whites and colorants, (2) tinting machines and (3) portable colour measuring instrument.

• POS tinting machines (CORAB/FLUID, etc.): Canister management and dosing management are made available by tinting machine manufacture, but one needs the ‘LINK’ package for integration and shakers for proper mixing.

• POS spectrophotometer: For on-spot colour measurement, select the required instrument for on-spot colour measurement of custom colour.

• In a POS system, the Formula Book with automatic search program is stored in the computer along with data of colour cards, individual colour chips, and fan decks.

• The mother system has a complete database which is transferred to the POS (daughter) system either by transferring the required database or through internet connectivity. Any given colour can be made quickly by using an automatic paint dispensing machine interfaced with POS colour matching systems.

12.9.1 Internet based colour matching at point of sale (POS)

There is an innovative development in the POS colour system based on internet technology (Benevue 2009). It is not based on normally used conventional K-M theory for match prediction. In this new POS colour system, you need a colour sensor and access to the web page of the service provider. There is no need to determine optical properties (K and S coeffi cients) of the bases and colorants. You have just to supply the existing formulae of the fan deck colours in electronic form and the service provider’s software will take care of matching any given custom colour. There is secrecy surrounding the software package as the manufacturers do not want to disclose the mathematics of colour matching. It is mainly database driven and for every colour, there is a formula. Every colour has a unique colour specifi cation such as X, Y, Z or L, a, b. This is correlated to the colour formula. Once you have a large number of colours covering the full colour gamut and you have the established formula bank, you can predict the tint formula for any custom colour by using database and powerful mathematics. Benevue has done a lot of work in this fi eld and the author knows this product’s development. The main features of this system are:

• Zap your inspiration: Take your colour reader and simply measure the desired custom color in terms of three numbers (R, G, and B). See Fig. 12.8.

• Log on to your internet website by entering your user name and password (see Fig. 12.9).


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12.8 Colour reader of Benevue Colour Management System. The user scans the custom colour and gets three numbers (R, G, B).

12.9 Benevue website: Enter user name and password for access to the service provider’s central server which has software and database.

• You will see various options available in the menu – book formula, closest colour and tint by name, etc. (Figs 12.10, 12.11 and 12.12).

• You enter the three colour values of custom colour (see Fig. 12.13), and select the desired option, say ‘Tint by Reading’.

• You will get the exact colour formula by selecting the option ‘Tint by Reading’ (see Fig. 12.14).

• The computer instantly gives the tinting formula which can be sent to an automatic dispenser or you can mix it manually.


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12.10 Book formula: Users select the colour ID of the fan deck and retrieve the tint formula.

12.11 Closest colour: After entering the three numbers (R, G, B) of the custom colour, users fi nd the closest match from their own fan deck colour. In this output, three close matches are found. The user can visually compare and fi nd the best match.


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12.12 Tint by name: In this option, users select the fan deck name and sample ID. The user can store a large number of fan deck colours and obtain tint formulae by using this option.

12.13 Unit ID is the ID of the colour reader. After measuring the custom colour, the user just enters the three colour numbers – Red Value (R), Green Value (G) and Blue Value (B) of the custom colour to get a tint formula. The user can also select the quantity. To fi nd the formula, click ‘Find Formula’.


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• Number of fan deck colours and their formulae are stored on central server along with matching software and one can get the formula by using the name of colour in fan deck or any other options.

The author has worked with the Benevue Colour Management System and introduced it to a leading paint company in India and established the technology at a number of paint dealer shops. Results are extremely good. Illustrations mentioned here are the actual data obtained by the author who has matched hundreds of pastel and saturated colours using the Benevue Colour Management System. It is a unique system offering low cost, internet based colour matching solutions.

12.9.2 Conventional CCM vs internet based point of sale

• In a conventional CCM system, the daughter system at the dealer’s shop needs an experienced operator and the database is updated as instructed by the mother system.

• An internet based shop system can be updated automatically as it is on a central server.

• In an internet based colour management system, the centralized database of the fan deck, Formula Book, etc., is on the main server which is instantly available to any paint dealer shop on the internet. The dealer need not worry about updates.

• An internet based system is equipped for instant use of the Formula Book, closet formula, formula for any product line, formula for any base/any tint base.

12.14 ‘Tint by reading’ gives an instant colour match of a measured custom colour. In this option, the user just enters the three colour numbers of the measured sample to get a tinting formula.


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• The central server has an ‘eye’ on the paint dealer shop and one can get all the information about the usage of the dealer, number of new custom colours matched, new formulae found, competitor’s colours popularly used, regional sales, dealers sales, etc. It offers a unique data mining facility which is extremely useful for sales/marketing analysis.

12.10 Colour matching of automotive paints

Computer colour matching programs for automotive paints are now addressing the needs of opaque, translucent or gonioapparent paints. Point of sale colour matching started with consumer paints but is now common also in automotive refi nish and industrial fi nishes.

The following three factors are required to be successful with instrumental colour matching.

1 The right measurement geometry for the specifi c paint.2 Appearance matching: commonly including factors such as gloss, haze, etc.,

gonioapparent colours must also match in apparent texture and sparkle.3 A reproducible application procedure, representative of the end-use: Colour

may not be the same as from brushing or roller-coating, but colour match is not very critical. In automotive OEM, robotic spraying in a controlled environment, representative of assembly line conditions is necessary (X-Rite 2009; Hunterlab 2009).

12.10.1 Measurement of metallic and pearlescent colours

A portable multi-angle spectrophotometer can measure metallic, pearlescent, and special effect colours on curved surfaces with fi ve angles of measurement: 15°, 25°, 45°, 75°, and 110°. Measurements are taken remotely and can be uploaded to a PC. The full range of angular viewing allows accurate evaluation of the changes exhibited in various colour fi nishes.

Sample preparation, procedure for creation of database and fi xing tolerance limits for pass/fail for metallic colours are important points to be noted for success of this technique.

Computer colour matching programs for automotive paints are now addressing the needs of opaque, translucent or gonioapparent paints. Point of sale colour matching started with consumer paints but is now common also in automotive refi nish and industrial fi nishes (X-Rite 2009).

12.10.2 Portable multi-angle spectrophotometer

The portable multi-angle spectrophotometer is a next generation measurement tool designed for consistent, precise colour measurement of metallic, pearlescent,


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and other complex special effect fi nishes. The unit provides 10 measurement angles and two illumination angles to create a unique master profi le of each colour that serves as a benchmark for optimizing colour communication from initial design, through formulation, processing, and quality assurance. These instruments are used in automotive paint industry with partial success.

The MA98 multi-angle spectrophotometer (Fig. 12.15) features a rugged, compact, ergonomically effi cient design. Colour values are obtained for the following colorimetric systems: L*a*b*, ΔL*, Δa*, Δb*, C*, h°, ΔL*, ΔC*, ΔH* Flop Index, ΔE ab, ΔECMC, ΔE 94, ΔE 2000. It meets DIN and ASTM standards.

Minolta instrument

The CM-512m3 is a multi-angle spectrophotometer (Fig 12.16) especially designed for metallic paints. It uses an exclusive geometry with 0° viewing and illumination at 25°, 45° and 75° angles. This special geometry is truly symmetric and therefore free of orientation errors often found in traditional multi-angle instruments. In addition, this illumination gives very accurate results even when curved surfaces are measured. Therefore diffi cult samples such as vehicle mirror bodies or door handles can be measured easily. The CM-512m3 is extremely rugged because it is free of any moving parts (Konikam Minolta 2009). The temperature data of the sample is taken at each measurement. This data is memorized with each sample reading, so that thermochromatic effects can be analysed. Another practical feature

CM-512m3Spectrophotometer (spectral type, multi-angle)

Multi-angle spectrophotometer for metallic colors.

The New Technology X-Rite MA98TM

Portable Multi-Angle spectrophotometer

XDNATM

12.15 Multi-angle instruments from Minolta and X-Rite.


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is the infrared data transfer to an external PC, which avoids troublesome cable connections.

Measurement is not much of a problem as suitable software is required for the desired application. There are a few research groups working on these aspects but no more details are available due to confi dentiality issues.

12.11 Future trends

At present, colour matching software is based on the Kubelka-Munk theory or a modifi cation of the K-M theory. Now researchers are looking beyond this and new software will be based on databases and internet technology.

Low cost and cheaper spectrophotometers will be available in the market in coming years and custom colour matching will be driving the market. Colour matching problems with metallic, special effect pigments and fl uorescent colours will be solved with new measurement techniques and mathematical solutions will be obtained for developing new colour software for increasing the accuracy of match prediction. Wet-paint colour matching is still a problem and new low cost instruments are to be developed. New pigments, resins and processing equipments for paints and coating will generate new colour matching problems and a colour measurement tool will be very useful for new developments. ‘Colour on demand’ will be the challenge and the paint formulator will have to use colour measurement tools intelligently. ‘Do it yourself’ is also a concept used in the market. As POS systems equipped with a mobile computer colour matching system will help to expand the coating business, colour measurement will continue to be an important tool which will have to be used effectively.

12.12 Conclusions

We have discussed in detail how to fi x three-dimensional tolerance limits (DL, Da, Db) or a single number tolerance limit (DE) for paint quality control. We have seen how to quantify various colour related properties such as whiteness index, yellowness index, colour strength of pigment, opacity, contrast ratio and hiding power.

We have reviewed various theoretical and practical aspects such as quality control of incoming pigment, pass/fail decisions for outgoing paint products, colour formulation, production batch correction, quantifying colour related properties such as pigment load and opacity. We covered procedures for preparation of colour samples for quality control of paint and new techniques of wet-paint colour matching. We have looked at new colour matching systems for a paint shop and new developments based on internet technology for the POS system. We looked into measurement of metallic colours and special effect pigments, and measurement techniques for automotive paints. A new digital colour management system for paint dealer shops was illustrated indicating innovative trends in ‘computer tinting’.


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Proceedings of conferences of CIE, AIC, SDC, AATCC, and ISCC.Publications of CIE: Central Bureau of CIE, Kegelgasse 27, and A-1030 Vienna, Austria

(Email: [email protected]).See on internet: NEWSGOUP: Sci.eng.color – a number of issues related to colour

measurement were raised and colour experts have answered them. The author extracted and compiled information and presented it in ‘Color Clinic’, J. Colour Technology and Management, Vol 1, No 9, September 2004, pp 4–28.

Web sites of colour instrument manufacturers (Datacolor, X-Rite/ GretagMacbeth, Hunterlab, Orintex).

http://www.datacolor.com: Latest colour matching software, portable spectrophotometers and articles on colour measurement by colour experts – Ken Butts and others.

http://www.x-rite.com: Look for recently introduced on-line, non-contact and multi-angle spectrophotometers.

http://www.konicaminolta.com: Look for non-contact spectrophotometers.See Hunterlab web site: http://www.hunterlab.com Look for colour education with the

topic ‘Color theory’. This includes an excellent presentation ‘The Basics of Color Perception and Measurement’ and papers related to test methods, instrument theory as well as colour theory and colour scales. Also see articles and papers and the book The Measurement of Appearance, Second Edition, edited by Richard S. Hunter and Richard W. Harold, published by John Wiley & Sons, Inc., New York, 411 pp. (ISBN 0-471-83006-2) 1987.

12.14 References

AIC (1981), Proceedings AIC Color ’81, 21–25 September, 4th Congress of AIC Berlin.Allen E. (1966), J. Opt. Soc. Amer., 56(9), 1256.Benevue (2009), http://www.benevue.comBillmeyer F. W. Jr. and Saltzman M. (1966), Principles of Color Technology, New York,

John Wiley and Sons.Billmeyer F. W. Jr and Wyszecki G. (1978), Color ’77, Adam Hilger Ltd.CIE (1931) International Commission on Illumination (1931), Proceedings of the Eighth

Session, Cambridge, England.CIE (1976), CIE Recommendations Uniform Color Spaces, Color Difference Equations

and Metric Color Terms, Supplement No. 2 to CIE Publication No. 15, Bureau Central de la CIE, Paris.

CIE (1995), CIE Technical Report: Industrial Color Difference Evaluation, CIE Publication No. 116, Vienna, Austria, Central Bureau of the CIE.

Committee on Colorimetry, Optical Society of America (1953), The Science of Color, Crowell, New York.

Clark F. J. J., McDonald R. and Rigg B. (1984), ‘Modifi cation to JPC 79 Color Difference Formula’, Jour. Soc. Dyers Col., 11, 128–132 and 281–282.

Datacolor (2009), http://datacolor,comDavidson H. R. and Hemmendinger H. (1966), ‘Colour Prediction Using the Two Constant-

Turbid-Media Theory’, J. Opt. Soc. Amer., 56(8), 1102.Gangakhedkar N. S. (1974), Chromatic Notes, In-house Publication, Asian Paints (India)

Ltd., Mumbai, October 14.


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Gangakhedkar N. S. (1991), Understanding Computer Color Matching, Mumbai, Rutu Prakashan.

Gangakhedkar N. S. (1992), Two Constant Theory for Paints & Plastics, Mumbai, Rutu Prakashan.

Gangakhedkar N. S. (2003), Understanding Science and Technology of Color, Mumbai, Rutu Prakashan.

Ganz E. (1975), ‘Whiteness Measurement’, J. Color Res. & Appl. 4/5, 33.Hunter R. S. (1958 & 1960), ‘Description and Measurement of White Surfaces’,

J. Opt. Soc. Amer., 48, 597–605 and J. Opt. Soc. Amer., 50, 44.Hunter R. S. (1975), The Measurement of Appearance, New York, John Wiley and Sons,

Inc.Hunterlab (2009) http://www.hunterlab.comJudd D. B. and Wyszecki G. (1952), Colour in Business, Science and Industry, New York,

John Wiley and Sons, Inc.Judd D. B. and Wyszecki G. (1975), Color in Business, Science and Industry, 2nd edn,

New York, John Wiley and Sons, Inc.Kubelka P. and Munk F. (1931), ‘Ein Beitrage zur Optik der Farbanstriche’, Z. Techn.

Physik, 12, 593.Kuehni R. G. (1975), Computer Colorant Formulation, Lexington, Massachusetts,

Lexington Books, DC Heath and Company.Konikaminolta (2009), http://www.konicaminolta.comLuo M. R. (2001), ‘The CIE 2000 Color Difference Formula.’ Color and Imaging Institute,

University of Derby.Luo M. R., Cui G. and Rigg B. (2001), ‘The Development of the CIE 2000 Color Difference

Formula,’ Color Res. Appl. 26, 340–350.McDonald R. (1987), Color Physics For Industry, Society of Dyers and Colorists – Dyers

Company Publication Trust.Parker B. M. (1973), ‘Opacity, Hiding Power and Tinting Strength’ in Pigment Hand Book,

Volume III, 289–339, New York, John Wiley & Sons.Paton T. C., ed. (1973), Pigment Handbook, Volume III, New York, John Wiley & Sons.Rensselaer Polytechnic Institute (RPI) (1978), Advances In Color Technology, Topics,

Experiments, Reprints Bibliography, Rensselaer Polytechnic Institute, Troy, New York.Rensselaer Polytechnic Institute (RPI) (1978), Color Technology for Management, Reprint,

Bibliography, Department of Chemistry, Rensselaer Polytechnic Institute, Troy, New York.

Wyszecki G. and Stiles W. S. (1967), Color Science, Concepts and Methods, Quantitative Data and Formulas, New York, John Wiley and Sons, Inc.

X-Rite (2009), http://www.x-rite.com

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312

13Colour measurement of food: principles

and practice

D. B. MACDOUGALL, Formerly of the University of Reading, UK

Abstract: Foods have an infi nite variety of appearance characteristics. Their surfaces may be diffuse, glossy, irregular, porous or fl at. They may be transparent, hazy, translucent or opaque and their colours may be uniform, patchy or multilayered. The interactive role of pigment absorption with light scatter from food structure can have massive effects on colour and visual appearance. This is demonstrated in studies on coffee, orange juice and fresh meat. Colour was measured by CIELAB, a visually uniform colour space, and the relative effects of pigmentation to scatter determined by the Kubelka Munk analysis. The relationship of various food colours, in colour space, from their spectra is shown.

Key words: appearance, vision, colour, CIELAB, absorption, scatter, Kubelka Munk, food variety.

13.1 Introduction

We perceive the world in which we live by our fi ve senses, vision, hearing, touch, taste and smell, of which the sense of vision is usually the fi rst used in detecting events and objects around us in the visual world. The process of seeing comprises many co-operating activities, fi rst detected by our eyes and then interpreted in our brain, recognition of movement and location of object, relationships of objects to their surroundings, the intensity and quality of the light and the colour appearance of objects or events in the visual scene. From the time humans fi rst recorded events pictorially, e.g., cave paintings and then by printing, the incorporation of colour as a medium was an integral component of the procedures. This was especially so in the development of printing and in the world of art. Artists, from earliest times, attempted to portray the colour appearance of food in their pictures as realistically as possible with the limited number and type of pigments available. Most national art galleries contain examples of both classical and modern painting where food items are part of or are central to the painting. With modern photographic techniques and computer-controlled printing, accurate and attractive pictures of food items are now expected in illustrated magazine articles. Although representational portrayal of natural objects by paint and print can appear real and visually pleasing, it was only in the last two centuries or so that a scientifi c understanding of the processes involved in determining colour appearance has been elucidated.

Scientifi c studies into the mechanism of vision and human colour perception began in the seventeenth century with the recognition that the eye’s lens must

Colour measurement of food: principles and practice 313

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somehow project an image of the object viewed onto the back of the eye. Newton’s classic experiments on the refraction of light led him to conclude that the rainbow did not possess colour but it was the spectrum’s rays that produced the sensation (Wright 1967). The rationality of arranging colours into orderly systems, based on Newton’s seven rainbow colours, has resulted in the construction of colour atlases which attempt to arrange their colours in such a way that equal visual distances exist between adjacent colours. Two of the most used atlases are the earlier Munsell system and the newer Swedish natural colour space system. The former, developed in the USA, is based on fi ve hues and the latter, used mainly in Europe, is based on the six unique perceptions of black, white, red, green, yellow and blue (Hard and Sivik 1981).

The experiments of Maxwell, Young and Helmholtz in the nineteenth century in mixing coloured lights (MacAdam 1970) clearly demonstrated that people with normal colour vision must have at least three retinal pigments in their eyes, detecting in the short-, mid- and long-wave regions of the visible spectrum. By the late 1920s and early 1930s, the eye’s sensitivity to light relative to wavelength was established and the so-called ‘standard observer’ defi ned (Wright 1980). This led to the fi rst truly functional system for measuring colour as specifi ed by the Commission Internationale de l’Éclairage (CIE), the so-called CIE 1931 2° visual fi eld system of colour measurement (CIE 1986). Although colours could be defi ned unambiguously in this space, the space is not visually uniform. Since that time, many improvements have been incorporated into the system to make it nearly visually uniform and this research continues. With the development of the computer, complex colour measurements and calculations are now routinely used for such industrial processes as paint formulation (Best 1987), colour match prediction (Nobbs 1997) and control of the appearance of dyed textiles (McLaren 1986; McDonald 1997). Improvements in instrument specifi cation and design have led to a considerable increase in their use in industry. In the food industry, colour measuring instruments are now routinely used in research and for studies into product functionality, for product ingredient standardisation and process control.

13.2 Colour vision: trichromatic detection

Three interacting factors are required for the measurement of the colour appearance of any object in a scene. These are an understanding of the human visual process, the effect of light on objects in their environment and the nature of the materials observed. The sensation of colour is a psychophysical phenomenon, which is only part of the overall visual perception of the information detected by the eye and interpreted by the brain. The complex visual response can be thought of as the sum of the responses recognised in the brain from the signals detected by the eye of the scene viewed. The sensation, therefore, is perceived as if it were projected out into the world from which it originated. This leads to the error of imputing to


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the scene the sensations it generated. Sensations exist in the observer’s mind and not in the external world which produces them.

Human eyes have a near circular fi eld of view and are composed of three membranes (Fig. 13.1a). The outer membrane, the sclera, is continuous posteriorly with the sheath of the optic nerve and anteriorly with the cornea. The iris and the ciliary body, which suspends the lens, arise out of the middle layer, the choroid, which contains the capillary network. The light-detecting inner membrane, the retina, lines the inside of the posterior of the eye. The fi rst step in the visual process is the automatic control of the amount of light entering the eye through the iris. The fl ux is then focused by the lens on to the fovea in the central region of the retina where it is detected as colour. The signal is amplifi ed (Normann and Werblin 1974) and then transmitted through the visual pathway (Rodieck 1979) for interpretation in specifi c areas of the visual cortex of the brain (Hubel 1988; Zeki 1993). The retina has two types of light-detecting receptors, the cones and rods, so named because of the shape of their structures as viewed by the microscope. The ‘photoptic’ colour-detecting cones are sensitive to three wavelength ranges in people with normal colour vision and are densely packed in mosaic pattern in the centre of the fovea. This occupies <2° of the visual fi eld and is the basis of the CIE 1931 2° standard colorimetric observer. The >100 times more sensitive ‘scotoptic’ colourless detecting rods increase in density to 20° from the fovea and then decrease towards the periphery of vision (Fig. 13.1b). In 1964 a supplementary standard observer with a 10° fi eld of view was created to accommodate the changes that occur in colour perception as the visual angle increases beyond 2° where some rods are included in the detecting fi eld.

Light energy, focused on the retina, is converted into electrical signals by changes in the conformation of the photopigments in the multifolded disk-shaped structures in the outer segments of the rods and cones (Wald 1968; Hurvich 1981; Jacobs 1981; Stryer 1988). Although only the rod pigment rhodopsin has been characterised, determination of the spectral absorption of the cone pigments has been possible using retinal densitometry with colour-blind observers defi cient in one pigment (Smith and Pokorny 1975). The sensitivity curves of human cones determined by Estévez (1982) are presented in Fig. 13.1c. Rhodopsin absorbs maximally at 505 nm and the so-called blue (B), green (G) and red (R) cone pigments, that is the short-, mid- and long-wave sensors, are maximally sensitive at approximately 440, 540 and 570 nm respectively. The sensitivity range of B absorption marginally overlaps G and R between 450 and 550 nm, whereas the G and R absorption functions overlap substantially, displaced from each other by only 20 to 30 nm. Thus monochromatic light at 580 nm which is near maximum for R appears yellow and not red because of the combined contributions of G and R. Increasing the wavelength to > 600 nm increases the contribution of R relative to G and the perceived colour becomes redder. The fact that the R and G cones overlap gives rise to the enormous number of colours and colour differences that are experienced by people with normal colour vision.


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Optic nerve

Fovea

Sclera

Choroid

RetinaCiliary body

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–10 00

(b)

100 000

Cones

Rods

200 000

10 30

Angle from fovea (degrees)

Num

ber

(mm

–2)

50

13.1 Detection of light in the eye: (a) structure of the human eye (b) distribution of cones and rods in the temporal side of the retina (nasal side is similar except for the blind spot between 12° and 18° from the fovea).

Because cone vision is trichromatic, it means a suitable mixture of red, green and blue primary lights, as would be expected from a three-receptor system, can match any coloured light. The actual colour-matching functions depend on the wavelengths used as primaries. Although the initial detection of the stimulus is trichromatic, subsequent post-retinal processing gives rise to an achromatic lightness/darkness mechanism and coloured red/green and blue/yellow opponent mechanisms (Hurvich 1981; Hunt 2001). The lightness component consists of a


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weighted summation of all three cone pigment absorptions, whereas it is the degree of differences among the B, G, R absorptions that generates the opponent colour mechanism. However, the neural linkages among the pigment cone signals are not in simple one-to-one opposition. The simplest scheme that can be constructed is that the red/green opponent response is red activated by absorption of B plus R and green activated by G; yellow is activated by G plus R in opposition to blue activated by B.

13.3 The infl uence of ambient light and food structure

13.3.1 Adaptation and colour constancy

Adaptation is the process whereby the visual system conditions itself to the chromatic nature of the surroundings as affected by the quality, that is the wavelength distribution, and intensity of the illumination. It compensates for changes in the spectral power distribution of the light and serves to keep the eye in balance (Boynton 1979). The magnitude of this near automatic adjustment that chromatic adaptation has on visual experience is not usually recognised because of the limitations of human memory for individual colours and the phenomenon of colour constancy (Brill and West 1986). White objects appear to be white over a vast range of light conditions, e.g., from bright sunlight to the relatively dim levels of light found in room interiors, while colours appear to have similar colour appearance under most types of white or near white illumination. Studies into the phenomena

400(c)

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13.1 continued. (c) spectral sensitivities of blue (B), green (G) and red (R) cone pigments.


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of adaptation which elicit this near constancy of colour appearance have been concerned mostly with predicting the changes that occur to colour recognition when lamp type and output are altered (Bartleson 1979a). Lightness and contrast among greys are affected by luminance while colourfulness increases with an increase in the level of illumination and varies with the spectral emission of the lamp and its colour temperature (Hunt 1977). The phenomena of adaptation can be subdivided into three components, chromatic adaptation, light adaptation and colour constancy.

Chromatic adaptation occurs where changes in the visual system compensate for changes in the spectral quality of the illumination. Light adaptation occurs where the visual system attempts to compensate for changes in the level of illumination and colour constancy is experienced where the colour of an object tends to remain constant although the level and colour of the illumination are changed (Berns 2000). This is further discussed in section 3.9.1 on fresh meat, where the degree of red enhancement of lamp spectra is shown to affect the perception of product attractiveness. Models of cone adaptation response have been used to predict the consequences of changing lamp spectra on object appearance (Bartleson 1979b; Nayatani et al. 1986; Hunt 1987). The concept of apparent colourfulness has been used to construct grids of constant hue from which other grids can be derived for other illuminants (Pointer 1980; 1982). Such models use logarithmic and hyperbolic functions to mimic the physiological mechanisms involved. Hunt’s (1987) model can be used to predict the changes that occur in object colours at any level of illumination for a wide range of backgrounds in the realistic situation where the eye’s fi xation wanders. A considerable amount of research has been done into the subject of chromatic adaptation since the late 1980s. The most recently tested chromatic adaptation transform, CMCCAT2000, for predicting the change in colour appearance on changing illuminant has been shown to be simpler to use than previous models and has superior predictive accuracy (Li et al. 2002). These effects of light quality on colour perception illustrate the diffi culties in separating the concept of vision from that of appearance. The light from the scene modulates vision, whereas the characteristics of appearance are modifi ed by the light incident upon the object. Hence, procedures devised for observing and measuring colour must take account of the nature, quality and quantity of the light as it affects the observer’s perception. The British Standards Institute and the International Standards Organisation have recently produced general guidance and test methods for the assessment of the colour of foods (BSI 1999).

13.4 Appearance

Colour is usually considered the most important attribute of any food’s appearance (Francis and Clydesdale 1975) especially if it is associated with other aspects of food quality, for example, the ripening of fruit or the visible deterioration which occurs when a food spoils. Nearly every food product has an acceptable colour range, which depends on a wide range of factors including variability among consumers,


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their age and ethnic origin, and the physical nature of the surroundings at time of judgement (Francis 1999). However, in addition to colour specifi cation, the nature and extent of internally scattered light and the distribution of surface refl ectance are required for a more complete description of appearance. The food’s structure and pigmentation interact to affect both colour and translucency/opacity, for example, small changes in scatter may produce larger changes in colour appearance than are attributable to change in pigment concentration (MacDougall 1982).

The characterisation of an object’s appearance is accomplished in two stages. The fi rst is physical and the second is psychological. The physical characteristics are the size, shape and uniformity of the object along with the type, degree and variability of pigmentation throughout the object and the nature of the object’s structure that attenuates light. The physical information is converted to the psychological by translating the object’s refl ectance or transmittance spectrum into its tristimulus values and then to a defi ned colour space.

The concept of ‘total appearance’ (Hutchings 1999) can be applied to foods where it comprises more than just the food’s physical appearance characteristics and takes account of such social factors as the observer’s culture, memory, preferences and appreciation of the product. Foods have an infi nite variety of appearance characteristics. Their surfaces may be diffuse, glossy, irregular, porous, or fl at. They may be transparent, hazy, translucent or opaque and their colour may be uniform, patchy or multilayered. Hence, colour-measuring procedures for foods often have to be modifi ed from those used in the measurement of fl at opaque surfaces such as paint and paper, for which most colour-measuring instruments are designed. However this is not always recognised by those involved in food colour appearance measurement. Different instrument optical geometries will lead to diffi culties in sample presentation and, coupled with the uncertainties of sample structure, are likely to give different colour values for the same material if measured on different instruments. The inclusion or exclusion of surface specular refl ection in the measurement procedure depends not only on its importance as a characteristic of the food but also on the design of the detector/sensor unit in the instrument.

The spreading of the light transmitted within the food depends largely on its structure as well as the level of pigmentation. Hence, lateral transmittance of light through translucent materials will affect both their refl ectance and visual appearance (Atkins and Billmeyer 1966; Hunter and Harold 1988; MacDougall 1988; Hutchings 1999). The translucence effect must be allowed for in the assessment of such products as tomato paste (Brimelow 1987) because the ratio of absorption to scatter varies with aperture area and the concentration of components in the product (Best 1987; MacDougall 1987).

13.5 Absorption and scatter

The refl ection of light from opaque and translucent materials depends on the ratio of absorption to scatter as affected by pigmentation, refractive index and the


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light-scattering properties of the material. The Kubelka-Munk (KM) method for separating subsurface absorption and scatter (Kubelka 1948) is illustrated by Judd and Wyszecki (1975). Its use in the determination of pigment absorption in opaque materials is given in the latest edition of the book by Billmeyer and Saltzman (Berns 2000) and its use in opaque, translucent and layered materials is fully discussed by Nobbs (1997). The KM procedure relates refl ectivity R∞, i.e., refl ectance at infi nite thickness, to the coeffi cients of absorption K and scatter S by

K/S = (1 – R∞)2/2R∞

Hence K/S can be calculated directly from measurement of infi nite thickness but to calculate K and S separately it is necessary to measure the refl ectance of thin layers mounted on white and black backgrounds. If K and S are required for prediction purposes, the accuracy of their measurement can be improved by appropriate correction factors for surface refl ection (Saunderson 1942). Colour calculated from R∞, with separate estimation of the specular component as gloss is usually suffi cient information to describe opaque objects, but for translucent or layered materials K and S are also necessary.

13.6 Colour description: the CIE system

The CIE system of colour measurement (ASTM 2000; CIE 1986) transforms the refl ection or transmission spectrum of the object into three-dimensional colour space using the spectral power distribution of the illuminant and the colour-matching functions of the standard observers (CIE 1986). The mathematical procedures are given in any standard text on colour, for example Wright (1980), Judd and Wyszecki (1975), Hunt (2001) and Berns (2000). The system is based on the trichromatic principle but, instead of using ‘real’ red, green and blue primaries with their necessity for negative matching, it uses ‘imaginary’ positive primaries X, Y, and Z. Primary Y, known as luminous refl ectance or transmittance, contains the entire lightness stimulus. Every colour can be located uniquely in the 1931 CIE colour space by Y and its chromaticity coordinates x = X/(X + Y + Z) and y = Y/(X + Y + Z), provided the illuminant and the observer are defi ned.

The original illuminant representative of daylight was defi ned by the CIE as source C, but is now superseded by D65, i.e., an illuminant which includes an ultraviolet component and has a colour temperature of 6500°K. The colour temperatures of lamps and daylight range from approximately 3000°K for tungsten fi lament lamps and 4000°K for warm white fl uorescent to 5500°K for sunlight and 6500°K for average overcast daylight to approximately 20000°K for totally sunless blue sky. Because the original 2° colour-matching functions apply strictly only to small objects, i.e., equivalent to a 15 mm diameter circle viewed at a distance of 45 cm, the CIE has added a 10° observer (Fig. 13.2) where the object diameter is increased to 75 mm. Currently, the trend in colour measurement is to use D65 and the 10° observer except for very small objects. The


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1986 CIE recommended procedures for colorimetry are included in the ASTM Standards (2000) and also in Hunt (2001) along with the weighting factors for several practical illuminants (Rigg 1987). These include representative fl uorescent lamps, of which F2 is a typical lamp at 4230° K but with a low colour-rendering index (Ra) of 64 (Fig. 13.3). The colour-rendering index Ra is a measure of the effi ciency of a lamp at a given colour temperature to render the true appearance of Munsell colours. The broadband lamp F7 has the same colour temperature (6500°K) and chromaticity co-ordinates as D65 and, because of its fl atter spectrum, it has a high Ra of 90. The triband lamp F11 (4000° K) also has a moderately high Ra of 80, but its main advantage is its much improved effi ciency in energy utilisation.

13.7 Colour description: uniform colour space

The original 1931 CIE Y, x, y system of colour measurement is not visually uniform (Fig. 13.4a). Constant hue and chroma are distorted and equal visual distances increase several-fold from purple-red to green. Improved spacing has been accomplished by both linear and non-linear transformations of Y, x, y (Berns 2000). Near uniform colour spaces of practical importance are the Hunter and the CIELUV and CIELAB spaces. In the Hunter (1958) L, a, b colour space the lightness co-ordinate L is the square root of the tristimulus value Y, and a, and b are the red/green and yellow/blue opponent co-ordinates. The 1976 CIELUV and CIELAB spaces (Robertson 1977) attempted to reduce the many scales then

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x10 (λ)

13.2 Colour matching functions of the CIE 10° standard observer.


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13.3 Relative spectral power distributions of preferred CIE representative fl uorescent lamps.


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h*

C*

+b* (yellow)

+a* (red)

(blue) –b*

(green) –a*

0 = black(b)

L* = 100 = white

in use to two. The lightness co-ordinate L* is the same for both but the spaces use different concepts in their construction. The CIE L*, a*, b* space (Fig. 13.4b), known as CIELAB, has generally replaced the Hunter space for industrial applications although this has been somewhat slower in parts of the food industry where methods established on the Hunter system have economic reasons for its continued use. The improvements in CIELAB are due to the

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13.4 Colour diagrams: (a) CIE 1931 chromaticity diagram showing non-uniformity of spacing of red, yellow and blue unique hues; (b) CIELAB uniform diagram showing relationship of red/green (a*+/–) and yellow/blue (b*+/–) opponent co-ordinates to lightness L*, chroma C* and hue angle h*.


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nonlinear cube root transformation of the 1931 tristimulus values, which more approximate the visual spacing of the coloured samples in the Munsell system. The formulae are

L* = 116(Y/Yn)1/3 – 16 for Y/Yn > 0.008856

L* = 903.3 (Y/Yn)1/3 for Y/Yn < 0.008856

a* = 500[(X/Xn)1/3 – (Y/Yn)

1/3]

b* = 200[(X/Xn)1/3 – (Z/Zn)

1/3]

where Xn, Yn, Zn refer to the nominally white object colour stimulus.The co-ordinates of L*, a* and b* in CIELAB serve to defi ne the location of

any colour in the uniform colour space. However, in most industrial applications the object of measuring products is usually to determine how far they may be divergent from a set standard, both in colorimetric terms and in acceptability of visual match. The determination of uniform colour differences by CIELAB is not the same as the recognition of acceptability. CIELAB is based on the perception of just noticeable colour differences in the cylindrical co-ordinates of the system. However, acceptability differences are based on the perception of colour tolerance differences of real materials of industrial interest, e.g., textiles.

Colour terms can be divided into the subjective and the objective (Hunt 1978). The subjective, i.e., the psychosensorial, are brightness, lightness, hue, saturation, chroma and colourfulness. Colourfulness, a more recently introduced term, is that aspect of visual sensation according to which an area appears to exhibit more or less chromatic colour. Although hue is easily understood as that attribute described by colour names red, green, purple, etc., the difference between saturation and chroma is less easily comprehended. Saturation is colourfulness judged in proportion to its brightness, whereas chroma is colourfulness relative to the brightness of its surroundings. A similar difference exists between lightness and brightness. Lightness is relative brightness. Lightness is unaffected by illumination level because it is the proportion of the light refl ected, whereas the sensation of brightness increases with an increase in the level of illumination.

The objective, i.e., the psychophysical are related to the stimulus and are evaluated from spectral power distributions, the refl ectance or transmittance of the object and observer response. They provide the basis for the psychometric qualities which correspond more nearly to those perceived. For CIELAB space the terms are lightness L*, hue h* = tan–1 (b*/a*) and chroma C* = (a*2 + b*2)1/2. CIELAB total colour differences ΔE* can be expressed either as the co-ordinates of colour space or as the correlates of lightness, chroma and hue. Hence

ΔE* = [(ΔL*)2 + (Δa*)2 + (Δb*)2]1/2

or

ΔE* = [(ΔL*)2 + (ΔC*)2 + (ΔH*)2]1/2


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where ΔH* is used rather than Δh* because the latter is angular. For small colour differences away from the L* axis, if h* is expressed in degrees, then

ΔH* = C* Δh* (π/180)

The major colour scales with their associated terminology are given in Table 13.1.

Table 13.1 Overview of colour description systems and notation

CIE system (1931)This is based on the imaginary positive primaries X, Y, Z (transformed from real red, green and blue trichromatic primaries which may contain negative values). In CIE space, colour is located by (Y, x, y), where

Y luminous refl ectance or transmittance (containing the entire lightness stimulus)x, y chromaticity co-ordinates

X = x/(X + Y + Z )

y = Y/(X + Y + Z )

CIE space is not visually uniform.

Hunter Lab System (1958)In Lab space, colour space is more uniform than CIE and is defi ned by (L, a, b), where

L correlate of lightnessa, b red/green and yellow/blue opponent co-ordinate correlates

L = 10 Y1/2 a = [17.5(1.02Y – Y)]/Y 1/2

b = [7.0(Y – 0.847Z)]/Y 1/2

CIELAB system (1976)In CIELAB space, colour space is defi ned by (L*, a*, b*), where

L* visually uniform lightnessa*, b* visually uniform chromaticness co-ordinates

L* = 116(Y/Yn)1/3 – 16 for (Y/Yn)1/3 > 0.008856L* = 903.3(Y/Yn)1/3 for (Y/Yn)1/3 < 0.008856a* = 500[(X/Xn)1/3 – (Y/Yn)1/3]b* = 200[–(Y/Yn)1/3 – (Z/Zn)1/3]

Where Xn, Yn, Zn are the values of X, Y, Z for the reference white.

Further terms used are

h* = tan–1 (b*/a*) hueC* = (a*2 + b*2)1/2 chroma

Note: Recent colour difference formulae CMC(1;c) and CIE94 are derivations of CIELAB with weighting functions applied to make vectors of ΔL, ΔC and H more visually acceptable. The most recent and superior formula is CIE2000. Worked example of CIE2000 is given in Luo et al. (2001) The most recent chromatic adaptation formula for describing colour appearance under different viewing conditions is given in Li et al. (2002).


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It has been the task of the CIE for several years to create a single number pass/fail equation that would weight the three components that make up the total CIELAB colour difference ΔE*, that is, ΔL*, ΔC* and ΔH* the lightness, chroma and hue vector differences. Equations that are a distinct improvement in this regard have been devised by the CIELAB on a very large body of experimental evidence (Berns 2000). The Colour Measuring Committee (CMC) of the Society of Dyers and Colourists formulated a signifi cant improvement in uniform colour difference formulae from the earlier JPC79 (J and P Coates) colour difference formula. In this CMC(1:c) formula, adjusting constants are incorporated by the user to weight the importance of lightness and chroma relative to hue (BSI 1988). Subsequently, on the basis of the work of Luo and Rigg (1986), Alman et al. (1989) and Berns et al. (1991), the CIE recommended the use of a new colour difference equation for use in industry, known as CIE94 where total colour difference is designated as ΔE94. It includes a term for the visually perceived magnitude of the colour difference. For those industries that require accurate colour difference measurement that is related to perception and acceptability, e.g., the textile industry E94 is used preferentially. A further improvement has now been recommended by the CIE on the basis of work described by Luo and his associates and is designated as the CIEDE2000 Colour-Difference Formula (Luo et al. 2001). It has now been offi cially adopted by the CIE (CIE 2001) and a worked example of its use is given in the Luo et al. (2001) publication. The food industry’s demand for such a level of precision of colour difference as recommended by CIEDE2000 remains to be assessed.

13.8 Instrumentation

Since colour is a psychological phenomenon, its measurement must be based on human colour perception. Hence, photoelectric instruments are corrected for both lighting and human visual response, while visual techniques must use observers with ‘normal’ colour vision under defi ned lighting. Examples of direct visual assessment are colour atlases for broad defi nition of the location of colours in colour space, collections or sets of printed or painted coloured papers specifi c to products or processes and visual matching instruments which use coloured fi lters. Typical of the former are the Munsell and Swedish NCS atlases which are structured on uniform colour space, and the Pantone collections of printer’s colours with defi ned ink mixtures printed from 10 to 100 per cent tinting strength. Probably the best known of the visual matching instruments is the Lovibond Tintometer in which the object, under specifi ed illumination, is viewed and matched against a series of coloured fi lters interposed over a white background by the observer.

Photoelectric colour measuring instruments can be divided into two classes, trichromatic colorimeters and spectrophotometers. The most successful of the early trichromatic colorimeters was developed in the 1940s by Hunter (1958). It comprised a light source and three wideband red, green and blue fi lters to


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approximate CIE standard illuminant C and the 2° observer. The tristimulus values obtained were transformed into Hunter L, a, b colour space. Until the advent of the computer and the photodiode such instruments were much less expensive than spectrophotometers and, although absolute accuracy may have been poor, they were extremely good at measuring the small colour differences demanded for industrial process control (Patterson 1987). The more modern tristimulus instruments are linked to computers with automatic calibration and the provision of a number of colour spaces. Such instruments may be supplied with a selection of sensor heads of different illuminating geometries to allow measurement of a wide range of product types depending on the nature and dimensions of their surfaces. Several companies now manufacture a range of hand-held lightweight colorimeters and miniature diode array spectrophotometers, with optical geometries comparable in function with the larger bench instruments. Their compactness is a direct result of the use of high-energy pulsed xenon arc lamps combined with fi ltered silicon detectors and microchip circuitry. Because such instruments, with their built-in memories and choice of colour scales, are comparatively inexpensive it has resulted in an increase in their use for in-line colour measurement in all branches of industry where colour control is necessary or desirable, e.g., in the printing and automotive industries.

The most accurate instrument for measuring colour is the spectrophotometer. Refl ectance instruments are usually fi tted with an integrating sphere with the choice of including or excluding the specular component of refl ectance. Care must be exercised in deciding which geometry is appropriate for particular applications. The diffuse component of refl ectance from subsurface absorption and scatter is wavelength dependent, whereas the specular component is not. For materials with glossy surfaces the inclusion of the specular will increase measured refl ectance which, when translated into colour space, can lead to large discrepancies in the interpretation of visual lightness, as usually viewed, and to a lesser extent of the chromaticness of the colour. For example, highly glossy black tiles used for instrument calibration have tristimulus Y values of approximately 0.3 when the specular is excluded but 4.5 when included. The consequence of this difference in Y of 4 per cent produces a specular excluded uniform lightness L* of 3 and an included L* of > 25. For medium grey and white tiles the excluded to included Y values are approximately 25 to 29 and 78 to 82 respectively, which give L* values of approximately 57 to 61 and 91 to 92 respectively. Hence the near constant effect of 4 per cent in Y from the specular refl ectance produces a decreasing effect from black to white from > 20 to about 1 per cent in L*. The CIE recommends that colorimetric specifi cations of opaque materials should be obtained with one of the following conditions of illumination and viewing geometries which should be specifi ed in any report:

• 45°/0° or 0°/45°, specular excluded• diffuse/0° or 0°/diffuse, specular included or excluded.


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However, spectrophotometers most commonly used for measuring colour do not have identical geometries. Three typical instruments were compared by Patterson (1987), who points out that probably the biggest source of differences among the instruments can be traced to the specular component. Hunt (1987) suggests that if measurements are to be compared it is better to include the specular because of the considerable variation in the area of gloss traps used in different spheres. However, the more nearly correct measurements in relation to practical visual observation are with the specular excluded (Best 1987). For computer match prediction of pigmented materials, e.g., paint formulation, the total refl ection (i.e., specular included) is preferred. This restriction does not usually apply to tristimulus colorimeters which normally exclude the specular component of refl ectance where the illumination viewing geometry is 45°/0°, as is the case in the classic Hunter bench colorimeter.

Another important source of variation among tristimulus colorimeters and spectrophotometers is the area of the viewing aperture relative to the area of the illuminating light spot, which affects both the direction and the amount of light returned from translucent materials. MacDougall (1987) demonstrated that translucent suspensions of milk exhibit a tenfold decrease in K/S for an increase in aperture area from 5 to 20mm. Best (1987) states that accurate determination of K and S by measuring thin layers on black and white backgrounds requires that the ratio of the aperture area to the thickness of the sample must be considerably greater than 10, a criterion unlikely to be met for many foods. One further source of potential error, in addition to those associated with instrument geometry and sample structure, is the wavelength interval used to calculate the tristimulus values. Although the CIE (1986) specifi es the standard observer at 5 nm intervals from 380 to 780 nm, such accuracy is not required for most practical purposes. For 10 nm accuracy the intermediate 10 nm values from the 5 nm tables should be used. However, the CIE has not yet offi cially recommended the use of 20 nm intervals, although many modern colour spectrophotometers detect at 20 nm intervals. Tables of weighting functions at 20 nm intervals for the CIE illuminants and several fl uorescent lights are published in the up-to-date colour textbooks cited in this chapter. Errors attributable to wavelength interval are likely to be less important than those from instrument geometry, except when estimating the effects of narrow-band emission lamps on materials with several absorption bands. Here the 20 nm interval may prove to be less effi cient.

13.9 Food colour appearance measurement in practice

Colour fading from pigment oxidation in fresh meat, the effect of illumination on the appearance of orange juice, the effects of varying coffee and milk concentration on coffee appearance and measuring breakfast cereals by grinding to a defi ned particle size are given as examples of the types of problems encountered in food colour appearance measurement. The effect of the illuminant


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on the calculated CIELAB colour values for a variety of food spectra is also presented.

13.9.1 Fresh meat

The surface of freshly cut meat oxygenates to bright red on exposure to air from the purple ferrous haem pigment myoglobin to the covalent complex oxymyoglobin. The red oxymyoglobin then oxidises to brownish green metmyoglobin (MacDougall 1982; MacDougall and Powell 1997) during refrigerated display and is affected by both the intensity of illumination and the temperature. Twenty per cent dilution of the surface oxymyoglobin with metmyoglobin causes the product to be rejected at retail because of its faded colour (Hood and Riordan 1973). The changes in the mean refl ectance spectra of over 100 packages of beef overwrapped with oxygen permeable fi lm and held in the light at < 5°C over a period of one week are shown in Fig. 13.5. As the pigment oxidises there is an increase in refl ectance in the green region of the spectrum as the alpha and beta absorption bands decrease. This is accompanied with a distinct loss in refl ectance in the red region with development of the metmyoglobin absorption band at

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13.5 Refl ectance spectra of fresh beef during oxidation of oxymyoglobin to metmyoglobin obtained on a diode array spectrophotometer at 20 nm intervals: means of over 100 samples wrapped in oxygen-permeable fi lm and stored at <5°C under 1000 lux fl uorescent illumination for one week.


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630 nm. The changes in colour, calculated for CIELAB for D65, are shown in Fig. 13.6. As meat fades there is a small loss in lightness L*, accompanied by much greater changes in a* and b*. The loss in a* and gain in b* can be interpreted as an increase in the hue angle h* in the direction of yellow with a concomitant loss in chroma C*, which is indicative of the colour becoming more grey or dull. The change in direction towards yellow with the reduction in C* is perceived as being more brown.

The appearance of meat is greatly affected by the colour-rendering properties of the lamps used for display (Halstead 1978). Some fl uorescent lamps recommended by the lamp industry for displaying meat have enhanced red emission which tends to maintain the preferred colour of oxymyoglobin and visually shifts the early stages of metmyoglobin development from brown towards red. This effect of red enhancement on meat colours has been shown to elicit a greater visual colour change in making brown appear red than in making red

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35

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41

42L*

C* C*

h*

h*

43

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3 4 5

13.6 Progressive changes in lightness L*, hue angle h* and chroma C* calculated from spectra of wrapped fresh beef stored at <5°C under 1000 lux fl uorescent illumination during oxidation of surface oxymyoglobin to metmyoglobin.


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appear more red (MacDougall and Moncrieff 1988). Some fi nd the fl attering of red-enhanced lamps makes meat appear too red.

The ICS Micro Match spectrophotometer used to measure these samples is equipped with the option of using alternative illuminants to calculate CIELAB. The estimated changes in meat colour attributable to different illuminants after one and four days’ exposure (Table 13.2) illustrate the effect that light quality has on lightness, hue and chroma. The changes in colour produced by the differences in colour rendering among some of the lamps are equivalent to that which occurs after four days’ fading, that is ΔL* ≤ 1, ΔC* ≤ –4 and Δh* ≤ 7. There was little change in L* on changing illuminant, but the large changes in C* and h* illustrate the effects of decreasing lamp colour temperature, altering fl attery and improving fi delity. A decrease in colour temperature from D65, as red emission increases, generally increases C*, that is the colour is perceived as brighter or more intense with the observer adapted to white. However h* may become more brown (more positive) or more red (more negative) as infl uenced by both colour temperature and the lamp’s spectral bandwidth which affects fi delity. This illustrates the potential for mistaken misinterpretation of the data when measuring by the normal procedure of D65 and 10° when, in reality, judgement at retail or in the home will be under warmer illumination.

13.9.2 Orange juice

Translucent suspensions are diffi cult to measure, and direct unobserved interpretation of instrumental data can lead to confusion because of the way the incident light is dispersed in the sample. Most consistent results are obtained if the instrument aperture is large relative to the incident beam (Kent 1987; MacDougall 1987). The effects of optical geometry on colour and the Kubelka-Munk absorption K (mm–1) and scatter S (mm–1) coeffi cients for orange juice were studied by

Table 13.2 Calculated changes in L*; C* and h* from D65 to other lamps for the fresh beef spectra shown in Figure 13.5

Storage Difference in colour from D65time ΔA ΔWWF ΔNFL ΔCWF Δ83 Δ84

2 hours ΔL* 2.9 2.0 0.6 2.0 2.2 1.2 ΔC* 7.3 7.1 2.2 7.1 7.6 4.9 Δh* −2.3 −1.2 3.9 6.5 3.3 −2.34 days ΔL* 2.3 1.6 0.6 −0.2 1.7 0.9 ΔC* 5.7 5.5 2.1 −0.7 6.3 3.8 Δh* −5.9 −1.9 4.5 9.1 2.8 −3.5

Tungsten lamp: A, fl uorescent lamps: WWF, warm white; NFL, natural; CWF, cool white; 83, triband at 3000° K; 84, triband at 4000° K.


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MacDougall (1983) who found Y∞, the luminous refl ectance at calculated infi nite thickness, increased by 50 per cent if the aperture diameter was increased from 2 cm to 5 cm while the incident beam was maintained at 1 cm.

The effect of dilution of fourfold orange juice concentrate on the refl ectance spectra obtained on 4 cm thick samples in thin walled polystyrene bottles is shown in Fig. 13.7. As can be seen, 4 cm is practically equivalent to infi nite thickness. Kubelka-Munk absorption and scatter coeffi cients were calculated from 2 mm thick samples on black and white backgrounds. On dilution, S decreased for X, Y and Z as the suspension became more translucent. K for Z decreased to approach the much lower near constant values of K for X and Y (Fig. 13.8). This decrease in K for Z is as anticipated for a blue absorbing pigment. The effect of loss of scattering power on dilution was to reduce Y∞, and hence lower L*. The most dilute juice, therefore, is instrumentally the darkest, and the most concentrated is the lightest (Fig. 13.9). However, this is not what is perceived. Glasses of orange juice viewed with overhead illumination range from pale yellow for concentrations less than 1 to deep orange at a concentration of 4, which is opposite to that determined instrumentally. For strongly scattering coloured materials in dilute suspension, measured colour, even supplemented by information on scatter, is inadequate to fully describe appearance. The instrument does not measure what the observer sees because light is refl ected from a limited solid angle, whereas the observer’s perception is infl uenced by the multidirectionality of illumination, which makes coloured translucent materials appear to glow.

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13.7 Refl ectance spectra of concentration and diluted orange juice at a path length of 4 cm, equal to infi nite thickness: reconstituted juice at normal concentration = 1:0.


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13.9.3 Coffee

Coffee in the cup is usually poured black for the drinker to adjust its colour by adding milk or cream. The drinker uses the combination of milk and coffee as an indication of its anticipated taste although he or she may not know the actual concentration of coffee in the drink. The most obvious appearance attribute of milk in coffee is the intensity of its colour, but translucency has also been shown

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13.8 Kubelka-Munk absorption K and scatter S coeffi cients for tristimulus values X, Y and Z for concentrated and diluted orange juice: values (mm−1) calculated from refl ectance spectra obtained from 2 mm path length cells with black and white backgrounds.


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to affect visual estimation of coffee strength (Hutchings 1999; Mackinney and Little 1962). The Kubelka and Munk theory of mixing pigments in a light-scattering suspension (Judd and Wyszecki 1975; Kubelka 1948) is used as the basis for colour formulation in the paint, plastics and textile industries (Nobbs 1997; McDonald 1997). By manipulating the relationship of the concentration of pigment (K) to degree of light scatter (S) it was demonstrated that it is possible to produce approximately equivalent levels of visual lightness in up to fi vefold dilutions of brightly coloured milk (MacDougall 1988). For coffee, since the colour is dull brown, then neither the lightness, chroma nor hue, from different

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13.9 Changes in lightness L*, opponent co-ordinates a* and b*, hue angle h* and chroma C* calculated from refl ectance spectra of concentrated and diluted orange juice at 4 cm path length.


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concentrations of the components would be expected to change markedly for the drink viewed in the cup, provided the K/S ratio remains constant and the drink does not become obviously translucent.

Hutchings (1999) previously had observed that the appearance of creaminess and coffee concentration was related to the K/S ratio but his studies did not include assessment of the actual drinking quality of the beverage. MacDougall and Lima (2001) recently studied the relation of the appearance of coffee at a range of constant K/S values to apparent coffee concentration and milk concentration coupled with a sensory panel’s detection of the strength of the components in the drink. The three ‘K/S constant ratios’ of coffee to milk were made from three levels of Nescafé Gold Blend instant coffee and pasteurised semi-skimmed milk, i.e., nine mixtures in all. Unit quantity of coffee was defi ned as 2.5 g/l and unit quantity of milk as 100 ml/l. The constant ratios were constructed by doubling the quantity of both coffee and milk at each level and the differences between the ratios by doubling the concentration of coffee from the previous ratio. This required three levels of milk, 100, 200, and 400 ml/l, designated 1, 2 and 3, and fi ve levels of coffee, 2.5, 5, 10, 20 and 40 g/l, designated A, B, C, D and E. Not all combinations were required for the three constant ratios of (A1, B2, C3), (B1, C2, D3) and (C1, D2, E3), i.e., coffee to milk ratios of 1:1, 2:1 and 4:1. It is to be noted that 2.5 g/l of freeze-dried coffee is equal to one heaped teaspoonful, a quantity typically used in making a cup of coffee.

Samples of the cold coffee mixtures were presented in 1.5 cm deep tissue culture bottles, which have a 5 cm2 clear face, illuminated by Artifi cial Daylight Fluorescent Lamps (D65) at 900 lux. Twenty assessors, with normal colour vision, spaced the nine bottles on three 150 cm unmarked straight edges on a pale grey bench for degree of lightness to darkness, for coffee strength and of milk strength relative to the most extreme samples located at the respective ends of the scales. The mean spacing of the samples is given in Fig. 13.10 where they were judged to cluster according to the three constant K/S ratios even though the concentration of milk and coffee in the most concentrated sample within each ratio was fourfold higher than the lowest. The differences within the constant ratios were much less than between the ratios showing that the effect of adding milk produced considerable confusion in the observers’ ability to identify the concentrations of the components. That is, up to a fourfold increase in pigment absorption is apparently nullifi ed by a similar increase in light scatter. When the sensory panel observed and tasted the same samples, this time prepared hot, they similarly separated their appearance when presented in white cups. The taste attributes of coffee and milk were more complexly related to their appearance. Quantitative descriptive analysis of the panel data separated the attributes primarily on the constant K/S ratios but interactively on the absolute values of their components. This demonstrates that the visual appearance of coffee and milk is not a reliable indicator of the taste.


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13.9.4 Breakfast cereals

Discontinuity in breakfast cereals may be defi ned as that condition where the components of the product and the inter-component spaces are arranged randomly, which does not lend itself to easy and reproducible measurement. The problem would appear to be intractable unless samples are manipulated to replace the discontinuity with a uniform distribution of the particles. One approach is to grind the sample to constant particle size (Kent 1987). This author studied the colour of four UK breakfast cereals (Shredded Wheat, Weetabix biscuits, Kellogg’s Corn Flakes and Bran Flakes) ground to >2 mm, 1–2 mm, 0.5–1 mm and <0.5 mm through BSI standard sieves. The increased surface area as the particles reduced in volume scattered more light but removed randomness. The 1–2 mm and 0.5–1 mm fractions were closest in appearance to the original fl akes, and to each other, whereas the <0.5 mm fraction in each case was considerably lighter and more yellow. In addition to measuring the cereals’ colours, reference colours were used to visually defi ne the degree of discrepancy between measurement and visual observation of the material (MacDougall 2001). This was accomplished with NCS colour patches under both Artifi cial Daylight (D65) and tungsten illumination. The greatest differences in L*, C* and h* between 1–2 mm samples for all cereals and

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13.10 Relationship of visual lightness to darkness of coffee and milk K/S mixtures to the perception of the strength of the coffee and milk.


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their nearest colour patch were in L* which ranged from 7 to 21 units and always in the same direction of the NCS colours being the lighter. Differences in C* were <3 units and h* were <7 degrees except for bran fl akes which was 15 degrees because of diffi culty in deciding which atlas page most resembled the product. The greatest contribution to ΔE* therefore, was ΔL*, with chroma and hue virtually unchanged. Typical differences in L* between adjacent colours in the atlas are approximately 10 units. This demonstrates that the magnitude of visual discrepancy in colour assessment, i.e., the error, of products prepared in this fashion will be greater than differences between adjacent NCS patches.

13.10 Illuminant spectra and uniform colour

Although D65 is the reference illuminant spectrum most used for calculating CIELAB, of the other lamps listed in Table 13.2 the more important for relating food colour measurement to visual colour in practice are A and 83. A is the emission spectrum of tungsten illumination and 83 is that of ‘tri-band’ fl uorescent illumination, similar to F11 in Fig. 13.3. However in 83 the red phosphor is increased by >25 per cent over that of F11 and the green phosphor reduced by about 12 per cent giving the lamp a warm appearance (3000°K) similar to that of tungsten but with a considerable reduction in energy utilisation. 83 is now commonly used for food display in supermarkets because of its attractiveness, pleasant and realistic colour rendering and low heat output. To examine the size of the effect of these lamps on CIELAB colour space, a variety of foods, selected as representative of the major hues, were measured on the ICS Micro Match spectrophotometer. The red hue examples were fresh tomatoes at supermarket readiness, i.e., not excessively ripe but bright attractive red-pink and Wiltshire bacon, typically translucent in appearance. The yellow spectrum was the mean of several bananas, ripe but without any indication of brown spotting. The difference between the two orange samples was that the colour of the peel of entire navel oranges is near opaque and brilliant, whereas the colour of freshly squeezed orange juice is translucent. This results in a large reduction in the juice’s refl ectance at the red end of the spectrum compared to that of the peel. The green spectrum was that of the external leaves of a mid- to light-green cabbage and the brown was that of the surface of a ‘digestive’ biscuit. The near-white spectrum was that of semi-skimmed milk. All samples were measured at infi nite thickness.

Figure 13.11 shows the average refl ectance spectra of the foods and Fig. 13.12 the location of the samples in CIELAB calculated for the three illuminants. The triplets of points for each sample show that D65 has lower b* values, that is more blue as would be expected, and A and 83 are more yellow with A more red than 83 with higher a* values. These results parallel visual observation of the products viewed under tungsten, artifi cial daylight (D65) and Philips 83 lamps. This study clearly demonstrates that if interpretation of CIELAB is to be related to illumination conditions in practice, then D65 should not be the only illuminant used to calculate the colour of the food.


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13.11 Refl ectance (%) spectra of a selection of foods of differing hue.

13.11 Conclusions and future trends

This chapter has attempted to present the basics of colour measurement as applied to foods. It is important to realise that the wide variability in the nature of foods and food products, from both their structure and pigmentation, may limit any


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colour measurement technique applicable only to that particular food. This is because the numeracy of the data is unlikely to match the visual experience of equivalent reference atlas colours. In some cases this discrepancy may be large and has to be recognised as an intrinsic property of the food. These differences may arise from the difference of the visual experience of the product when viewed under normal lighting conditions as opposed to the limitations of its optical properties when presented to the particular colour-measuring instrument. This is particularly so in the case of translucent foods. The values of measured lightness, hue and chroma are likely to be quite different from similarly coloured opaque materials. A consequence of this is that, if required for quality decisions, the variability of measured colour for any food over the range of acceptability and unacceptability needs to be established relative to real samples judged under appropriate controlled visual judgement conditions. In the light of recent studies on the increased accuracy of colour difference and perceptual acceptability formulae the industry may have to reassess its procedures. Whether or not the level of accuracy demanded by other colour industries is required by the food sector remains to be proved. If the foregoing are taken account of the use of

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13.12 CIELAB a*b* spacing of the food spectra from Fig. 13.11 calculated for the illuminants D65 (■), A (●) and 83 (▲). The L* values for each food are given adjacent to the symbols of the illuminants.


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colorimetry is likely to expand in the food industry in the future, especially so as portable instruments and in-line measurement techniques become accepted as reliable indicators of product quality. Care needs to be exercised in using such instruments because their detector geometries are different from that of larger reference spectrophotometers.

On-line use of colour measurement is likely to increase especially where the colour values obtained by the instrument can be incorporated automatically into process control. The use of video image analysis (VIA) to couple colour measurements with variation in appearance is likely to increase. Provided the RGB signals from VIA systems and digital camera output can be translated into meaningful colour data then a wider and more adaptable approach to food colour appearance control might be possible. VIA systems could become a more direct way of relating measurement to visual assessment and effectual quality assurance. Such systems in conjunction with standard colour measurement could possibly provide direct quality links by computer from the supplier to the processor to the supermarket

13.12 References

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Bartleson, C. J. (1979a) Changes in color appearance with variations in chromatic adaptation. Color Research and Application, 4, 119–138.

Bartleson, C. J. (1979b) Predicting corresponding colors with changes in adaptation. Color Research and Application, 4, 143–155.

Berns, R. S. (2000) Billmeyer and Saltzman’s Principles of Colour Technology, 3rd edn. Wiley, New York.

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CIE (1986) Colorimetry, 2nd edn. CIE publication 15.2. Commission Internationale de l’Éclairage, Vienna.

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Adam Hilger, London.Hard, A. and Sivik, L. (1981) NCS Natural Color System: A Swedish standard for color

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New York.Hurvich, L. M. (1981) Color Vision. Sinaver, Sunderland, MA.Hutchings, J. B. (1999) Food Colour and Appearance, 2nd edn. Kluwer Academic/Plenum

Publishers, Dordrecht.Jacobs, G. H. (1981) Comparative Color Vision. Academic, New York.Judd, D. B. and Wyszecki, G. (1975) Color in Business, Science and Industry, 3rd edn.

Wiley, New York.Kent, M. (1987) Collaborative measurements on the colour of light scattering foods. In

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Luo, M. R., Cui, G. and Rigg, B. (2001) the development of the CIE 2000 colour-difference formulae: CIEDE2000. Color Research and Application, 26, 340–350.

MacAdam, D. L. (1970) Sources of Color Science. MIT, Cambridge, MA.MacDougall, D. B. (1982) Changes in colour and opacity of meat. Food Chemistry, 9,

75–88.


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MacDougall, D. B. (1987) Optical measurements and visual assessment of translucent foods. In Physical Properties of Foods, vol. 2, eds R. Jowitt, F. Escher, M. Kent, B. McKenna and M. Roques, 319–330, Elsevier, London.

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MacDougall, D. B. and Moncrieff, C. B. (1988) Infl uence of fl attering and triband illumination on preferred redness-pinkness of bacon. In Food Acceptability, ed. D. M. H. Thomson, 443–458, Elsevier, London.

MacDougall, D. B. and Powell, V. H. (1997) Relative importance of temperature, wavelength and intensity of light on the colour display of fresh and aged beef cuts. Proceedings of the 43rd International Conference of Meat Science and Technology, New Zealand, 668–669.

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2nd edn, ed. R. McDonald, 209–291, Society of Dyers and Colourists, Bradford.McLaren, K. (1986) The Colour Science of Dyes and Pigments, 2nd edn. Adam Hilger, Bristol.Nayatani, Y., Takahama, K. and Sobagaki, H. (1986) Prediction of color appearance under

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343

14Colorimetric evaluation of tooth colour

A. JOINER, Unilever Oral Care, UK

Abstract: This chapter begins with an introduction to the structure of the dentition and its environment followed by a review of the optical properties of teeth. Tooth colour will be reviewed with particular emphasis on colour distribution and variation. Important factors that infl uence tooth colour and its perception will be discussed. With the current interest in tooth whitening, this chapter will then describe approaches to quantifying tooth whiteness and the measurement of tooth colour, in particular, visual assessment approaches and instrumental techniques. In addition, methods to improve tooth colour will be reviewed including whitening toothpastes, tooth bleaching and microabrasion.

Key words: tooth colour; tooth colour measurement; tooth colour perception; tooth whitening; tooth bleaching.

14.1 Introduction

Overall appearance is a key element in social interaction and success (Baldwin, 1980), including the appearance of the teeth (Kershaw et al., 2008). The ultimate aim of aesthetics in dentistry is to create a beautiful smile, with teeth of pleasing inherent proportions to one another, and pleasing tooth arrangement in harmony with the gingival, lips and face of the patient (Mayekar, 2001). One of the most important considerations in dental aesthetics is the optical properties of the teeth, which includes their colour and translucency, for example.

This chapter will begin with a basic introduction to the structure of the dentition and its environment followed by a review of the optical properties of teeth. Tooth colour will be reviewed with particular emphasis on colour distribution and variation. Factors that infl uence tooth colour will be reviewed, including tooth discolourations found within the teeth and on their surfaces and other important factors such as subject age and hydration state together with lighting conditions, observer and context which can impact the perception of tooth colour. In recent years tooth whitening has become one of the most rapidly growing oral care sectors, since patients now demand not only healthy smiles but also cosmetically attractive and whiter smiles. This chapter will therefore describe approaches to quantifying tooth whiteness and the measurement of tooth colour, in particular, visual assessment approaches and instrumental techniques. In addition, methods to improve tooth colour will be reviewed including whitening toothpastes, tooth bleaching and microabrasion. Finally, likely future trends in the tooth colour fi eld will be outlined together with sources of further useful information.


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14.2 The human dentition and its environment

14.2.1 Teeth and gums

The anatomy of a tooth essentially consists of enamel, dentine and the pulp (Ten Cate, 1998) (Fig. 14.1). Tooth enamel is highly mineralised and consists of about 95% by weight mineral (hydroxyapatite, Ca10(PO4)6(OH)2 and some carbonate), 4% water and less than 1% organic material, mainly protein. It can be up to 2.5 mm thick over the working surfaces (cusps) of the tooth, tapering to only a few hundred microns thick at the cervical margins. At an ultrastructural level, enamel is composed of long rod-shaped prisms as the fundamental unit. Each prism extends from its site of origin at the enamel–dentine junction to the outer enamel surface. These have an average width of 5 microns in diameter and are made up of highly organised crystallites of hydroxyapatite (Ten Cate, 1998; Addy et al., 2000). The bulk of the root and body of the tooth is composed mainly of dentine, which has been described as a porous biological composite material composed of hydroxyapatite crystal fi ller particles in a collagen matrix (Pashley, 1996). Dentine contains many micron-sized diameter dentinal tubules surrounded by highly mineralised (ca. 95% volume mineral phase) intertubular dentine (Addy et al., 2000). The central pulp chamber is fi lled with soft connective tissue, nerves and blood vessels (Ten Cate, 1998). The soft tissues that support the teeth are collectively termed the periodontium. The most superfi cial periodontal tissue is the gingiva, commonly known as the gums, and this is the tissue that surrounds

Pulp

Enamel

Dentine

Gum

Bone

14.1 The anatomy of a tooth.

Colorimetric evaluation of tooth colour 345

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the root of an erupted tooth. It is involved in the attachment of the tooth forming a junction between the gum and tooth (Ten Cate, 1998).

14.2.2 Saliva and pellicle

The hard and soft oral tissues are constantly bathed in saliva which is secreted by the various major and minor salivary glands found in the mouth. Saliva is a complex mixture of many types of proteins, glycoproteins, enzymes, lipids and several ions including sodium, potassium, calcium, phosphate and bicarbonate. It can also contain other components derived from cellular and bacterial origin. Saliva is a very important fl uid since it starts the initial process of food digestion, provides lubrication which makes mastication and swallowing easier, and aids in the maintenance of oral and overall general health (Jenkins, 1978).

When a tooth surface has been thoroughly cleaned, within minutes the enamel surface becomes coated by a sub-micron, colourless organic fi lm known as the pellicle. The pellicle is formed by the adsorption of high affi nity salivary proteins followed by further adsorption of other salivary proteins. The pellicle layer is of great physiological importance since it participates in all the tooth–saliva interfacial events taking place in the oral cavity, such as, de- and remineralisation, lubrication of the tooth surface and modulation of bacterial attachment (Hannig and Joiner, 2006). It is also involved in the formation of extrinsic stain which can impact on the aesthetics and colour of the teeth (see section 14.5.2).

14.3 Optical properties of teeth

A combination of the optical properties of a tooth will determine its colour and appearance. Four phenomena associated with the interactions of light with the tooth have been described (Ragain and Johnston, 2001): (1) transmission of the light through the tooth, (2) specular refl ection at the surface, (3) diffuse light refl ection at the surface, and (4) absorption and scattering of light within the dental tissues. Tooth colour has been shown to result from the volume scattering of light, i.e. illuminating light follows highly irregular light paths through the tooth before it emerges at the surface of incidence and reaches the eye of the observer (O’Brien et al., 1990; Van der Burgt et al., 1990). Both enamel prisms and dentinal tubules can act as optical fi bres (Odor et al., 1996). The hydroxyapatite crystallites contribute signifi cantly to light scattering in enamel (Vaarkamp et al., 1995) whereas within dentine the tubules are the predominant cause of scattering (Ten Bosch and Zijp, 1987; Zijp and Ten Bosch, 1993). Exposure of enamel to acid can give partial demineralisation of the enamel crystallites which will cause an increase in light scattering and thus the enamel can appear visibly whiter (Ko et al., 2000).

The translucency of teeth is an important factor for consideration in restorative dentistry since it contributes signifi cantly to the total aesthetics of the tooth


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and is an indication of the quality and quantity of light refl ection giving vitality to the tooth (Terry et al., 2002; Chu et al., 2004). The cervical regions of the teeth have the lowest translucency and the incisal edges the highest translucency (Hasegawa et al., 2000b). The translucency of enamel is an important factor when considering tooth colour, since in vitro studies (Ten Bosch and Coops, 1995) have shown that tooth colour is determined mainly by the colour of the dentine with enamel contributing only a minor part through scattering at wavelengths in the blue range.

Opalescence effects in tooth enamel occur in enamel when visible light is scattered, causing the refl ection of shorter wavelengths of light and transmission of longer wavelengths. Indeed, when enamel slabs are observed in daylight, they appear to be pale blue in refl ection and pale yellow in transmission (Zijp et al., 1995). This effect is most noticeable toward the incisal edges of anterior teeth (Priest and Lindke, 2000). Approaches to the measurement of opalescence of tooth enamel in vitro have been described using a spectrophotometer and opalescence was shown to be signifi cantly different in human and bovine enamel specimens (Lee and Yu, 2007).

The surface morphology of a tooth infl uences the amount and type of light refl ection. A rough or coarse surface gives more diffuse refl ection whereas a more smooth and uniform surface allows more specular refl ection. Surface gloss affects the appearance and vitality of the teeth, where light refl ected from the tertiary anatomy adds to vitality whereas less vitality is evident when this anatomy is worn with age (Terry et al., 2002). One goal of aesthetic dentistry is to maximise specular refl ection and highlights from teeth by maintaining smooth and highly polished enamel and restorative surfaces (Serra and Otis, 2004). The amount of light refl ected from tooth surfaces in vivo has been demonstrated to signifi cantly increase following toothbrushing (Sturzenberger et al., 1975; Redmalm et al., 1985) and signifi cantly decrease in vitro following enamel demineralisation (Serra and Otis, 2004).

The fl uorescence of dental enamel and dentine has been studied by a number of workers (Perry et al., 1969; Spitzer and Ten Bosch, 1975; Fujimoto et al., 1977). Enamel fl uorescence excitation peaks have been found at 285 and 330 nm, with corresponding emission maxima at 360 and 410 nm (Spitzer and Ten Bosch, 1975). Dentine has fl uorescence excitation peaks at <300, 325, 380 and 410 nm, and emission maxima at ca. 350, ca. 400, 450 and 520 nm (Ten Bosch and Zijp, 1987). The compounds responsible for the fl uorescent properties of teeth are not fully characterised, although some organic compounds resembling dityrosine analogues have been isolated (Booij and Ten Bosch, 1982). The combination of fl uorescence from enamel and dentine is reported to enhance the whiteness or value of teeth (Terry et al., 2002). However, in contrast, it is reported from in vitro experiments with extracted teeth that fl uorescence does not contribute to visually observed tooth colour (Ten Bosch and Coops, 1995).


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14.4 The colour of teeth

The colour of a tooth is determined by the light scattering and absorption properties of the enamel and underlying dentine. Hall (1991) described tooth colour as containing 100 000 different colours. Despite this huge magnitude of possible different tooth colours, a number of studies have been conducted to evaluate the variations of colour in a single tooth, tooth colour distribution within an individual and across study populations with known demographics.

14.4.1 Tooth colour variation

The colour of a tooth is often not uniform across the whole surface. The middle region of the tooth has been described as the site that best represents the colour of the tooth (Goodkind et al., 1987) since the incisal region is often translucent and can be affected by the background, whereas the cervical region can be affected by light scattering from the pink coloured gingival tissues.

Hasegawa et al. (2000b) measured the colour of the labial surface of central incisors in fi ve different locations along the tooth axis from the incisal edge to the cervical region and found signifi cant variations in CIELAB values. For L*, this was highest in the central portion of the tooth (ca. 73) and was lower towards the cervical area (ca. 69) and incisal edge (ca. 69). For a*, the highest values were found towards the cervical area (ca. 8.5) and signifi cantly lower towards the incisal edge (ca. 2.0). For b*, the cervical region had the highest value (ca. 20) which was reduced signifi cantly towards the incisal edge (ca. 13). Similar trends were observed by O’Brien et al. (1997) when measuring the colour of 95 extracted anterior teeth from 35 patients. The mean L*, a* and b* values, respectively, were 71.4, 0.9 and 12.8 for incisal, 72.4, 1.2, and 16.2 for middle, and 72.6, 1.5 and 18.4 for cervical regions.

14.4.2 Tooth colour distribution

The range and distribution of tooth colour has been described by a number of investigators. In general, the maxillary anterior teeth are slightly more yellow than mandibular anterior teeth (Goodkind et al., 1987), and the maxillary central incisors are higher in value than the lateral incisors and canines (Goodkind et al., 1987; Hasegawa et al., 2000a). Gender differences in tooth colour have been reported (Odioso et al., 2000) in a study group of 180 people from the USA, where women had statistically higher value and less yellow teeth than males. The L* values for the maxillary central incisors were approximately 3.7 units lower on average in males compared to females. In contrast, other studies have observed no signifi cant gender differences in tooth colour (Van der Burgt et al., 1990; Hasegawa et al., 2000a).

Early studies presented tooth colour data in terms of chromaticity co-ordinates and tristimulus values and have been summarised by Miller (1987) and O’Brien


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et al. (1997). However, these colour scales do not lend themselves to ease of interpretation or comparison (O’Brien et al., 1997) and therefore the majority of contemporary studies use the CIELAB colour space to describe tooth colour. In a recent review of published CIELAB tooth colours (Joiner et al., 2008a), it is evident that there is a broad range of reported L*, a* and b* values for anterior teeth. For examples, a USA (Gozalo-Diaz et al., 2007) and a Chinese (Zhu et al., 2001) population had mean L* values of 73.3 and 54.91 respectively, with a range of 38.0 to 89.5 and 21.89 to 83.75 respectively. In addition, the reported mean L* values for study populations from Spain (Rubino et al., 1994) is 67.6, from Canada (Douglas, 1997) is 54.76 and from Japan (Hasegawa et al., 2000a) is 73.1. This broad variation in L* probably refl ects differences in the measurement techniques used in the various studies rather than real differences between study populations. This is confi rmed in a single study population from Korea which had a mean L* value of 57.8 when measured by one type of colorimeter and a mean L* value of 74.0 when measured with a different instrument (Cho et al., 2007).

In terms of mean a* values, these are generally fairly similar across different study populations and are reported as being from -1.69 to 5.4, with the overall maximum range of a* values reported as -8.07 to 9.21 (Joiner et al., 2008a). In terms of mean b* values, these range from -0.2 to 19.4 depending on study population (Joiner et al., 2008a), with the largest overall range in b* values of 6.53 to 59.89 reported for a Chinese population (Zhu et al., 2001).

14.5 Factors that impact tooth colour and its perception

Tooth colour can be affected by what are termed intrinsic and extrinsic colourations of the tooth, and both types have associated discolourations or stains (Nathoo, 1997; Watts and Addy, 2001; Joiner, 2004). In general, intrinsic discolouration is found within the enamel and dentine whereas extrinsic discolouration is found on the surface of the tooth. Other important factors are known to impact tooth colour including subject age and hydration state, and tooth colour perception can be impacted by the variables of lighting conditions, the observer and the context that the teeth are viewed.

14.5.1 Intrinsic tooth discolouration

Intrinsic tooth discolouration occurs following a change to the structural composition of the dental hard tissues and is attributable to the incorporation of chromogenic materials into the enamel or dentine (Watts and Addy, 2001; Brook et al., 2007). Intrinsic discolouration can occur during tooth development or after tooth eruption. During tooth development, a number of inherited and metabolic disorders can affect intrinsic tooth colour, such as amelogenesis imperfecta, which can give a range of colourations from cream and yellow to brown and black. Other


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pre-eruptive discolourations include dental fl uorosis and the incorporation of tetracycline during tooth development.

Dental fl uorosis intrinsic tooth discolouration is endemic in areas where the fl uoride content is above optimal values (Hattab et al., 1999). It is a subsurface hypomineralisation that occurs during tooth formation and the resulting defects can give a range of white opaque spots and streaks, through to yellow brown bands (Dayan et al., 1983).

The tetracycline family of antibiotics have a broad anti-microbial spectrum and are used to treat a variety of infections. However, it is known that tetracycline can cause extensive discolouration of children’s teeth due to the deposition of tetracycline into the developing enamel and dentine (Mello, 1967; Watts and Addy, 2001; Brook et al., 2007). Upon eruption, the affected teeth are initially yellow and exhibit a bright yellow fl uorescence. Over time, this initial bright-yellow colour appearance becomes dark-yellow to brown, particularly on the labial surfaces of the anterior teeth. This is due to the formation of degradation products of the incorporated tetracycline under the effects of daylight (Mello, 1967; Lambrou et al., 1977).

The discolouration of teeth following severe trauma is a common cause of post-eruptive intrinsic tooth stain (Brook et al., 2007). This is due to haemorrhaging within the tooth pulp chamber driving blood into the dentine where the red blood cells and haemoglobin degrade to strongly coloured chromogens to give an overall darkening of the tooth colour (Hattab et al., 1999; Watts and Addy, 2001).

14.5.2 Extrinsic tooth stain

The pellicle has a tendency to develop stain, termed extrinsic stain, particularly in those areas of the dentition which are inaccessible to toothbrushing and the abrasive cleaning action of toothpaste (Forward, 1991; Dawson et al., 1998). Extrinsic stain is typically yellow-brown or dark brown-black depending on its origin, and can appear as distinct patches or lines, or across the majority of the tooth surface in severe cases. This type of stain can clearly impact the aesthetics and colour of the teeth.

Extrinsic stain is reported to be promoted by smoking, dietary intake of certain foods and beverages (e.g. red wine, tea, coffee) and the use of certain cationic agents such as chlorhexidine, tin and iron (Watts and Addy, 2001). The mechanisms of extrinsic stain formation have been reviewed by a number of authors (Addy and Moran, 1995; Nathoo, 1997; Watts and Addy, 2001; Hannig and Joiner, 2006; Brook et al., 2007). The most likely mechanism involves the initial absorption and binding of materials onto the pellicle. These materials can be coloured in themselves to give direct staining or subsequently have chemical interactions with other materials to generate the discolouration. An example of direct staining was demonstrated by Macpherson et al. (2000) who showed that the levels of extrinsic


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stain formed over six weeks in vivo was correlated with the amount of smoking and tea consumption.

An example of indirect staining is exhibited by the anti-plaque agent chlorhexidine. This is colourless when fi rst bound to the pellicle, but its subsequent chemical interaction with iron salts or tea polyphenols in the mouth cause the formation of dark brown-black discolourations on the tooth surface (Addy and Moran, 1995; Watts and Addy, 2001).

14.5.3 Other factors

Natural tooth colour has a signifi cant tendency to change with the age of the subject, becoming darker and more yellow with increasing age (Goodkind et al., 1987; Zhao and Zhu, 1998; Hasegawa et al., 2000a; Odioso et al., 2000; Jahangiri et al., 2002). In the vast array of genetically determined tooth colourations, all teeth appear to darken over the course of time (Morley, 1997). Odioso et al (2000) reported that for a USA population the average tooth colour shifted by an increase of 0.10 for b* and a decrease of 0.22 for L*, for each year of life. The impact of subject age on tooth colour is due to a number of factors. As the dental pulp ages it shrinks, leaving secondary dentine in its wake (Morley, 1997), and the surrounding dentine will become harder and less vascularised. It is hypothesised that pigments and ions of an amorphous organic and inorganic nature permeate and deposit at the enamel–dentine junction, causing the dentine chroma to become more saturated (Morley, 1997). Coupled with decreasing enamel thickness due to normal tooth wear processes, the dentine colour begins to have a greater infl uence on the overall tooth colour. The net result is a progressive darkening and yellowing of the teeth with age (Joiner, 2004).

Another important factor to impact tooth colour is the hydration state of the teeth, since dry teeth will appear whiter (Russell et al., 2000; Mayekar, 2001). For example, allowing the teeth to dry out for 15 minutes caused a signifi cant increase in L* value as compared to the fully hydrated state, and it took at least 30 minutes to recover to baseline values (Russell et al., 2000). Therefore care must be taken to avoid excessive drying of the teeth when attempting to measure their colour.

14.5.4 Tooth colour perception

The perception of tooth colour is a complex phenomenon and there are many variables that can affect how it is perceived. For example, different light sources, intensities and metamerism effects can signifi cantly alter the accuracy of visual tooth colour measurement via shade matching (Dagg et al., 2004; Curd et al., 2006). In terms of the human observer, factors such as colour blindness, age, fatigue, nutrition, emotions, medications and binocular difference are important variables (Chu et al., 2004). The context that the tooth is viewed in is also important, since gum and lip colour can infl uence tooth colour perception (Chu,


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2003). For example, tooth whiteness perception can be increased by the addition of a magenta hue to the gums (Reno et al., 2000). Tooth whiteness perception can also be enhanced by reducing the yellowness (b*) of the teeth (Joiner et al., 2008a). Spatial contrast between teeth is another factor, since a recessed tooth will appear darker than the adjacent teeth whereas a protrusive tooth will look lighter (Chu et al., 2004).

14.6 Tooth whiteness

Whiteness is recognised as a special colour attribute and much research has been conducted in order to defi ne whiteness colourimetrically (Luo et al., 2005). The quantifi cation of whiteness has long been of importance to a number of industries including paper, paint, plastic and laundry industries, and a number of whiteness indices have been developed to address their needs. One of the whiteness indices is defi ned within the CIE nomenclature (WIC) along with a tint measure (T) (Hunt, 1998).

WIC = Y + 800(xp – x) + 1700(yp – y) [14.1]

T = 900(xp – x) – 650(yp – y) [14.2]

where the subscript ‘p’ refers to the chromaticity of the white point for the colour space, and x and y are the chromaticity coordinates defi ned from the tristimulus values:

x = X/(X + Y + Z) and y = Y/(X + Y + Z) [14.3]

In general, the use of whiteness indices in the dental fi eld is limited to only a few examples in the literature. Garcia et al. (1993) reported that the WIC was the best index for describing the whiteness of a group of 20 samples of porcelain teeth in vitro. More recently, Guan et al. (2005) used the WIC to determine the increase of whiteness of extracted teeth following a bleaching procedure for one hour or brushing with toothpaste for two minutes. They demonstrated using image analysis of digital images and spectrophotometer measurement techniques that the bleaching procedure gave a signifi cant increase in tooth whiteness whereas the brushing procedure did not. The WIC has been used to value rank the shade tabs of the Vita Lumin Shade Guide (Guan et al., 2005) and the Vitapan Master 3D Shade Guide (Lath et al., 2007), and the WIC has also been applied within forensic dentistry to help determine the age of dental skeletal remains (Martin de las Heras et al., 2003).

An alternative whiteness index (W*) based on CIELAB measurements and colour space has been described in the dental literature (Gerlach et al., 2002). This uses a nominal white point, defi ned as L* = 100, a* = 0 and b* = 0, and calculates the distance the colour of a tooth is from this point, as follows:

W* = [(a*)2 + (b*)2 + (L* – 100)2]1/2 [14.4]


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Changes in tooth whiteness (ΔW*), for example, following a tooth whitening procedure can be calculated from:

ΔW* = W*(treatment) – W*(baseline) [14.5]

The increase in tooth whiteness (ΔW*) following bleaching procedures was demonstrated in a number of clinical studies, for example after following seven days use of a 6% hydrogen peroxide product (Xu et al., 2007) and after three weeks use of a 6.5% hydrogen peroxide product (Guerrerohd et al., 2007).

In general, for a whiteness index to be valid it must be used on the type of material for which it is intended. Therefore, Luo et al. (2005) conducted a series of psychophysical experiments on a tooth shade guide to optimise the coeffi cients of the WIC formula according to visual results and a new optimised formula was developed for the evaluation of the whiteness of teeth (WIO), as follows:

WIO = Y + 1075.012(xp – x) + 145.516(yp – y) [14.6]

In terms of agreement with the visual assessment of tooth whiteness, the WIO formula was shown to outperform the WIC formula (Luo et al., 2005, 2009) and the most appropriate for assessing changes in tooth whiteness (Luo et al., 2007). The use of the WIO whiteness index has been utilised in a clinical study comparing 2 weeks use of either a non-whitening toothpaste or a 6% hydrogen peroxide tooth bleaching product where it was found that the bleaching product gave a signifi cantly greater increase in tooth whiteness than the toothpaste (Luo et al., 2007). The WIO whiteness index has also been used in a series of in vitro and clinical evaluations of a silica whitening toothpaste containing blue covarine where a signifi cant increase in tooth whiteness was measured following tooth brushing (Collins et al., 2008; Joiner et al., 2008b).

14.7 Measurement of tooth colour

The measurement and communication of tooth colour is important in modern aesthetic dentistry. For example, in the preparation and fi tting of a tooth crown the dentist must be able to measure the tooth colour of the existing dentition accurately, then communicate the tooth colour to the laboratory technician who will select the appropriate coloured dental materials to fabricate the crown, and together they will produce a crown that is an acceptable colour match to the existing dentition. Indeed, the gold standard for tooth colour measurement is the ability to match a restoration on a single tooth to the adjacent teeth, with suffi cient accuracy to be undetectable by the critical eye (Browning, 2003).

With the increase in the interest and demand in tooth whitening, the measurement of change in tooth colour is important for the communication between dentist and patient. In addition, for manufacturers of oral care products the measurement of tooth colour is important to them in order to demonstrate effi cacy of tooth


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whitening products in laboratory and clinical studies and to support related whitening claims.

There are a number of approaches to measuring tooth colour, ranging from visual subjective assessments by comparison of the tooth with a tooth coloured shade guide, to instrumental objective methods such as using spectrophotometers, colorimeters and digital image analysis techniques.

14.7.1 Visual assessment of tooth colour

The visual assessment of tooth colour by the comparison and matching of the tooth with a tooth colour shade guide under the same lighting conditions is the most frequently applied method in dentistry (Van der Burgt et al., 1990). The use of shade guides is a relatively quick and cost-effective method for measuring tooth colour. Tooth shade guides come in many types and forms, but essentially the basic design involves a series of standard tooth colours, often tooth shaped, and can be arranged according to chroma and/or value. An example is the Vita Shade Guide as shown in colour Plate XI between pages 42 and 43.

As colour perception is a subjective process, it is not surprising that inconsistencies among dentists attempting to match natural tooth colours and the inability of individual dentists to reliably duplicate their own selections have been reported (Culpepper, 1970). Tooth colour perception is affected by, for example, the general variables such as lighting conditions, experience and age of assessor, fatigue, length of exposure of the specimen to the eye, previous exposure of the eye, and colour blindness (Douglas, 1997; Joiner, 2004).

A number of methods and techniques for aiding the visual assessment of tooth shades have been described (Greenwall, 2001; Mayekar, 2001; Browning, 2003). One approach is to arrange the colour guide in terms of value and select the tooth value fi rst followed by hue selection. The hue can be viewed best in the middle third of the tooth because the range of colour changes from incisal to the cervical regions (Greenwall, 2001; Mayekar, 2001).

The control of the light source is important for consistent visual colour measurements with shade guides in the clinical situation and in laboratory experiments. In the latter, typically viewing cabinets or booths with controlled and specifi ed light sources and grey coloured surfaces have been used to improve tooth shade measurement (Ragain and Johnston, 2000; Joiner et al., 2008b). The tooth colour matching ability of individuals can be improved with training and experience (Ragain and Johnston, 2000; Watts and Addy, 2001). Indeed, training and calibration exercises for clinical assessors using shade guides to measure tooth colour are often conducted when evaluating longitudinal tooth colour changes during tooth whitening clinical studies (Godder et al., 1994; Leonard et al., 1998; Kihn et al., 2000). With these precautions, shade guides have been used successfully to measure tooth colour changes in a number of tooth whitening


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clinical studies (Haywood and Heymann, 1989; Kihn et al., 2000; Nathoo et al., 2002b; Browning, 2003; Sulieman, 2004).

Although shade guides are commonly used in dentistry as the colour standard to which tooth colour is matched, several disadvantages have been described. For example, the range of available shades is inadequate since it does not cover the complete natural tooth colour space (Goodkind et al., 1987; Miller, 1993); colour differences between successive shade tabs are not uniform and systematic (O’Brien et al., 1985; Li, 2003), with colour differences of up to 4.88 ΔE units between tabs of like adjacent shade (Li, 2003); none of the commercially available shade guides are identical (Yap, 1998); the results can not be transformed into CIELAB colour space (Van der Burgt et al., 1985); and the material used to fabricate the guide is different to the tooth material and the restoration material (Preston, 1985). To overcome some of these diffi culties, the Vitapan 3D-Master Shade Guide (VITA Zahnfabrik, Germany) was designed to have a broader and more uniform colour range, better colour distribution, and improve repeatability of measuring tooth shade as compared to other shade guides (Paravina et al., 2002; Hammad, 2003; Analoui et al., 2004). Despite these improvements, to date this shade guide has not been widely used for evaluating tooth whitening (Paravina, 2008) and has been criticised that the difference between shade groups is too course to make the tooth whitening benefi ts easily measured (Browning, 2003). Indeed, no commercially available shade guide is primarily designed for evaluating tooth whitening. However, recently a new VITA Bleachedguide 3D-Master was designed and developed to include additional shade tabs and value ordered to increase its applicability for monitoring tooth whitening (Paravina et al., 2007; Paravina, 2008).

14.7.2 Instrumental tooth colour measurement

Instrumental approaches for objectively measuring the colour of teeth have received much attention in the scientifi c literature. Spectrophotometers have been used to measure the visible spectrum of teeth in vitro and in vivo (Zhao and Zhu, 1998; Paul et al., 2002; Xiao et al., 2007). However, the widespread use of spectrophotometers in dental research and clinical settings has been hindered by the fact that the equipment is relatively complex and expensive (Tung et al., 2002; Chu et al., 2004). On the other hand, colorimeters have been widely used in dental research for measuring tooth colour both in vitro and in vivo (Li, 2003; Joiner, 2004) and in general shown to be reliable, have good repeatability, and are accurate for colour difference measurements (Van der Burgt et al., 1990; Goldstein and Schmitt, 1993; Douglas, 1997; Tung et al., 2002). For in vivo longitudinal tooth colour measurements with colorimeters, the use of custom made alignment devices (i.e. gum shield with apertures aligned to the anterior teeth and fabricated for each subject) are required to ensure accurate realignment of the colorimeter measuring head onto the tooth surfaces before and after treatment (Li, 2003).


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This approach has been successfully used to measure tooth colour changes following bleaching procedures in vivo (Rustogi and Curtis, 1994; Mokhlis et al., 2000; Nathoo et al., 2001). Methods to reposition a colorimeter onto extracted teeth in the laboratory have also been described and successfully utilised to determine tooth colour changes following a peroxide bleaching protocol (Rosenstiel et al., 1991).

Investigations into the relationship between the colour perceived by human observers and the colour measured by colorimeters appear to be inconclusive (Tung et al., 2002), where some investigators (Johnston and Kao, 1989; Van der Burgt et al., 1990) have reported a correlation while others have reported no signifi cant agreement (Goldstein and Schmitt, 1993; Okubo et al., 1998).

The reported disadvantages of using colorimeters for tooth colour measurement include: the instruments are designed to measure fl at surfaces, teeth are often not fl at and can have surface anomalies; teeth are translucent which can lead to edge losses and systematic errors; only small areas of the teeth can be measured at one time, leading to results that can be unrepresentative of the whole tooth; inter-instrument agreement can be poor; requires contact with the teeth which raises the need for measures to prevent cross infection, and can be intrusive to the patient (Joiner, 2004; Brook et al., 2007).

Advancements in technology in this area have given rise to the availability of a number of different types of commercial computer-aided tooth shade measurement systems, based on spectrophotometers and colorimeters (Chu et al., 2004; Hugo et al., 2005). Systems vary but essentially a measurement is taken from the patient’s tooth that is then converted directly by the system’s software into a tooth shade guide value. This can be a single shade or map subtle differences in shade across the surface of the tooth (Chu et al., 2004). A number of these systems were compared with visual assessment in a clinical study measuring the colour of teeth of 57 subjects. It was found that there was a signifi cant disagreement of the devices with human colour measurement results (Hugo et al., 2005). This indicates a defi cit in the conversion of colorimetric data into colour shades adequate to the test of human perception and suggests the need for further refi nements in methods. However, some types of shade taking devices have been successfully used in laboratory studies to measure quantitative changes in tooth colour following staining and bleaching protocols (Amaechi and Higham, 2002; Sulieman et al., 2003, 2004).

14.7.3 Digital image analysis techniques

Recent advances in photography and computing have resulted in the widespread use of the digital camera for colour imaging (Jarad et al., 2005). The camera records digital data from an object which can be subsequently used to view an image on a computer. These images can be analysed with appropriate software to generate colour values from the whole or selected parts of the image.


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A number of digital imaging systems have been described in the literature for measuring tooth colour in vitro and in vivo (Cal et al., 2004; Guan et al., 2005; Jarad et al., 2005; Lath et al., 2007; Luo et al., 2007; Sagel and Gerlach, 2007; Mohan et al., 2008; Smith et al., 2008). In general, the systems consist of a digital camera and a controlled lighting source. There is no consensus on which camera or lighting source is best to use as different research groups utilise different makes and models. However, a number of different makes and models of digital cameras have been evaluated and shown to have potential for use in the colour replication process of clinical dentistry (Bengel, 2003; Elter et al., 2005; Wee et al., 2006). In general, the subject uses a lip retractor to expose the anterior teeth and their head positioned in front of the camera via for example, a chin rest, forehead bar or a cephalometric head apparatus. The image of the teeth is captured and transferred to a computer where the software converts the RGB values of the required tooth image pixels to, for example, CIELAB values using an appropriate algorithm. The whole system is colour calibrated via imaging a standard white tile, coloured tile or grey tile before and/or during each clinical session.

The measurement of tooth colour via image analysis of digital images has been shown to be highly reproducible in both in vitro and in vivo studies (Gerlach et al., 2000; Cal et al., 2004; Smith et al., 2008), and has been reported to be more reliable in tooth colour quantifi cation than a spectrophotometer (Guan et al., 2005). Image analysis of digital images has been applied successfully in a number of clinical trials evaluating tooth whitening procedures and has measured the increase in tooth whiteness following bleaching procedures (Gerlach et al., 2000; Ferrari et al., 2004; Mohan et al., 2008) and the use of whitening toothpastes (Collins et al., 2008). It has also been used to demonstrate uniform tooth whitening effects in different regions of the teeth by measuring colour changes in the body and interproximal tooth areas (Gerlach and Barker, 2003). Its use in colour shade matching has demonstrated the potential of the technique for improved shade matching performance than visual methods (Jarad et al., 2005).

Advantages of this technique in clinical research include: increase in objectivity of measurement and improved reliability; ability to measure small differences in tooth colour; reduction of bias in studies; no contact with the tooth surface thus avoiding cross contamination issues, and the images can be archived. This latter advantage can help in clinical case presentation, planning secondary analyses or for follow-up in data quality assessment (Brook et al., 2007; Sagel and Gerlach, 2007).

14.8 Measurement of extrinsic stain

14.8.1 Clinical indices

An important part of the evaluation process for a whitening toothpaste is its clinical effi cacy, particularly in terms of its extrinsic stain removal and prevention.


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Extrinsic stain is usually determined by a trained clinical examiner who views the anterior teeth under controlled lighting conditions and scores the stain by using a clinical index (Macpherson et al., 2000), although photography has sometimes been used (Eriksen et al., 1979; Addy and Moran, 1985). The Lobene stain index (Lobene, 1968) divides the tooth surface into gingival and body regions. The level of stain is then scored as 0 – no stain, 1 – light stain, 2 – moderate stain, and 3 – heavy stain. In addition, the extent which the stain covers the gingival and body areas is scored as 0 – no stain detected, 1 – stain over a third of region, 2 – stain over two thirds of the region, and 3 – stain over more than two thirds of the region. This index has frequently been used to successfully evaluate whitening toothpastes (Yankell et al., 1994; Nathoo et al., 2002a), although alternative indices or modifi cations of the Lobene index have been reported (Davis and Rees, 1975; Shaw and Murray, 1977; Macpherson et al., 2000).

14.8.2 Instrumental methods

The use of spectrophotometers and colorimeters has routinely been used to measure and quantify artifi cially formed extrinsic stain on teeth, tooth specimens, hydroxyapatite and perspex substrates in vitro and to evaluate the effi cacy of tooth whitening products (Prayitno and Addy, 1979; Dawson et al., 1998; Suliemann et al., 2003; Joiner and Thakker, 2004; Joiner, 2006a).

Image analysis techniques for the evaluation of stain removal effi cacy of whitening toothpastes in vitro have also been described. Tantbirojn and Douglas (1998) used bovine enamel specimens with naturally occurring stain and showed that the image analysis technique provided an objective and quantitative measurement to distinguish stain removal effi cacy of a range of toothpastes. Lath et al (2006) used acrylic blocks stained with saliva, chlorhexidine and tea and demonstrated that an imaging system was a reliable alternative to spectrophotometry for measuring stain removal by toothpastes.

Quantitative light-induced fl uorescence (QLF) has been used to assess artifi cial stain build-up and removal on extracted teeth (Amaechi and Higham, 2002). This technique involves the capturing of fl uorescent images of the tooth, where stained areas show reduced fl uorescence compared to non-stained areas. QLF has demonstrated the ability to detect and quantify staining longitudinally in vitro and has shown a high correlation with digital imaging (Adeyemi et al., 2006). However, its use in clinical applications has yet to be reported.

14.9 Methods to improve tooth colour

Many individuals are dissatisfi ed with their dental appearance and in particular with their current tooth colour. In a UK survey of 3215 subjects, 50% perceived they had some kind of tooth discolouration (Alkhatib et al., 2004). Other recent studies have shown that personal dissatisfaction with tooth colour can range from


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17.9–52.6% depending on the study population (Odioso et al., 2000; Alkhatib et al., 2005; Xiao et al., 2007). With this relatively high level of tooth colour dissatisfaction, it is perhaps not surprising that the demand from patients and consumers for tooth whitening procedures and products has increased. Indeed, the market for tooth whitening products has been growing dramatically over the last few years, particularly in the United States and Western Europe (Gerlach, 2002; Pickles et al., 2005).

Tooth colour can be improved by a number of methods and approaches including whitening toothpastes, professional cleaning by scaling and polishing to remove stain and tartar, internal bleaching of non-vital teeth, external bleaching of vital teeth, microabrasion of enamel with abrasives and acid, and placement of crowns and veneers (Berman, 1982; Joiner et al., 2002; Sarrett, 2002).

14.9.1 Whitening toothpastes

In response to the consumer dissatisfaction with their perceived tooth colour, oral care product manufacturers have developed a vast array of contemporary whitening toothpastes. Most contain the same basic functional ingredients, all of which have a specifi c role to play within the formulation. These typically include: solid abrasive materials; humectant to prevent the formulation from drying out; thickening agent to control the rheological properties; surfactant to generate foam and impart desirable sensorial properties during use; and active agents such as fl uoride to provide health benefi ts, and fl avour.

Whitening toothpastes provide this benefi t by removing and preventing the formation of extrinsic stain. It is well documented that if a very low abrasive toothpaste is used, stained pellicle usually accumulates on the surfaces of teeth (Kitchin and Robinson, 1948; Stookey et al., 1982) and it is now widely accepted that toothpastes require a certain amount of abrasivity to remove or prevent extrinsic stains from forming (Nordbo, 1977; Stookey et al., 1982; Joiner et al., 2004). The evidence to date suggests that the primary stain removal ingredient in whitening toothpastes is the abrasive, although other ingredients such as surfactants, calcium chelators, enzymes and polymers have been described in the literature (Joiner, 2007) which are claimed to aid removal and/or prevent extrinsic stain.

Types of abrasives used in contemporary toothpastes include hydrated silica, calcium carbonate, dicalcium phosphate dihydrate, calcium pyrophosphate, alumina, perlite and sodium bicarbonate (Hefferren, 1998). These function by a process termed abrasion, which can be defi ned as the removal of material from the bulk of the substrate during relative movement of the abrasive and substrate (Davis, 1978) and the term can be applied to the removal of tooth surface fi lms, such as stained pellicle, by toothpaste abrasives. There are a number of key parameters that have been demonstrated to affect the abrasion process, including particle hardness, shape, size, size distribution, concentration and applied load (Joiner, 2007).


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The understanding of the abrasive action and cleaning of extrinsic tooth stain has enabled oral care product manufacturers to continually develop optimised levels of abrasives within toothpaste formulations, carefully tailored to meet specifi c consumer needs in terms of the level and speed of the tooth whitening achieved, while moderating the abrasivity of a toothpaste towards the dental hard tissues. In this way, products have been designed to maximise cleaning while minimising hard tissue wear. Indeed, in a recent review of experimental and clinical evidence of tooth wear by toothpastes, Addy and Hunter (2003) state that, ‘brushing with a toothbrush and toothpaste produces limited dentine wear in a life time of use, and virtually no wear to enamel’. Combinations of abrasives in toothpastes can be particularly successful. For example, a whitening toothpaste containing a combination of perlite and calcium carbonate has been shown in a clinical study to signifi cantly remove extrinsic stains in two weeks versus control toothpaste (Collins et al., 2005). This product was also shown not to give a clinically relevant level of wear to enamel or a signifi cant increase in dentine wear compared to a marketed non-whitening toothpaste (Joiner, 2006a).

An alternative optical approach to abrasive tooth whitening has recently been described (Collins et al., 2008). This involves the deposition and retention of a blue pigment from toothpaste onto the tooth surface where it can alter the optical properties of the tooth by primarily giving a yellow–blue shift in tooth colour. This yellow–blue colour shift is known for its importance in aiding the overall perception of tooth whiteness (Joiner et al., 2008a). Thus, immediately after brushing with the toothpaste the teeth appear less yellow and whiter. This optical whitening effect has been demonstrated through in vitro experiments where extracted teeth were brushed with the blue pigment containing toothpaste and measurements taken using a colorimeter and by visual assessment with a shade guide (Joiner et al., 2008b). Whitening benefi ts for this toothpaste was also demonstrated in a randomised clinical study (Collins et al., 2008).

14.9.2 Vital tooth bleaching

Intrinsic tooth colour can generally be improved by bleaching and typically responds favourably to oxidation with peroxide (Gerlach, 2002). This is usually in the form of hydrogen peroxide or carbamide peroxide, formulated into a gel formulation which is applied to the enamel surfaces of the teeth via trays, strips or simply painted on. The mechanism of peroxide action is not fully known but involves diffusion of peroxide into the tooth where it oxidises coloured materials found within the dental hard tissues giving a reduction in tooth chroma and making the teeth whiter (Joiner, 2006b). The fi nal tooth whitening outcome of bleaching is dependent on a number of factors, but is strongly correlated with peroxide concentration and contact time, activation by heat and light may be possible, the type of intrinsic stain and the starting tooth colour (Joiner, 2006b).


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Vital tooth bleaching products and procedures generally fall into three categories, namely, dentist supervised nightguard bleaching, in-offi ce or power bleaching and mass market bleaching products (Heymann, 2005). Nightguard bleaching involves placing a relatively low level peroxide (i.e. 3–6% w/w) containing gel into a custom fabricated mouth guard and this is worn at night for at least two weeks. In contrast, in-offi ce or power bleaching uses much higher concentrations of peroxide (25–35% w/w) which are applied for typically 30 minutes per treatment. Although signifi cant tooth whitening can be achieved in one treatment, multiple treatments may be required to achieve optimum whitening (Sulieman, 2005b). In general, mass market consumer products also contain relatively low levels of peroxide (3–6% w/w) that are self applied twice per day for up to two weeks and come in a range of product formats such as trays, strips or paint-on. Recent developments in mass market bleaching products has seen the launch of higher levels of peroxide being used and products that are based on alternative bleaching agents such as activated sodium chlorite (Joiner, 2006b).

Tooth shade guides have been used by dental clinicians to measure longitudinal tooth colour changes in clinical product testing (Haywood and Heymann, 1989; Kihn et al., 2000; Nathoo et al., 2002b; Sulieman, 2004). For example, a mean 2.71 shade improvement from baseline was measured following the two weeks twice per day use of an 18% w/w carbamide peroxide paint-on gel (Nathoo et al., 2002b). Colorimeters, spectrophotometers and camera-based digital imaging and analysis have all been used to measure tooth bleaching, both in vitro and in clinical trials (Joiner, 2006b). For example, the mean change from baseline in L* and b* was 2.07 and -1.67, respectively, following 14 days use of a tray-based 10% carbamide peroxide product (Gerlach et al., 2000).

14.9.3 Non-vital tooth bleaching

A non-vital tooth is one that has a dead pulp or has had the pulp removed as part of an endodontic treatment. A non-vital tooth can appear much darker and discoloured compared to the adjacent teeth. This type of discolouration has been successfully treated by the ‘Walking Bleach Technique’ whereby a bleaching composition is sealed inside the pulp cavity for a number of days (Sulieman, 2005a). The types of bleach compositions that have been used typically include sodium perborate alone or in combination with low levels of peroxide (Greenwall, 2001; Attin et al., 2003; Plotino et al., 2008). Much shorter exposure times of the pulp cavity with relatively high levels of peroxide (30–35% w/w) are also used, which may be activated with heat or light, in a technique termed ‘Internal Non-Vital power Bleaching’ (Suliemann, 2005a). Bleach compositions can also be applied simultaneously to the pulp and the external enamel surfaces in a technique called ‘Inside/outside Bleaching’ (Sulieman, 2005a; Kelleher, 2008).


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14.9.4 Microabrasion

Microabrasion is a dental professional treatment for improving the colour of some types of intrinsically stained teeth (Croll, 1991). It involves applying an abrasive slurry (e.g. pumice, silicon carbide) in combination with a strong acid (e.g. hydrochloric acid) to the surface of teeth using a dental hand-piece. This removes the surface layers of enamel which can contain the stain (McEvoy, 1989). Clinical application needs to be used with caution to avoid excessive enamel removal since it has been shown that a commercial microabrasion product removed 134.8 microns of enamel after 20 seconds of application in vitro (Schmidlin et al., 2003). Following microabrasion, subsequent polishing has been shown to be crucial in maintaining optimal tooth aesthetics (Paic et al., 2008). The technique has been successfully applied to fl uorosis discoloured teeth (Croll, 1991; Limeback et al., 2006). However the depth of discolouration cannot be known until attempts are made to remove it (McEvoy, 1998), and if it is too deep, it cannot be removed using microabrasion, and thus a restorative solution should be considered (Sarrett, 2002). Indeed, the combination of surface microabrasion and the placement of resin-based composite veneers was shown to improve the aesthetics of discoloured anterior teeth (De Araujo et al., 2000). The combination of microabrasion followed by tooth bleaching has also been shown to improve anterior tooth colour (Greenwall, 2001; Kelleher, 2008).

14.10 Future trends

The colour and appearance of teeth is a complex phenomenon and this topic will progress in the future through research at the interface of many different scientifi c disciplines. Signifi cant advances in the future are likely to be seen in the area of tooth colour measurement. Despite the limitations of visual assessment methods of measuring the colour of the teeth using a shade guide by the dentist, it is still a quick and simple method for recording basic tooth colour and therefore further developments of new improved shade guides and their application is likely to continue (Paravina, 2008). The major future development in tooth colour measurement will come in the area of instrumental measurement, particularly in the standardised capture of tooth images, computer software manipulation and colour calibrated computer screens. This will enable the improved computer-aided colour shade determination of natural teeth in order to refl ect human visual perception better. In addition, this will enable variations in tooth colour to be viewed and improve communication between clinicians and dental materials technicians in order to produce an acceptable restoration. The application of image analysis techniques for the clinical assessment of extrinsic stain has not been fully evaluated and validated and in the future, this approach may fi nd further clinical application. When coupled with other measurement techniques, such as QLF, this may give further advantages in clinical stain measurement (Brook et al., 2007). Progress in


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digital image analysis measurement techniques are also likely to be further applied to the colour measurement of other oral tissues such as the gums in order to give an objective assessment of gingival health (Denissen et al., 2007).

Computer simulation of teeth is in its infancy (Lindsey and Wee, 2007) and there exists the means today to create far more realistic computer simulations of the dentition. With further developments, these simulations could be used in the future to investigate a number of hypotheses based on the perception of tooth colour, translucency, surface texture, gloss, patient satisfaction, etc. Further, the combination of the ability to realistically simulate human teeth with the ability of computers to automate data collection in a highly repeatable and effi cient way, will allow the opportunity to give rapid progress in establishing reliable standards for the fabrication of prosthetic teeth.

With the continued interest in tooth whitening by patients and consumers, the growth in understanding of tooth colour, stain mechanisms and the factors that affect its perception will concomitantly continue. Oral care product manufacturers are likely to translate this understanding into step change technology for tooth restorations and tooth whitening products in the future. What these new products and procedures will look like only time will tell. However, ultimately, patients, consumers and the fi eld of aesthetic dentistry will all greatly benefi t from these future developments.


Tooth colour and its application to clinical dentistry are covered by a number of books. Fundamentals of Color: Shade Matching and Communication in Esthetic Dentistry (Chu et al., 2004) covers the basics of the science and art of colour to enable the reader to better understand the mechanics involved in the tooth shade-matching process, whether visual or instrumental, together with clinical case studies. Further in depth clinical experience together with handy hints and tips on the whole process of measuring and fi tting aesthetic dental restorations can be found in the book by Ahmad (2006). The practical and clinical application of dental bleaching to effect tooth whitening is described in great detail in a number of books (Greenwall, 2001; Haywood, 2007; Kelleher, 2008). These books also go into more detail regarding the chemistry of bleaching, tooth discolourations and clinical case studies.

The International Association of Dental Research (www.IADR.com) has a mission to advance research and increase knowledge for the improvement of oral health worldwide. This association organises global and regional scientifi c conferences where topics on tooth colour and whitening are often presented and their website allows the electronic searching of recent past conference abstracts.

The Society for Color and Appearance in Dentistry (www.scadent.org) is a consortium of dental professionals and other experts interested in the application of colour science and appearance in dentistry. Their website has many useful links to other relevant organisations, journals and meetings of interest.


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Plate XI The Vita Shade Guide.

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371

15Hair color measurement

D. J . TOBIN, University of Bradford, UK

Abstract: Skin and hair color contribute disproportionately to human communication due to their prominent role as signals for health and cultural identity. Hair pigmentation is of interest to a broad range of professionals including biomedical scientists, anthropologists, forensic science researchers, as well as the cosmetic science and toxicology industry. The chapter begins with a discussion of how natural hair color is formed in the hair follicle, and how this changes with age and in hostile environments. From there strategies for artifi cial hair coloring are described as well as how researchers, especially in industry, measure hair fi ber color produced either naturally or via artifi cial colorants. The chapter concludes with comment on future trends in the fi eld.

Key words: hair follicle, hair fi ber, pigmentation, melanin, melanocyte, melanogenesis.

15.1 Introduction

Skin and hair color have since time immemorial contributed disproportionately in human communication due to its prominent role in informing on health and cultural identity. Study of pigmentation and its variation is currently pursued by a broad range of interested professionals and this chapter will approach the subject from a number of angles to hopefully provide something of interest for all readers. The chapter begins with a description of how natural hair color is formed at the level of hair follicle pigmentary unit during embryogenesis in our mother’s womb. Much is set in train at this stage, most particularly the inheritance of our so-called phenotype, which includes both normal and abnormal pigmentation variants some of which can have associated pathology. Once formed the activity of the hair follicle pigmentary unit is thereafter tightly coupled to the hair growth cycle, and is regulated by a battery of extrinsic and intrinsic factors. In time the functionality of the hair follicle pigmentation system begins to fail, with the loss of hair color (through graying) being one of our most striking bellwethers of lost youth. The chapter will then address how environmental factors may impact on the functionality of the hair pigmentary system, and together with aging will comment on how the use of artifi cial colorants can mask some of these aging effects. The hair colorant industry and others have adopted numerous and sophisticated approaches to color measurement. The chapter concludes with a review of future trends in this subject area.

15.2 Background

If beauty is in the eye of the beholder, it is not surprising then that as social beings much of our communication is via our physical appearance. In


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particular, pigmentation has been a focus of attention and study for over 4000 years (Westerhof, 2006) due to its role in informing on health and cultural identity. Study of pigmentation variation in humans is currently pursued by a broad range of professionals including biomedical scientists, anthropologists, forensic science researchers, as well as the cosmetic science and toxicology industry. For example, a recent and unexpected piece of research to whet not only the anthropologist’s appetite has been a report that Neanderthals exhibited variants of the MC1R gene, a gene associated with the red hair phenotype in humans (Lalueza-Fox et al., 2007).

Of the three pigments present in skin, hemoglobin and carotenoids contribute little to overall skin or hair color. By contrast, the main determinant to our phenotypic palette derives from a class of mixed indole-rich compounds – the melanins, which are formed within unique organelles called melanosomes. These lysosome-related organelles are produced within the cytoplasm of the melanocyte via a complex and phylogenetically ancient biochemical pathway called melanogenesis.

Red hair color has dominated recent attention not least for its much rarer and more dramatic visual cue value than for other colors, but also due to its association with an increased risk of melanoma. The purported positive selection pressure, for maintaining dark skin and hair pigmentation seen in much of Africa and Asia for its associated protection from solar ultraviolet light (Rees, 2006; Parra, 2007), appears to have been lifted when humans migrated to more northern climes where requirement for Vit D synthesis competed with lower UVR levels.

Hair growth and pigmentation have clearly facilitated evolutionary success in non-human mammals. However, it is not at all clear how (or if) these traits have contributed to survival in humans. More enigmatic still is why humans should be unique among the primates in growing scalp hair that is very thick, long, and highly pigmented. Teleologically, this phenotype is likely to refl ect particular evolutionary selective pressures that were present during the early stages of human evolution. Some authors, for example, have suggested that this may have aided human development along sea coasts and river banks where fi sh were a dominant part of the diet (Morgan, 1985). In this context it would have been important to develop strategies to prevent the build-up of toxic metals from fi sh species which concentrate heavy metals. The selective and avid binding of toxins and metals to melanin within the rapidly growing and highly melanized hair fi ber would have been an important adaptation. The highly proliferative hair follicle bulb tissue (second only to the bone marrow) would, via its melanin-accepting keratinocyte population, rid the body of this harmful substances via an extruded pigmented hair shaft. There may have been further evolutionary pressures for pigmented hair growth including those which pertained to mate selection strategies, and to the fact that our relative nakedness draws much attention to our facial and scalp hair. Thus, sexual dimorphism based on traits like beard growth may have been critical for the development of optimal communication strategies.

Hair color measurement 373

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The African origin of humans refl ects well the current deep brown-black predominance of human hair color – true for over 90% of the world’s human population. However, that black scalp hair predominates in all primates living in tropical climes may appear somewhat paradoxical given its associated thermal insulating properties via the trapping of radiant heat. A probable explanation may be the protection against sunstroke afforded by deep skin and hair pigmentation, as well as the contribution of melanin’s very effi cient and fast exchange of ion transport and effl ux to adequate salt balance (Wood et al., 1999). If over 90% of the human population have ‘environmentally-friendly’ brown-black hair and tanning skin, why then did the remaining 5–10% (mostly ‘originating’ in northern Europe) emerge with a bewildering array of hair colors that range from white blonde, yellow blonde, auburn to red and all shades in between? Recent advances in molecular genetics offer intriguing clues that may explain the basis of this dramatic diversity of human cutaneous pigmentation and phenotype. In particular, the discovery of the role played by the melanocortin-1 receptor (MC1R) gene has been hugely signifi cant, and more particularly its tendency to mutate to yield functional variability (i.e. polymorphism) (Rees, 2000). Regardless of our MC1R genetic inheritance, the follicular melanin unit appears to have an intrinsic ‘biologic clock’, and canities is an inevitable harbinger of disappearing youth.

15.3 Natural hair color

15.3.1 Development of the hair follicle pigmentary unit

Melanocytes of both the epidermis and the hair follicle derive from immature cells that migrate from the neural crest (a transient component of the ectoderm located between the neural tube and the epidermis) into the skin during embryogenesis. How some of these neural crest cells commit to the melanocyte lineage remains a subject of intense research (Dupin & Le Douarin, 2003). The most obvious evidence of melanocyte maturation is the initiation of melanogenesis (melanin synthesis), and in human skin this occurs very early at around seven weeks of gestation (Holbrook et al., 1989). Melanoblasts have the potential to proliferate and differentiate further when within the epidermis. Some progeny leave the epidermis to distribute in the now developing hair follicles. Importantly, these hair follicle-homing melanocytes represent a mixed population of 3,4-dihydroxy phenylalanine (dopa) oxidase-positive (i.e. express active tyrosinase) and dopa oxidase-negative cells (i.e. either fail to express tyrosinase or express an inactive tyrosinase) cells (Peters et al., 2002).

Of the more than 100 genetic loci shown to affect pigmentation (Nakamura et al., 2002; Steingrimsson et al., 2006), mutations in receptor tyrosine kinase KIT and its cognate ligand SCF, and endothelin 3 and its receptor Ednrb have been most informative. Mutations in these loci can cause almost complete lack of hair pigmentation.


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Casual observation of our fellow humans highlights a relative independence of the epidermal- and follicular-melanin units. For example, one can readily appreciate the co-expression of white hair and black skin in aging people of African descent and conversely the raven hair of some white-skinned Europeans. Furthermore, melanocytes of the follicular-melanin unit are larger, more dendritic, have more extensive cytoplasmic organelles and produce larger melanosomes compared to melanocytes in the epidermal-melanin unit (Tobin & Bystryn, 1996; Tobin & Paus, 2001). While melanin produced by the latter degrades almost completely in the differentiating layers of the epidermis, brown/black eumelanin granules transferred into hair cortical keratinocytes remain minimally digested; hence, the similarly pigmented proximal and distal ends of a typical hair shaft.

Melanocytes in the mature adult anagen (i.e. growing) scalp hair follicle are distributed in several distinct anatomic compartments where they are associated with a region-specifi c differentiation status (Fig. 15.1). Melanogenically-active melanocytes positive for 3,4-dihydroxy phenylalanine (dopa)-oxidase activity are readily detectable in the basal layer of the infundibulum and in the hair bulb matrix around the upper dermal papilla. Moderately differentiated melanocytes may also be detected in the basal layer of the sebaceous gland, although it is not

Epi

HB

ORS

SBSB

15.1 Cartoon of pigmented and graying human anagen scalp hair follicle, showing loss of melanization in the hair bulb and hair shaft with graying. Some amelanotic melanocytes can be seen in the outer root sheath (ORS) and in the most proximal and peripheral hair bulb (HB). SB, sebaceous gland; Epi, epidermis.


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immediately clear what their function is there (anti-microbial?). The hair bulb is the only site of pigment production for the hair shaft (Fig. 15.1), and contains melanogenically-active melanocytes. Melanogenically-active melanocytes are located just below the pre-cortical keratinocytes, from where melanin can be transferred to the hair shaft cortex (Fig. 15.2), less so to the medulla, and very rarely the hair cuticle.

The presence of immature melanocytes in fully-developed adult anagen hair follicles has been confi rmed both in vivo and in vitro and some of these may indeed have bona fi de melanocyte stem cell potential (Horikawa et al., 1996; Tobin, 2003). Dopa oxidase-negative amelanotic melanocytes can be readily detected in the mid-to-lower outer root sheath, but also in the periphery of the bulb and the most proximal matrix (Figs 15.1, 15.2). Although amelanotic hair follicle melanocytes may lack dopa-oxidase activity, low levels of the tyrosinase protein itself may still be detected in some of these cells, as well as KIT and Bcl-2. However, these melanocytes are still immature as they do not express the melanogenic enzymes tyrosinase-related protein-1 (TRP1) and TRP2 (dopachrome tautomerase, DCT) (Horikawa et al., 1996). These pigment cells could represent a pool of ‘transient’ melanocytes that migrate from the hair follicle bulge to other areas of the outer root sheath (Slominski et al., 1994; Tobin et al., 1999; Van Neste & Tobin, 2004).

By far the most striking difference between the epidermal- and follicular-melanin units, and one with signifi cant implications for the regulation of hair pigmentation, is the observation that the activity of the hair bulb melanocyte is under tight cyclical control (Slominski et al., 1994). Epidermal melanocytes, by contrast, appear to be continuously active as far as melanogenesis is concerned (Nordlund & Ortonne, 2006), though this constitutive activity can be stimulated further (e.g. after exposure to UV radiation).

15.3.2 Hair follicle melanocytes during the hair cycle

Active hair pigmentation only occurs during the period of active hair growth or anagen (Fig. 15.2). In the human scalp, hair follicle active melanogenesis can last up to 10 years, though more usually for approximately three years (Tobin et al., 1999; Stenn & Paus, 2001). By contrast, melanogenesis continues for one month or less in other body sites (e.g. eyebrow).

Telogen

To follow the fate of melanocytes during the hair follicle growth cycle it is perhaps useful to look fi rst at their status in the ‘resting’ or telogen hair follicle, during which no melanin is actively produced. Some immature melanocytic (stem?) cells are distributed in the so-called secondary germ of telogen follicles and can be detected immunohistochemically for DCT.


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15.2 Longitudinal section of a fully-pigmented human anagen scalp hair follicle showing intense melanization of the hair bulb and hair shaft.


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Early anagen

After the fi rst one or two days of new anagen growth (so-called anagen I) some immature melanocytic cells begin to express the tyrosinase gene, and then some tyrosinase protein. Melanocytes distributed close to the anagen follicular papilla are more melanogenically-active than melanocytes located elsewhere in skin. We have shown that the follicular papilla of early anagen hair follicles pool high concentrations of L-phenylalanine, a potential requirement for the supply of L-tyrosine for melanogenesis (Schallreuter et al., 1998).

Melanocytes start to divide as early as anagen II, with a burst of melanocyte mitosis a little later in anagen III (Sugiyama, 1979). Hair bulb melanocytes continue to increase in number until full anagen VI and also to produce more of the cellular machinery needed for melanogenesis. Maturing melanocytes also become much more dendritic at this stage, in preparation for the active transfer of mature melanosomes to pre-cortical keratinocytes.

Full anagen

The hair pigmentary unit becomes fully-functional with respect to melanin synthesis during full anagen VI, and the heterogeneous melanocyte subpopulations are now distributed in discrete locations throughout the hair follicle (Fig. 15.1). It is the nature of this variable of follicle melanocyte subpopulation status which remains so enigmatic, as they exhibit characteristic protein expression profi les. Only melanocytes distributing to the so-called melanogenic zone of the hair follicle (i.e. the hair bulb matrix above the follicular papilla) express the crucial TRP1, DCT, tyrosinase and KIT proteins in the majority of melanocytes.

Although anagen VI or full anagen lasts for three or more years in normal human scalp, it is not clear whether melanocyte activity/status changes signifi cantly during full anagen VI. The physiologic decrease in follicular melanogenesis during the end of anagen VI may refl ect either an exhaustion of an active signaling system that stimulates melanogenesis, and/or may be caused by the production of inhibitors of melanocyte activity (Sugiyama, 1979).

Catagen

Some of the earliest signs of imminent hair follicle regression include the retraction of melanocyte dendrites and the attenuation of melanogenesis during late anagen VI (Keogh & Walsh, 1965). Perhaps surprisingly, a limited amount of keratinocyte proliferation even continues for a short while thereafter, such that the most proximal telogen hair shaft is usually unpigmented. The need for melanocyte replacement is clear, because at least a proportion of the mostly highly melanotic (and possibly terminally-differentiated) hair bulb melanocytes do not survive


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catagen (Tobin et al., 1998). Some melanogenically-active melanocytes may derive however from a subpopulation of catagen-surviving melanocytes (Commo & Bernard 2000). Our current view suggests that newly-recruited immature melanocytes, derived from the melanocyte reservoir in the upper hair follicle, rebuild the pigmentary unit (Tobin et al., 1999; Nishimura et al., 2002). This is supported by the existence of a subpopulation of immature melanocytes in the hair bulge (Botchkareva et al., 2001; Nishimura et al., 2002), progeny of which migrate into the reforming anagen hair bulb.

15.3.3 Regulation of hair follicle pigmentation

Melanogenically-active follicular melanocytes appear to be located beyond the reach of direct stimulation by UV radiation – the principle regulator of melanocytes in the epidermis. Despite this, follicular pigmentation is still responsive to numerous intrinsic factors including: hair-cycle-dependent changes, body distribution, racial and gender differences, variable hormone responsiveness, genetic defects, and age-associated change (Tobin et al., 2008; Tobin, 2005; Slominski et al., 2004). As with any multi-step process, several positive and negative regulators/factors regulate hair pigmentation (Boissy et al., 1998; Commo et al., 2004; Slominski et al., 2004; Nordlund & Ortonne, 2006).

Melanogenesis can be mostly conveniently divided into: (1) melanosome/melanin granule biogenesis and (2) the biochemical pathway that converts phenylalanine/L-tyrosine into the melanins. Melanosome structure correlates with the main type of melanin produced. For example, melanocytes in darkest brown/black hair follicles contain the largest number of melanosomes (and most electron-dense), with a characteristic and highly ordered internal fi brilar matrix. These melanosomes are termed true or eu-melanosomes, and their melanin termed true or eu-melanin. Brown hair bulb melanocytes also contain eumelanosomes, but these are somewhat smaller than in black hair. Melanocytes in blonde hair follicle bulbs however, produce only weakly melanized melanosomes, with little eumelanin, such that their melanosomal matrix is visible and not obscured by melanin. By contrast, red hair bulb melanocytes produce the pheomelanosome – with a characteristic disordered internal structure. Unlike the fi lbrillar core of the eumelanosome the pheomelanosome contains a vesicular matrix with red/yellow melanin deposited irregularly as blotches, apparently in a random fashion not guided by a substructure. Perhaps surprisingly, eumelanogenic and pheomelanogenic melanosomes often co-exist in the same melanocyte.

The constitutive color of an individual’s hair is due to absolute tyrosinase activity, rather than levels of tyrosinase protein expression. Thus, the regulation of tyrosinase and its activity is critical. Activities of phenylalanine hydroxylase increase linearly with inherited skin color, i.e. activities up to eight-fold greater in black skin compared to white skin (Schallreuter et al., 2004).


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Melanogenesis is also regulated by hormones, neurotransmitters, cytokines, growth factors, eicosanoids, cyclic nucleotides, nutrients, and the physicochemical milieu (Slominski et al., 2004). These factors act sequentially and redundantly (i.e. in parallel). The cognate receptor of α-MSH is MC1R, and despite its very low level of receptor density on human melanocytes (Jackson et al., 2007) appears to be an important positive regulator of hair pigmentation (Rees, 2000). This receptor is activated upon binding with POMC-derived ACTH, α-MSH, and β-MSH peptides. The resultant signal transduction cascade activates adenylate cyclase, leading to subsequent cAMP production, and gene transcription for increased melanocyte proliferation, melanogenesis, and dendrite formation. However, while the injection of α-MSH into human skin increases epidermal melanogenesis (particularly of sun-exposed skin), no effect has been reported in hair follicles (Levine et al., 1991).

However, not all pigmentation researchers agree with the totality of the current MC1R-centric view of the control of pigmentation. This author would encourage caution as it is likely that such an important functionality as pigmentation (protection against harmful UVR effects) will have back-up systems or redundancy. In this regard, we have examined a POMC/MC1R complementary system – the β-endorphin/µ-opiate receptor system, and have shown that this pathway participates in the regulation of both human epidermal and follicular melanocyte biology at least in vitro (Tobin, 2005; Kauser et al., 2003; Kauser et al., 2004). We have also examined the role of the most proximal element of the hypothalamic–pituitary–adrenal axis (our body’s central response mode to stress), i.e. corticotropin-releasing factor (CRF), in the regulation of follicular melanogenesis. Like POMC, CRF is produced locally in the human skin (Slominski et al., 1995) and can modify the phenotype of human hair follicle melanocytes in vitro by upregulating cell dendricity and pigmentation levels (Kauser et al., 2006).

15.4 Gray hair and age

We are born with what appears to be a full complement of approximately fi ve million hair follicles, about 100 000 of which are found on the scalp. Still there is signifi cant plasticity in hair follicles we do have, such that a single human hair follicle is capable of producing several different types of hair fi ber during its lifetime. These include fi ne pigmented lanugo hair during fetal life, short (mostly unpigmented) vellus hair or fi ne pigmented intermediate hair in the prepubescent, and long thick terminal hair shafts in the adult. Moreover, hair growth rates also vary during human aging. When averaged for various sites and for post 40-year-old non-balding individuals, remarkably hair grows most rapidly and with greater individual fi ber thickness in individuals during the 50–70 decades (Pelfi ni et al., 1969)

Hair color also shows striking age-related changes, particularly in those individuals of Eurasian origin. There is often a switch from fair-colored


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‘intermediate hair’ to more deeply-pigmented coarser ‘terminal hair’ during puberty. Indeed, hair color in children usually darkens with advancing age (Allende, 1972) and it is not unusual for a blonde child to be dark-haired after puberty. Similarly, the phenomenon of heterochromia also becomes more apparent with age and is often strikingly apparent for scalp and beard, but may also affect the scalp alone (Lee et al., 1996).

However, the continuing extension of human longevity focuses increasing interest in elucidating the mechanisms of skin and hair aging, whether these are contributed from intrinsic (e.g. genetics, evolutionary selective pressures) or extrinsic (e.g. sun exposure, environmental insults and stress) factors. Chronologic skin aging in humans is associated with a 10–20% reduction in pigment-producing epidermal melanocytes (whether in exposed or unexposed skin), for every decade after 30 years of age (Quevedo et al., 1969; Whiteman et al., 1999). Nevertheless, it appears that epidermal melanocytes are relatively long-living cells, protected in part from reactive oxygen species (ROS), including those generated during melanogenesis.

The accumulation of oxidative damage is an important determinant in the rate of aging, though it is unclear whether it is the primary cause of aging (Harman, 1956; Gutteridge & Halliwell, 2000). Reactive oxygen species (ROS) damage DNA (both nuclear and mitochondrial) that can lead to an accumulation of mutations, can induce oxidative stress, and thereby trigger antioxidant mechanisms. It is likely that the antioxidant systems within the hair follicle melanocyte become impaired with age, leading to uncontrolled damage to the melanocyte itself including from its own melanogenesis-related oxidative stress (Kauser et al., 2007). Recent work suggests that the follicular–melanin unit of graying hair is indeed associated with increased melanocyte apoptosis and oxidative stress (Arck et al., 2006). This study also reported that the ‘common’ deletion in mitochondrial DNA (associated with oxidative stress) occurred more prominently in graying compared to normally pigmented hair follicles. Graying hair follicles in this study were also less well equipped to handle an exogenous oxidative stress, which is likely to be the result of impaired antioxidantmechanisms.

Melanin synthesis, by its very nature, is a rather toxic business. Melanogenesis also produces mutagenic intermediates, and so the induction of replicative senescence in melanogenic hair bulb melanocytes may be an important self-protective mechanism against cell transformation. Even the casual observer of cutaneous melanocyte phenotypes will be impressed by the very high melanin load and phenomenal synthetic capacity for melanin production of bulbar melanocytes, compared to that of their epidermal cousins. Indeed, a relatively small number of melanocytes (<100 cells per scalp anagen hair follicle) can, in a single hair growth cycle, produce suffi cient melanin to intensely pigment up to 1.5 m of hair shaft. This prolonged melanogenesis is likely to generate large amounts of ROS via the oxidation of tyrosine and dopa to melanin (Hegedus, 2000). If not adequately removed, their accumulation may generate signifi cant


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oxidative stress in both the melanocyte itself and perhaps also in the highly proliferative anagen hair bulb epithelium. Given the potential risks for mutation under these circumstances it is perhaps best for melanogenic bulbar melanocytes to assume a post-mitotic, terminally differentiated ‘(pre)senescence’ status to prevent cell transformation.

Scalp hair bulb melanocytes are at their most active during our youth, when the follicular melanin unit is just a few hair growth cycles old, and also when they are most responsive to the full post-puberty hormonal stimulus. On average, an individual scalp hair follicle will experience fewer than 15 melanocyte seedings from the presumptive reservoir in the average fully ‘gray-free’ life span of 40 years for Caucasians (Keogh & Walsh, 1965). There is experimental evidence to suggest that the melanocyte stem cell reservoir in adult hair follicles may be rather limited.

The onset and progression of hair graying correlates closely with chronological aging and occurs to varying degrees in all individuals, regardless of gender or race. The age of onset is genetically controlled and inheritable. Thus, the average age for Caucasians is mid-30s; for Asians, late-30s; and for Africans, mid-40s. Indeed, hair is said to gray prematurely only if it occurs before the age of 20 years in whites, before 25 years in Asians, and before 30 years in Africans. Although not formally tested, a good rule of thumb is that by 50 years, 50% of people have 50% gray hair (Tobin & Paus, 2001). However, what our eyes see when we look at the hair fi ber is a complex interplay of many physical characteristics, not just color. Also important here are the hair fi ber’s geometry, consisting of curvature, and its shine and luster. The darker the hair the more noticeable early graying will be. Conversely, graying can be more extensive in dark hair before total whitening is apparent; the reverse is true for blonde hair. Scalp hair graying fi rst appears usually at the temples, and spreads to the vertex and then the remainder of the scalp, affecting the occiput last. Beard and body hair is usually affected later.

It is likely that hair bulb melanocytes of the follicular melanin unit also infl uence their pre-cortical keratinocyte neighbors in several ways (and vice versa) (Tobin, 2003, 2008, 2009). For example, melanin transfer to keratinocytes appears to reduce the latter’s proliferative potential and rather may stimulate their terminal differentiation. This melanin-associated ‘brake’ on keratinocyte proliferation or its modulation of keratinocyte differentiation appears to be lifted somewhat in graying or white hair follicles. Indeed, white beard hair appears to grow faster than do adjacent pigmented hair in vivo (Nagl, 1995). Moreover, unpigmented hair follicles exhibit a higher rate of hair fi ber elongation in vitro than do matched pigmented hair follicles (Arck et al., 2006). If this interpretation is correct, then melanin granules can be viewed as ‘regulatory packages’ (Slominski et al., 1993), e.g. by providing a buffer for calcium. Furthermore, the saturation binding of transition metals (e.g. iron, copper) to melanin is also likely to infl uence the antioxidant defense for the melanosome-receiving keratinocyte (Kauser et al., 2007).


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There is evidence of melanocyte–keratinocyte interactivity in the hair fi ber from a clinical perspective also. Graying hair in its early stages is often coarser, wirier, and more unmanageable compared to pigmented hair, which would again refl ect a change in the chemical and physical properties of the post-pigmented hair fi ber (Van Neste & Tobin, 2004). Additionally, gray hair is often unable to hold a set and is more resistant to incorporating artifi cial color. These changes have signifi cant implications for the cosmetics industry. On the basis of the above fi ndings it appears that graying hair follicles may reprogram their matrix keratinocytes to increase the production of medullary, rather than pre-cortical, keratinocytes.

15.5 Effect of environment

The hair fi ber is constructed as a highly integrated system of several components including in order of decreasing amount; keratins, water, lipids, pigment, and trace elements. Despite the signifi cant variability in hair form between humans of different ethnicities, e.g. with regard to environment, diet, and hair texture, the chemical composition of hair protein across the ethnic groups is remarkably uniform (see Tobin, 2005). Thus, there are no signifi cant differences in amino acid composition of hair of different ethnicities.

Extreme trauma, protracted mechanical stress, and acute/chronic ultraviolet light exposure can result in damage to the normal hair shaft and these need to be distinguished from hair shaft abnormalities due to intrinsic defects, either genetic or nutritional. The location of the damage along, say, a one meter-long length of hair is also highly relevant, as the ‘oldest’ fractions (i.e. distal sections towards and at the tip) will have been ‘weathered’ by repeated washings, cosmetic treatments, brushings, and other manipulations that cause splitting and cuticular damage. Conversely, observing these features in short hair lengths suggests either the presence of more traumatic damage, e.g. aggressive grooming or cosmetic treatments or an underlying nutritional defi ciency or intrinsic defect. Another source of structural damage to normal hair can be due to extreme heat stress. Cosmetic practices, for example those associated with straightening of African hair, may induce so-called ‘bubble’ hair where extreme heat induces bubble formation under the cuticle within the hair cortex.

Hair pigments, hair proteins and hair lipids can also be degraded by visible and ultraviolet radiation (UVR). Indeed, lipids making up the membrane complexes holding the hair cortex together may be even more sensitive to light radiation than UVR (Hoting & Zimmermann, 1997). This may be particularly severe if the hair has already been pre-exposed to chemical bleaching (see below). Sunlight affects the amino acids of the hair fi ber’s cuticular covering more so than its inner bulk cortex, given their greater exposure. Hair proteins appear to absorb light principally between 254 and 350 nm and so most light degradation may occur between these wavelengths (Arnaud, 1984). Interestingly, amino acids in darker hair may be more resistant to photodamage than paler shades. Chronic exposure to sunlight is


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likely to cause fi ber brittleness that if further subjected to even relatively mild chemical bleach conditions, i.e. aqueous alkalinity and peroxides, may result in marked loss of structural integrity. Ultrastructural assessment of sun-exposed hair fi bers revealed that damage occurs in the cuticle as well as damage to melanin pigments within the hair cortex (Braida et al., 1994). Recent research suggests that gray hair undergoes more severe UV damage and so may need more UV protection than naturally pigmented hair (Gao & Bedell, 2001).

15.6 Artifi cial hair coloring shades

Our increasing longevity ensures a continual upward trajectory in sales of all types of products used for hair care. Unsurprisingly, the growth in hair colorant use has been particularly marked, as the principal application of these dyes is to cover gray hair. Additionally, recent societal trends are seeing whole populations of young adult males, especially those in western societies, being brought into this mass ‘cosmetication’ process, alongside the traditional constituency of young women. These target groups seek to either add color to their hair (original or different) or to lighten/bleach their hair to generate a preferred, lighter, color or to permit subsequent addition of color(s) shades lighter than their natural color. Despite this increasing use of chemical hair colorant products, technological developments in this area have unfortunately failed to keep pace. Indeed, most of the technological advances have been with improvements in the safety profi les of the current formulations. However, there remains a strong consumer preference and drive for all things ‘natural’ and this, together with tightening regulation of chemical dyes, has begun to stimulate interest in pursuing the development of more natural hair coloring agents. In the subsequent paragraphs, I restrict my discussions to the former category of colorants/hair dyes including: bleaching agents, permanent dyes, semi-permanent dyes, temporary dyes, and metallic dyes as these have the widest application. Others including vegetable dyes will only be alluded to here.

15.6.1 Chemical bleaching

Hair can be bleached by both chemical and photochemical (i.e. sunlight, see above) oxidative mechanisms. Typically, hair is bleached by hydrogen peroxide-containing systems that also include an alkaline hair-lightener base, with additional persulfate salts to accelerate or boost the reaction. To adequately destroy the melanin, the hydrogen peroxide bleach may also degrade hair proteins due to the numerous oxidizable cysteine groups in the hair cortex and cuticle (Wolfram, 1970). Indeed, normal bleaching (across a few shades) may break as much as 25% of the fi bers disulfi de bonds, while bleaching from black to blonde may break as many as 50% of these bonds (Robbins & Kelly, 1969). The high pH values of currently used hair aqueous alkaline bleaches (pH 9–11) are also likely to increase


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the rates of hydrolysis of resultant oxides to compete with that of oxidation, such that cleavage of cysteine occurs via a S-S fi ssion route to yield principally sulfonic acid. Moreover, hydrolysis of peptide bonds within hair protein (cystine, methionine, tyrosine, histidine and lysine) may also occur during severe bleaching episodes using higher concentrations of bleach and for longer exposure times (Robbins, 2002).

Oxidation of hair that lacks melanin occurs much more slowly than does melanized hair fi bers (Wolfram & Hall, 1975; Wolfram & Albrecht, 1987), suggesting that peroxide reacts preferentially with melanin pigment compared to hair proteins, but only after peroxide gains access to the core of the fi ber, i.e. after the pigment-free cuticle is breached. While isolated pigment granules are resistant to a large range of reagents, they are readily dissolved by alkaline hydrogen peroxide at pH 11.75 (Wolfram & Hall, 1975; Wolfram & Albrecht, 1987).

15.6.2 Permanent hair colorants

The permanent hair colorants or oxidative dyes represent approximately 80% of the hair color market (Anderson, 2000) and consist principally of uncolored precursors of p-amines and p-aminophenols that diffuse into the hair fi ber where they condense with dye couplers (e.g. resorcinol) and are then oxidized into active intermediates (e.g. diiminium or quinonimunium species) by (most commonly) hydrogen peroxide. Other secondary compounds are involved including surfactants, preservatives, pH-adjusting additives (to pH 8–10), etc. This chemistry is designed to provide for shampoo-fast hair color and a depth of color that can be controlled by adjusting relative amounts of the three principal agents. Most dyes are diffusion-controlled, so that ‘ring-dyeing’ is achieved, i.e. whereby the dye is restricted to the surface of the hair fi ber. However, this is variably achieved, as dye will penetrate more deeply into some hair fi bers than others. Somewhat surprisingly, this variability does not always correlate with fi ber caliber.

Permanent dyes have a very complex chemistry. Briefl y, the dye precursor (e.g. p-phenylenediamine) is oxidized to its diiminium ion and this active intermediate condenses with an electron donating coupler (e.g. resorcinol) to form a dinuclear product. This is then oxidized to an indo dye. For a more detailed discussion of associated chemistry the interested reader is directed to an excellent and comprehensive treatment of this subject by Robbins (2002). Permanent dyes are formulated for use as a two-part modality, consisting of the precursor-coupler base (with secondary agents for texture, etc., as described above) and the oxidizing agent (with associated secondary products for stabilization, etc.). Strand testing allows for inter-individual variability to be minimized. A signifi cant potential concern with use of permanent dyes is induction of an allergic reactive in sensitive individuals, and therefore testing is recommended prior to full application of these


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products. Brown to black shaded compounds are formed when dye precursors are oxidized in the absence of couplers, while couplers can modify the color formed by the precursor. Highly colored indo dyes can fade, possibly due to the addition of aromatic moieties to the dinuclear indo dyes.

15.6.3 Semi-permanent hair colorants

If consumers prefer not to have a permanent color change to their hair they can choose to change their hair color in a non-permanent way, i.e. color that can be removed after about 4–6 shampoos (Corbett, 1973). These dyes do not involve the use of hydrogen peroxide, and usually contain multiple dyes in combination (Brown, 1982). As with permanent dye, several secondary compounds are also included in these dyes, e.g. surfactants, pH adjusters, etc. These hair dyes consist of highly polar agents including neutral aromatic amines, nitro aromatic amines, or antraquinone derivatives (Brown, 1982). Rinse-out rates of these dyes correlate with size of the dye molecules used, but also depends on the region of the hair fi ber. For example, dye is removed most readily from weathered hair fi ber tips than the scalp end and so accommodation needs to be built into the formulations to ensure evenness of tones. Furthermore, some dyes, especially small mononuclear dye molecules, may be washed out more readily from previously bleached hair. As with permanent dyes, ring-dyeing is also observed with this group of hair colorants, consistent with diffusion-controlled reactions.

15.6.4 Temporary hair colorants/rinses

Another option is to use hair colorants that can be washed out of the hair with a single shampoo. These contain several (as many as fi ve) color ingredients (e.g. D&C brown No.1, Acid violet 43, etc.) to achieve the desired shade, and can be applied directly to the hair or sprayed onto the hair. More dye can be applied if a more intense color is sought and less dye if only tint addition to gray hair is desired. The dye molecules used here tend to be larger than those used for semi-permanent colorants and are chosen for their maximum water solubility and minimum penetration, thereby facilitating easy rinse out (Wall, 1957).

15.6.5 Metallic hair dyes

Historically, salts of metals like silver, bismuth, cobalt, copper, iron, mercury and lead have been used to dye hair. However, only lead salts (e.g. lead acetate with sulfur) are still in use in some jurisdictions. These are often chosen by men with graying hair as the darkening of the hair shaft occurs very gradually, mostly likely via the formation of lead-sulfur complexes in the periphery of the hair shaft. A caveat here is that these complexes may be unstable, especially when exposed to other chemicals and dyes.


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15.6.6 Vegetable hair dyes

While the classical literature contains several ‘colorful’ references to the use of vegetable hair colorants, only henna and chamomile are still used to any signifi cant commercial extent, although several patents based on this technology have been fi led by major hair colorant companies. Henna (lawsone) is found in the leaves of the Egyptian privet plant and contains 2-hydroxy-1,4-naphthoquinone. This compound can add red (ionized form) and yellow (non-ionized form) shades to protein in hair and nails when applied in acidic media. Deeper henna shades are possible at high concentrations in alkaline media, while acidic pHs are more suitable for hair dyeing. The pH of these may control the chemical bonding of the lawsone to the hair fi ber, but levels of color fastness are generally relatively low. This can be improved if lawsone is mixed with p-phenylenediamine, though this does not come without some adverse reactions (Le Coz et al., 2000). Chamomile fl owers contain an active coloring polyhydroxy fl avone called 4´,5,7-trihydroxy fl avone that has been used for dyeing of cloth as well as hair.

15.6.7 ‘Natural’ hair colorants

The hair colorant industry is currently under signifi cant pressure to develop economical, natural hair dyes. Although not yet commercialized, there may be some scope for using DOPA (3,4 dihydroxyphenylalanine), which after oxidation provides a natural brown dye. In the presence of cysteine, natural red pigments (pheomelanins) can be formed from DOPA, while the presence of sulfur-containing nucelophile (rather than cysteine) can increase still further the range of hair color shades possible. Here, hydrogen peroxide is a superior oxidizer, though the reaction can proceed even with basic atmospheric oxygen. Brown colors can be deepened to intense blacks if potassium ferricyanide is added.

15.7 Color measurement methods and instruments

Hair ‘color’ needs some defi nition in order to be a useful parameter for study and for accurate comparison of studies. This is especially true at the margins of color categories, e.g. is ‘dirty’ blonde rather a brown? Moreover, not all hair strands in a head of hair are identical in color; indeed different regions of the scalp can have hair of quite different shades. For example, hair at the back of the head is commonly of deeper pigmentation than the crown of the vertex. Thus, a representative assessment of the entire scalp is needed to generate a truly accurate determination. In this context hair color can be perceived as varying along a continuum, rather than occupying discrete color spaces.

The rods and cones of the human eye are sensitive to changes in both lightness (mostly rods) and color (cones) (Hunt, 1998). The technique that has been most widely used, including for biomedical and cosmetic sciences, is refl ective


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spectrophotometry. This methodology is widely viewed as truly an objective measure for assessing skin and hair skin pigmentation (Shriver and Parra, 2000; Parra et al., 2007; Naysmith et al., 2004). An objective description of color requires a model of the so-called color space. Among the several models existing to measure color is one developed by the Commission Internationale de l’Eclairage, CIELAB or CIE L*a*b*, which measures color on three axes broadly linear with human perception (Ford and Roberts, 1998). Another benefi t of this model is that it provides a grid point for each specifi c color (TASI, 2004) and in addition, has been used successfully in human pigmentation studies (Shriver and Parra, 2000; Takahashi and Nakamura, 2004; Parra, 2007). Here color intensity is measured on the ‘L*’ axis from a value of 0 (black) to 100 (white), while color itself is measured on the ‘a*’ axis from a value of -100 (green) to +100 (red) and on the ‘b*’ axis from –100 (blue) to +100 (yellow). The smallest difference the human eye can detect is equated to one unit on the L*, a*, or b*axes (TASI, 2004). Together, this color grid allows for a quantitative comparison of color, though is limited ultimately by the operational limits of the instruments used. The CIE L*a*b* system has been used together with refl ective spectrophotometry to measure hair color where color intensity (‘L*’) was found to correlate well with the Melanin Index, especially in those of non-European ancestry (Shriver & Parra, 2000). Others have found a good correlation of hair color as measured with b* and the MC1R gene variant expression (Naysmith et al., 2004).

A study by Vaughn and colleagues (2008) reported that, when given a limited choice of colors, self-reported and observer-reported hair color determination was accurate to 85.7%, with one shade (lighter and darker) discrepancies found in approximately 14% of cases where the observer mostly graded the hair color as darker than the self-reporter. However, this is often not useful for a fully quantitative assessment of hair color. Indeed, there can be poor separation of hair color categorization by observers and the clustering analysis via refl ective spectrophotometry using the CIE L*a*b* system – further emphasizing the importance of using an objective measurement. Vaughn and co-workers also found that the b* component (yellow) of the CIE L*a*b* color space provided the most discriminating information for hair color grouping (which concurred with the observed strongest correlation between b* and the MC1R genotype (Naysmith et al., 2004), suggesting a multi-component approach needs to be adopted. So-called trichromatic vision (though in reality more than three types of color receptor cones) leads to signifi cant similarities in the reporting of color by observers and by the spectrophotometry with clustering method. However, the latter is more sensitive in one or all three of the color axes than biologic discriminations (limited to one unit) and so can provide more discriminatory power.

Recently Vaughn and co-workers (2009) conducted a comparison of the more cumbersome refl ective spectrophotometry and digital image analysis for measurement of hair color. While refl ective spectrophotometry has been used mostly at the macroscopic scale, forensic scientists have been using digital image


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analysis to measure hair color of single hair fi bers at the microscope scale (Bednarek, 2004). Digital images, measured and displayed on the computer screen in the Red-Green-Blue (RGB) colour space need to be converted into the colour space required (CIE L*a*b* in this case) using standard mathematical algorithms (Herbin et al., 1990). The results of this study showed however that digital image analysis, while more convenient than refl ective spectrophotometry, is inferior to it and so has very limited potential for high resolution studies of color measurement. In summary, objectively defi ned and measured colors, rather than observer or self-reported colors, are necessary to classify individuals for further study and at this time, this is best achieved through refl ective spectrophotometric measurement.

15.8 Future trends

The future growth in hair color measurement will be led principally by the hair colorant industry and the forensic sector of both archeological and police investigations. There will be increasing interest in the characterization of melanin variability (i.e. eumelanins, pheomelanins and mixed melanins), which is a current source of signifi cant challenge. Furthermore, there will be parallel and mind-boggling advances in imaging technology and a greater appreciation of the unique growth dynamics of hair. These aspects are currently leading to a resurgence of interest in hair analysis. Within this hair color analysis and pigment pattern recognition will be hugely important.


In addition to the bibliography for specifi c reading, perhaps the most impressive single source for all things pigmentation in terms of biology/physiology is the current edition of The Pigmentary System by Nordlund JJ, Boissy R, Hearing VJ, King R, Oetting W, Ortonne J-P (eds), Blackwell Publishing, 2006.

15.10 References

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Bednarek J (2004), ‘An attempt to establish objective criteria for morphological examinations of hairs using the image analysis system’, Probl Forensic Sci LVI, 65–77.

Boissy RE, Sakai C, Zhao H, Kobayashi T, Hearing VJ (1998), ‘Human tyrosinase related protein-1 (TRP-1) does not function as a DHICA oxidase activity in contrast to murine TRP-1’, Exp Dermatol 4, 198–204.


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Botchkareva NV, Khlgatian M, Longley BJ, Botchkarev VA, Gilchrest BA (2001), ‘SCF/c-KIT signaling is required for cyclic regeneration of the hair pigmentation unit’, FASEB J 15, 645–658.

Braida D, Dubief C, Lang G (1994), ‘Photoageing of hair fi ber and photoprotection’, Skin Pharmacol 7(1–2), 73–77.

Brown K (1982), ‘Hair colorants’, J Soc Cosmet Chem 33, 375–383.Commo S, Bernard BA (2000), ‘Melanocyte subpopulation turnover during the human hair

cycle: an immunohistochemical study’, Pigment Cell Res 13, 253–259.Commo S, Gaillard O, Thibaut S, Bernard BA (2004), ‘Absence of TRP-2 in melanogenic

melanocytes of human hair’, Pigment Cell Res 17, 488–497.Corbett J (1973), ‘The role of meta difunctional benzene derivatives in oxidative hair

dyeing. I. Reactions with p-diamines’, Cosmet Toiletries 24, 103–134.Dupin E, Le Douarin NM (2003), ‘Development of melanocyte precursors from the

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intermediates and the reactive oxygen side-products in human diseases and aging’, Toxicology 145, 85–101.

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Holbrook KA, Underwood RA, Vogel AM, Gown AM, Kimball H (1989), ‘The appearance, density and distribution of melanocytes in human embryonic and fetal skin revealed by the anti-melanoma monoclonal antibody, HMB-45’, Anal Embryol (Berl), 180(5), 443–455.

Horikawa T, Norris DA, Johnson TW, Zekman T, Dunscomb N, Bennion SD, Jackson RL, Morelli JG (1996), ‘DOPA-negative melanocytes in the outer root sheath of human hair follicles express premelanosomal antigens but not a melanosomal antigen or the melanosome-associated glycoproteins tyrosinase, TRP-1, and TRP-2’, J Invest Dermatol 106, 28–35.

Hoting E, Zimmermann M (1997), ‘Sunlight-induced modifi cations in bleached, permed, or dyed human hair’, J Soc Cosmet Chem 48 (2), 79–91.

Hunt RWG (1998), Measuring Colour, Kingston-upon-Thames, Fountain Press.Jackson IJ, Budd PS, Keighren M, McKie L (2007), ‘Humanized MC1R transgenic mice

reveal human specifi c receptor function’, Hum Mol Genet 16(19), 2341–2349.Kauser S, Schallreuter KU, Thody AJ, Gummer C, Tobin DJ (2003), ‘Regulation of

human epidermal melanocyte biology by beta-endorphin’, J Invest Dermatol 120, 1073–1080.

Kauser S, Schallreuter KU, Thody AJ, Gummer CL, Tobin DJ (2004), ‘Beta-endorphin as a regulator of human hair follicle melanocyte biology’, J Invest Dermatol 123, 184–195.

Kauser S, Slominski A, Wei ET, Tobin DJ (2006), ‘Modulation of the human hair follicle pigmentary unit by corticotropin-releasing hormone and urocortin peptides’, FASEB J 20(7), 882–895.


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Kauser S, Westgate G, Green M, Tobin DJ (2007), ‘Age-associated down-regulation of catalase in human scalp hair follicle melanocytes’, Pigment Cell Res 20(5), 432.

Keogh EV, Walsh RJ (1965), ‘Rate of graying of human hair’, Nature 207, 877–878.Lalueza-Fox C, Rompler H, Caramelli D, Staubert C, Catalano G, Hughes D, Rohland N,

Pilli E, Longo L, Condemi S, de la Rasilla M, Fortea J, Rosas A, Stoneking M, Schoneber T, Bertranpetit J, Hofreiter M (2007), ‘A melanocortin 1 receptor allele suggests varying pigmentation among Neanderthals’, Science 318, 1453–1455.

Le Coz CJ, Lefebvre C, Keller F, Grosshans E (2000), ‘Allergic contact dermatitis caused by skin painting (pseudo-tattooing) with black henna, a mixture of henna and p-phenylenediamine and its derivatives’, Arch Dermatol. 136(12), 1515–1517.

Lee WS, Lee IW, Ahn SK (1996), ‘Diffuse heterochromia of scalp hair’, J Am Acad Dermatol 35, 823–825.

Levine N, Sheftel SN, Eytan T, Dorr RT, Hadley ME, Weinrach JC, Ertl GA, Toth K, McGee DL, Hruby VJ (1991), ‘Induction of skin tanning by subcutaneous administration of a potent synthetic melanotropin’, JAMA 266, 2730–2736.

Morgan E (1985), The Ascent of Woman, London, Souvenir Press.Nagl W (1995), ‘Different growth rates of pigmented and white hair in the beard:

differentiation vs. proliferation?’, Br J Dermatol, 132, 94–97.Nakamura M, Tobin DJ, Richards-Smith B, Sundberg JP, Paus R (2002), ‘Mutant

laboratory mice with abnormalities in pigmentation: annotated tables’, J Dermatol Sci 28, 1–33.

Naysmith L, Waterson K, Ha T, Flanagan N, Bisset Y, Ray A, Wakamatsu K, Ito S, Rees JL (2004), ‘Quantitative measures of the effect of the melanocortin 1 receptor on human pigmentary status’, J Invest Dermatol 122, 423–428.

Nishimura EK, Jordan SA, Oshima H, Yoshida H, Osawa M, Moriyama M, Jackson IJ, Barrandon Y, Miyachi Y, Nishikawa S (2002), ‘Dominant role of the niche in melanocyte stem-cell fate determination’, Nature 416, 854–860.

Nordlund JJ, Ortonne J-P (2006), ‘The normal color of human skin’, in Nordlund JJ, Boissy RE, Hearing VJ, King RA, Oetting WS, Ortonne J-P, The Pigmentary System: Physiology and Pathophysiology, New York, Blackwell, 504–520.

Parra EJ (2007), ‘Human pigmentation variation: evolution, genetic basis, and implications for public health’, Yearbk Phys Anthropol 50, 85–105.

Pelfi ni C, Cerimele D, Pisanu G (1969), ‘Aging of the skin and hair growth in man’, in Montagna W, Dobson RL, Advances in Biology of the Skin – Hair Growth, New York, Pergamon Press, 153–160.

Peters EM, Tobin DJ, Botchkareva N, Maurer M, Paus R (2002), ‘Migration of melanoblasts into the developing murine hair follicle is accompanied by transient c-Kit expression’, J Histochem Cytochem 50(6), 751–766.

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Rees JL (2006), ‘Plenty new under the sun’, J Invest Dermatol 126, 1691–1692.Robbins CR (2002), Chemical and Physical Behavior of Human Hair, New York, Springer-

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Cosmet Chem 20, 555–564.


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Schallreuter KU, Beazley WD, Hibberts NA, Tobin DJ, Paus R, Wood JM (1998), ‘Pterins in human hair follicle cells and in the synchronized murine hair cycle’, J Invest Dermatol 111, 545–550.

Schallreuter KU, Wazir U, Kothari S, Gibbons NC, Moore J, Wood JM (2004), ‘Human phenylalanine hydroxylase is activated by H2O2: a novel mechanism for increasing the L-tyrosine supply for melanogenesis in melanocytes’, Biochem Biophys Res Commun 322(1), 88–92.

Shriver MD, Parra EJ (2000), ‘Comparison of narrow-band refl ectance spectroscopy and tristimulus colorimetry for measurements of skin and hair color in persons of different biological ancestry’, Am J Phys Anthropol 112, 17–27.

Slominski A, Paus R, Schadendorf D (1993), ‘Melanocytes as “sensory” and regulatory cells in the epidermis’, J Theor Biol 164, 103–120.

Slominski A, Paus R, Plonka P, Schallreuter KU, Paus R, Tobin DJ (1994), ‘Melanogenesis during the anagen-catagen-telogen transformation of the murine hair cycle’, J Invest Dermatol 102, 862–869.

Slominski A, Ermak G, Hwang J, Chakraborty A, Mazurkiewicz JE, Mihm M (1995), ‘Proopiomelanocortin, corticotropin releasing hormone and corticotropin releasing hormone receptor genes are expressed in human skin’, FEBS Lett 374, 113–116.

Slominski A, Tobin DJ, Shibahara S, Wortsman J (2004), ‘Melanin pigmentation in mammalian skin and its hormonal regulation’, Physiol Rev 84, 1155–1228.

Steingrimsson E, Copeland NG, Jenkins NA (2006), ‘Mouse coat color mutations: from fancy mice to functional genomics’, Dev Dyn 235(9), 2401–2411.

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murine hair follicle’, J Ultrastructural Res 67, 40–54.Takahashi T, Nakamura K (2004), ‘A study of the photolightening mechanism of blond hair

with visible and ultraviolet light’, J Cosmet Sci 55, 291–305.TASI (2004), ‘Colour theory: understanding and modeling colour’, Technical Advisory

Service for Images, Bristol, UK, University of Bristol.Tobin DJ (2003) ‘The ageing hair follicle pigmentary unit’, in Van Neste D, Hair Science

and Technology, Tournai, Belgium, Skinterface srl, 155–168.Tobin DJ (2005) ‘Pigmentation of human hair’, in Tobin DJ, Hair in Toxicology – An

Important Biomonitor, Cambridge, The Royal Society of Chemistry, 57–88.Tobin DJ (2005), ‘The human hair fi ber’, in Tobin DJ, Hair in Toxicology – An Important

Biomonitor, Cambridge, The Royal Society of Chemistry, 57–88.Tobin DJ (2008), ‘Human hair pigmentation – biological aspects’, Int J Cosmet Sci, 30(4),

233–257.Tobin DJ (2009), ‘Biology of hair follicle pigmentation’, in Blume-Peytavi U, Tosti A,

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apoptosis during hair follicle regression (catagen)?’, J Invest Dermatol 111, 941–947.Tobin DJ, Paus R (2001), ‘Graying: gerontobiology of the hair follicle pigmentary unit’,

Exp Gerontol, 36(1), 29–54.Tobin DJ, Slominski A, Botchkarev V, Paus R (1999), ‘The fate of hair follicle melanocytes

during the hair growth cycle’, J Invest Dermatol Symp Proc 4, 323–332.Van Neste D, Tobin DJ (2004), ‘Hair cycle and dynamic interactions and changes associated

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Whiteman DC, Parsons PG, Green AC (1999), ‘Determinants of melanocyte density in adult human skin’, Arch Dermatol Res 291, 511–516.

Wolfram LJ, Albrecht L (1987), ‘Chemical-bleaching and photobleaching of brown and red hair’, J Soc Cosmet Chem, 38, 179–191.

Wolfram LJ, Hall K (1975), ‘Photodegradation of human hair’, J Soc Cosmet Chem 26, 247.

Wolfram LJ (1970), ‘Chemical bleaching’, J Soc Cosmet Chem 19, 675.Wood JM, Jimbow K, Boissy RE, Slominski A, Plonka PM, Slawinski J, Wortsman J, Tosk

J (1999), ‘What’s the use of generating melanin?’, Exp Dermatol 8, 153–164.

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393

Index

AATCC, 108, 116AATCC 6, 204AATCC 8, 204AATCC 15, 204AATCC 16, 205AATCC 23, 204AATCC 61, 206AATCC 101, 203AATCC 104, 204AATCC 106, 204AATCC 107, 204AATCC 109, 204AATCC 115, 204AATCC 116, 204AATCC 125, 206AATCC 129, 204AATCC 132, 206AATCC 133, 207AATCC 157, 204AATCC 162, 204AATCC 164, 204AATCC 172, 206AATCC 173, 212AATCC 181, 205AATCC 188, 206AATCC 190, 206AATCC EP3, 210AATCC EP8, 210AATCC EP 1, 208AATCC EP 2, 208AATCC EP 8, 204AATCC Fading Units, 205AATCC Test Method 110, 289AATCC Test Method 173–1991, 171abrasion, 358absolute accuracy, 238achromaticity, 53–4Acoat system, 51active pixel sensor see CMOSAdams Nickerson space, 281adaptation, 316–17Adaptive Neuro Fuzzy Inference

System, 137additive colour mixing, 196amelogenesis imperfecta, 348American Upland cotton, 254analogical photosensor see optoelectronic

sensorANLAB, 82

antecedent, 273anterior chamber, 4aqueous, 4Artifi cial Daylight D65 Lamp, 334, 336artifi cial intelligence, 135artifi cial neural networks, 34, 125–44

application to colour measurement, 132–5

architecture, 127–8artifi cial neuron, 128network layers, 128network types, 128schematic diagram, 127

case studies, 140–1actual and predicted L*, a* and b*

values, 143spectral refl ectance curves, 142

evaluation method, 140actual and predicted dyeing time, 140actual and predicted L* a* and b*

values, 141feed-forward neural networks, 130future trends, 141–4history, 126–7learning process, 129–30

illustration, 130reinforcement, 129supervised, 129, 130unsupervised, 129

recipe prediction, 135–9dyeing time prediction, 139

theory, 135training using back propagation

algorithm, 130–2sigmoid activation function, 133training phases, 131–2transfer function, 132

artifi cial neurons, 128ASTM 1925, 289ASTM (2003), 93ASTM (2005), 103, 108, 110ASTM D 1729, 223ASTM D 1925, 289ASTM D E313–73, 289ASTM Designation E313–73, 288ASTM E308:08, 78ASTM E313, 289ATD models, 7–10atmospheric contaminants, 204

394 Index

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back propagation algorithm, 129, 131Bayesian regularisation technique, 134BCRA tile data, 299BCRA tile test, 237Beer’s law, 136Benevue Colour Management System, 301, 306Berger formulae, 102, 104Boynton model, 8, 9, 13brightness, 323BS 6923, 234bubble hair, 382BYK–Gardner Colour View, 193

calibration, 237camera-based colour measurement, 147–63

case studies, 158–62multi-spectral capture system, 160–2spatial-chromatic dithering, 158–60

CCD and CMOS application fi elds, 148future trends, 162–3principles, 149–51

imaging capture process, 150procedures, 151–4

monochromatic based experimental set-up, 152

strengths and weaknesses, 154–8characterisation models, 154luminance adaptation model, 155multi-spectral capture systems, 155–7spectral vs colorimetric imaging, 157–8

cameras, 159CCC see Clemson colour clusteringCCD, 147–8centroid calculation, 275chemical bleaching, 383–4Chevreul colour order system, 49–50choroid coat, 4chroma, 53, 71, 84, 323Chroma Cosmos 5000, 30, 37Chromagen 707, 37chromatic adaptation, 317chromatic attributes, 223chromatic opponency theory, 7chromatic perception, 10–12

appearance of colour, 11–12apparent mix, 12Aubert-Abney effect, 12Bezold-Brücke effect, 11–12crispening effect, 12Helmholtz-Kohlrausch effect, 12physical and perceptual colour

descriptors, 11simultaneous contrast, 12

discrimination, 10–11chromatic stimulus, 6chromatic transference scale, 210chromatic vision, 6–10

models, 6–10ATD models, 7–10chromatic opponency theory, 7Smith and Pokorny fundamentals, 9trichromatic theory, 6–7

stimulus and perceived colour, 6chromaticity coordinates, 80chromaticity diagram, 80–1

illustration, 81chromaticness, 38

Cibacron blue TR-E, 136Cibacron brilliant red 4G-E, 136Cibacron yellow G-E, 136CIE 1931 colour space, 313, 314, 320CIE 1976 colour space, 225CIE 94 formula, 285, 325CIE 1964 supplementary standard observer, 76CIE 1976 uniform colour space, 281CIE 1931 XYZ, 149CIE colorimetric system, 23, 26CIE DE 2000 formula, 283, 285CIE illuminant D65, 288CIE Lab colour space, 224–5, 387CIE LCh colour space, 225CIE nomenclature, 349CIE Publication 15.2, 288CIE standard illuminant D65, 92CIE standard observer, 74–6

illustration, 74unreal primaries, 75–6

graphical representation of real and unreal primaries, 77

illustration of real and unreal primaries, 75CIE standardised light sources, 72–3CIE system, 72, 222

limitations, 82transformation and improvement, 82–6

chroma C calculation, 85CIEL*a*b* 1976 Colour Space, 84hue circle, 85

usefulness, 81–2CIE two degree standard observer, 76CIE whiteness formulae, 106–8, 117, 289

old and new tint limits in the CIELAB a* – b* diagram, 106–8

CIE-XYZ values, 154, 157, 160CIECAM97s, 134CIEDE2000, 168, 215, 232, 325CIELAB, 187, 191, 192, 347, 351, 356CIELAB 1976 formula, 283, 285CIELAB colour difference, 225CIELAB colour space, 320, 322–3CIELAB colour tolerancing, 282CIELAB equation, 171CIELUV colour space, 320classic Colour Checker, 151Clemson colour clustering, 174–7

modifi ed method, 180CM-512m3, 308CMC see Colour Measuring CommitteeCMC colour difference formula, 226, 232,

234, 283, 285CMCCAT2000, 317CMOS, 147–8colorant formulation, 295Colorant Mixture Computer, 296Colorcurve colour order system, 50colorimeters, 357colorimetric characterisation, 153colorimetric measurements, 155Colorimetry Committee of the Optical Society

of America, 89Coloroid system, 47–9colour attribute, 222Colour Checker charts, 151colour constancy, 15–17, 151, 316–17

adaptive mechanism, 15–16

Index 395

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chromatic adaptation and gain control mechanisms, 16–17

light deduction, 17colour control process, 160colour difference, 207, 224Colour-Difference Formula, 325colour difference unit, 293colour digital cameras, 160colour imaging technology, 277Colour iMatch Industrial software, 182colour matching, 295colour measurement

artifi cial neural networks, 125–44application, 132–5architecture of ANNs, 127–8basic principles, 126–7case studies, 140–1evaluation method, 140feed-forward neural network, 130future trends, 141–4learning process, 129–30recipe prediction, 135–40training using back propagation

algorithm, 130–2cotton grading, 253–77determining and improving accuracy, 184–94

absolute correction of instrumentally generated spectrometer values, 190

accuracy improvement, 190–1applications, 193–4defi nitions, 186–7determining uncertainty, 184uncertainty, 185–6

experimental modelling, 191–3distribution of specimens in CIELAB L* vs

a* plane, 192original uncorrected and corrected values

vs bin size, 194specimen location in CIE a*–b* colour

space, 192and fastness assessment, 196–216hair colour, 371–88methods for textiles, 221–51paint fi lms and coatings, 279–309principles and practice for food, 312–39uncertainty values in units of the value itself

single baby blue smooth glossy plastic plaque for Instrument 100, 189

single baby blue smooth glossy plastic plaque for Instrument 500, 189

single yellow smooth glossy plastic plaque for Instrument 100, 188


Colour Measuring Committee, 325colour merge value, 175colour mixing, 196colour notation systems

aim points database, 19device dependent, 19mathematical systems, 19

colour order systems, 19–65accuracy, 54application, 24–5classifi cation, 25–9

colorant mixture systems, 25–6colour appearance systems, 28–9

colour mixture systems, 26–8comparison and interrelation, 51–4

achromaticity representation, 53–4chroma/saturation representation, 53hue representation, 52–3

computer-based systems, 54–61digital colour atlases, 59–61HSV and HSL colour spaces, 58

future trends, 63–5merits-demerits, 29–30systematic arrangements, 22–3universal colour language, 61–2

universal colour names, centroid colours and hue angle boundaries, 63

various colour order systems, 31–51Coloroid system, 47–9DIN system, 43–4Munsell colour order system, 31–7natural colour system, 38–41OSA-UCS system, 45–7Ostwald system, 41–3other colour system, 49–51practical colour coordinate system, 37

Colour Science Association of Japan, 115colour shade sorting, 167–82

555 fi xed-grid shade sorting system, 168–74apparel colour specifi cation, 170boxes arrangement, 169packing of spheres, 173shade sorting effectiveness

indices, 174tolerance ellipsoid and position of

samples, 169truncated octahedron, 173

Clemson colour clustering, 174–7comparing two populations sorted by

CCC and 555 methods, 176demonstration for adoption by

industry, 177groups produced by various sorting

methods, 177process, 175

defi ned, 167K-means clustering, 178–80

clusters graphic presentation, 179numbers of clusters vs mean

distance, 179modifi ed CCC shade sorting method, 180shade sequencing and clustering, 180–2

linear tapering of blue dyed sample, 181colour space, 387colour specifi cations, 70–1, 224–6Colour Standards and Colour

Nomenclature, 26–7colour strength, 286–7

calculations using different methods, 287computer output, 287

colour tolerance, 232colour vision

theories and principles, 3–17advantages, 3chromatic perception, 10–12colour constancy, 15–17defective colour vision, 12–15human colour vision, 6–10human visual system, 3–6

396 Index

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colourfastnessassessment, 196–216

colour and colourfastness, 196–7standard test format, 201–2test method development, 199–200test standard setting organisations, 200–1use and usefulness of testing, 197–9

results assessment, 207–16AATCC chromatic transference

scale, 211AATCC grey scale for evaluating change in

colour change, 209AATCC grey scale for evaluating

staining, 210grey scale for staining step values, 214grey scales, 208–12instrumental colour measurement of test

data, 212–14use of digital camera-based

systems, 214–16specifi c tests, 203–7

consumer use, 203–6production processes, 203refurbishment, 206–7

colourfulness, 323colours, 222

numerical expression, 70–86, 222–4chromaticity coordinates and diagram,

80–1CIE standard light sources/illuminants,

72–3CIE Standard Observer and unreal

primaries, 74–6CIE system, 72CIE XYZ system, 81–2future trends, 87limitations of CIE system, 82refl ectance measurement, 79–80specifi cation, 70–1transformation and improvement of CIE

system, 82–6tristimulus values computation, 77–9

computer colour matching, 295–7computer tinting, 297, 309confusion point, 14consequent, 273contrast ratio, 290conventional colorimeters, 160cosmetication process, 383cotton colour grading

affecting factors, 255–9redness, 255–6redness contribution to cotton chroma, 257redness range for each colour category, 257refl ectance, yellowness and redness of

USDA physical standards, 256spots, 257–8trash particles, 258–9

American Upland cotton colour grades, 254colour image analysis, 259–63

colour measurement, 261–3identifi ed irregular regions, 261imaging colorimeter set-up, 259irregular regions identifi cation, 260–1L*C*h distributions, 261multi-dimension thresholding, 262refl ectance distribution, 263

refl ectance vs yellowness, 262colour measurement, 253–77CR-210 colorimeter measurement

infl uence of leaves, 259infl uence of spot, 258

disagreements between different grading methods, 276

fuzzy logic, 268–76cotton colour grades boundaries, 268–9defuzzifi cation, 274–5fuzzifi cation, 271–4fuzzy inference system, 269–70fuzzy rules, 274fuzzy sets and membership

functions, 270–1input fuzzy sets membership functions, 272output fuzzy sets triangular membership

functions, 272parameters for fuzzy sets of input

variables, 270weighted membership functions of output

fuzzy sets, 275weighted output functions aggregation, 275white and light spotted colours, 269

history, 253HVI colorimeter, 254–5irregular regions in cotton image, 260neural network, 263–8

colour grade disagreement distributions, 267

cotton colour grading results by NN classifi er, 266–8

disagreement in major categories, 263disagreement in subcategories, 264HVI–classer and NN–classer, 266MLP topology, 265multilayer perception, 264NN classifi er for cotton colour

grading, 265–6USDA cotton colour grades, 253–77

cotton fabric, 138Cotton Incorporated, 254CR-241, 299CR-210 colorimeter, 258

D illuminants, 73, 82De Valois-De Valois model, 9defective colour vision, 12–15

anomalies and defi ciencies, 12–14colour vision based on cones, 13

confusion lines and colour discrimination tests, 14–15

deuteranopic confusion lines, 15tritanopic subject, 14

defuzzifi cation, 274–5dental fl uorosis, 349dentine, 344desert island experiment, 22–3deuteranomaly, 14deuteranopia, 13, 15Dictionary of Colour, 27–8DigiEye, 158, 215digital Colour Checker Semi Gloss, 151digital colour imaging, 147digital image analysis, 387–8

techniques, 355–6digital photography, 151

Index 397

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digital trichromatic cameras, 158DIN 53160, 201DIN system, 43–4DOPA, 386drift test, 237dry fabrics, 247–8dye, 197Dynamic Linked Library, 190

error back-propagation algorithm, 266eumelanin, 378eumelanosomes, 378Eurocolour system, 51European Standards, 200European Standards Organisation, 200extrinsic tooth stain, 349–50

fastness colour difference, 213feed-forward neural networks, 127, 128,

130, 131feed-forward network, 127mathematical representation, 131

feedback architectures see interactivefeedback networks, 128fi bre, 238FIS see fuzzy inference system555 fi xed-grid shade sorting, 168–74fl at-fi eld correction, 152fl uorescence, 346fl uorescent lamps, 73fl uorescent whitening agents, 88, 289fl uoride, 358FMC-II formula, 285food colour measurement

absorption and scatter, 318–19appearance, 317–18CIE system, 319–20

colour matching functions, 320relative spectral power distributions, 321

concentrated and diluted orange juicechanges in lightness, opponent co-ordinates

and hue angle and chroma, 333K-M absorption and scatter coeffi cients for

tristimulus values, 332

refl ectance spectra, 331future trends, 337–9illuminant spectra and uniform colour, 336

CIELAB a*b* spacing, 338foods refl ectance spectra of differing

hue, 337infl uence of ambient light and food

structure, 316–17instrumentation, 325–7measurement in practice, 327–36

breakfast cereals, 335–6calculated changes in fresh beef spectra,

330coffee, 332–4fresh beef during oxymyoglobin

oxidation, 328fresh meat, 328–30orange juice, 330–1visual lightness to darkness relationship of

coffee and milk, 335wrapped fresh beef progressive changes in

colour, 329

principles and practice, 312–39trichromatic detection, 313–16

light detection in the eye, 315–16uniform colour space, 320–5

colour description systems and notation, 324

colour diagrams, 322four-dimensional Riemannian colour space, 21fuzzifi cation, 271–4fuzzy clustering, 134fuzzy inference system, 269–70

classifi ed white and light spotted colours distributions, 277

cotton colour grading results, 275–6schematic diagram, 270

fuzzy logic, 134, 268–76fuzzy logic technique, 133FWAs see fl uorescent whitening agents

Gaertner-Griesser device, 96–7, 112Ganz-Griesser method, 108–10Ganz linear whiteness and tint

formulae, 104–6with Berger formulae, 106with Stensby formulae, 106

Gardner Scale, 110Gaussian distribution curve, 271geometric attributes, 223German Standard DIN 6164, 43gingiva, 344–5Global Organic Textile Standard, 199GretagMacbeth spectrophotometer, 243grey hair, 379–82grey scale difference, 214grey scales, 208–12

staining step values, 214Guide to Measurement Uncertainty, 185gums see gingivaGuth model, 10

hair colourartifi cial hair colouring shades, 383–6

chemical bleaching, 383–4metallic hair dyes, 385natural hair colorants, 386permanent hair colorants, 384–5semi-permanent hair colorants, 385temporary hair colorants/rinses, 385vegetable hair dyes, 386

background, 371–3effect of environment, 382–3grey hair and age, 379–82hair follicle melanocytes during hair

cycle, 375–8catagen, 377–8early anagen, 377full anagen, 377telogen, 375

human anagen scalp hair folliclefully-pigmented, 376pigmented and greying, 374

measurement, 371–88future trends, 388methods and instruments, 386–8

natural, 373–9hair follicle pigmentary unit

development, 373–5

398 Index

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regulation of hair follicle pigmentation, 378–9

henna, 386heterochromia, 380hidden, 128hiding power, 290–1

calculation, 291measurement for white vs coloured

paints, 290–1non-coloured paints, 290

hierarchical agglomerative clustering technique, 175

High Volume Instrument system, 253Hopfi eld network, 134hue, 52–3, 224, 323hue difference, 225human visual system, 3–6, 314

eye structure, 4Hunter bench colorimeter, 327Hunter Colorimeter (1950), 253, 275Hunter formulae, 102–3Hunter Lab colour space, 82, 239, 243, 320Hunt’s model, 317HVI see High Volume Instrument systemHVI colorimeter, 254–5, 276

ICI Colour Atlas, 25–6ICS Micro Match spectrophotometer,

330, 336imaging colorimeters, 162imaging spectrographs, 162indo dye, 384input units, 128Inside/outside Bleaching, 360insolubility, 197Instrument 100, 188Instrument 500, 188, 189instrument calibration, 237instrument metamerism, 226instrument uncertainty, 186instrument uncertainty conditions, 186instrumental measurement

visual evaluation of whiteness and yellowness, 88–117

application in industry, cosmetics and dentistry, 111–15

assessment of whiteness, 90–4future trends, 115–17introduction, 88–90measuring techniques and

instruments, 95–100whiteness and yellowness indices, 100–11

inter-instrument agreement, 238Inter-Society Colour Council Subcommittee, 91interactive, 128Internal Non-Vital power Bleaching, 360International Organisation for

Standardisation, 200intrinsic tooth discoloration, 348–9IR drying technique, 292ISO 105, 203ISO 12232:2008, 154ISO 14524:2009, 154ISO 15739:2003, 154ISO 17025, 185ISO 17321:2006, 154ISO 22028:2006, 154

ISO 17321–2, 154ISO 105-A02, 208ISO 105-A03, 208ISO 105-B01, 205ISO 105-B02, 205ISO 105-B06, 205ISO 105-B08, 205ISO 105-C06, 206ISO 105-C08, 206ISO 105-D01, 206ISO 105-E01, 204ISO 105-E02, 204ISO 105-E03, 204ISO 105-E04, 204ISO 105-E05, 204ISO 105-E07, 204ISO 105-E08, 204ISO 105-E09, 203, 204ISO 105-E10, 203ISO 105-E11, 203ISO 105-E12, 203ISO 105-E13, 203ISO 105-G02, 204ISO 105-G03, 204ISO 105-G04, 204ISO 105-J05, 212ISO 105-N05, 203ISO 105 N01–4, 203ISO 105 P01, 203ISO 105-P01, 203, 207ISO 105-P02, 203ISO 105-X01, 203ISO 105-X02, 203ISO 105-X04, 203ISO 105-X06, 203ISO 105-X08, 203ISO 105-X11, 203ISO 105-X12, 204ISO 105-X16, 204ISO Blue Wool standards, 205ISO/CD 105-A11, 216ISO/FDIS 105-B07, 206ISO/TR 12116:2008, 207

Jade Green Standard, 287JPC79 formula, 283, 325JVCKY-F55B, 259

K-means clustering, 178–80K/S constant ratios, 334knitted sleeves, 239Konica, 246Kubelka-Munk equation, 191, 247, 296Kubelka-Munk theory, 126, 136, 141, 286,

309, 330–1

lateral geniculate nuclei, 5–6learning process, 129LEDs, 163lens, 4levelness, 186levelness uncertainty, 186levelness uncertainty conditions, 186light adaptation, 317light fastness, 205–6light spotter, 257lightness, 224, 323

Index 399

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Lobene stain index, 357loops, 128Lovibond Tintometer, 325luminance adaptation model, 155luminance factor Y, 100

MA98 multi-angle spectrophotometer, 308MacAdam limits, 43Macbeth 7000, 193Macbeth colour checker chart, 29Macbeth D65 fl uorescent lamps, 92machine learning, 129macula lutea, 5Marks & Spencer, 235Maxwell Colour Triangle, 76MDC1R gene see melanocortin-1 receptormean distance, 178Mean Plus algorithm, 194measurement system, 186Melanin Index, 387melanins, 372melanocortin-1 receptor, 373melanocyte, 372melanogenesis, 372, 378–9melanogenic zone, 377melanosomes, 372metamerism, 155, 158, 226–31, 293

degree of illuminant metamerism, 227instrumental test, 229–31metameric match, 227–8

matched in daylight but not in artifi cial light, 227

not a refl ectance match, 228non-metameric match, 227–8

dark grey fabric, 229fawn colour fabric, 228with two crossings, 229

reducing effects, 231Metamerism Index, 293microabrasion, 361milling acid dyes, 203Minimum Path, 181Minolta, 246Minolta CF 1440, 246, 299Minolta colorimeter CR-210, 257, 262monochrome camera, 156Motion Control HVI, 262M&S 89, 235M&S colour difference equation, 235multi-dimension thresholding algorithm, 261multi-spectral capture systems, 155, 160

covering VIS and NIR radiation for textile applications, 160–2

refl ectance spectra, 161reconstruction of spectral refl ectances from

multi-channel colour values, 155–7image capturing systems

classifi cation, 156transmittances with acquisition channels of

multi-spectral system, 157multilayer perceptrons, 130, 133, 264Munsell Book of Colours, 71Munsell colours order systems, 31–7, 70–1, 222,

313, 323, 325limitations, 34–5SCOTDIC colour atlas, 35–7

Munsell neutral scale, 71

National Cotton Council Quality Task Force Committee, 255

natural colour system, 38–41Nescafé Gold Blend, 334neural network, 263–8neurodes, 128neuron function, 130neurons, 128Nickerson-Hunter colour diagram, 254,

255, 268, 276nodes, 130non-contact spectrophotometers, 246, 299non-metamerism, 293non-vital tooth bleaching, 360

object metamerism, 226observer metamerism, 30, 226Oeko-tex 100 certifi cation, 199on-line colour measurement, 243–6opacity, 290opalescence effects, 346operator uncertainty, 186operator uncertainty conditions, 186opponent colour theory, 39optimal colour stimuli theory, 43optoelectronic sensor, 149Orintex, 243Orintex software, 243OSA-UCS system, 45–7

cubo-octahedron, 46Ostwald system, 41–3

paint fi lms and coatingscolour control system, 297–9

colour measuring instruments, 297–8instrument metamerism problems, 299instrument performance, 298today’s colour measuring instruments, 298

colour difference in FMC and CIELAB unit, 294

colour matching of automotive paints, 307–9metallic and pearlescent colours, 307portable multi-angle spectrophotometer,

307–9colour measurement, 279–309

applications, 295future trends, 309sample preparation, 291–2

computer colour matching for paints, 295–7instant colour matching at the paint

shop, 300–7conventional CCM vs Internet based POS,

306–7Internet based at Point of Sales, 302–6

paint quality control, 280–91CIE 1976 colour space, 281CIE L, C, h colour space, 281–2CIE L C h tolerance limits, 283CIE Lab colour space and CIE L, C,

h colour space, 281CIELAB colour co-ordinates, 282CIELAB vs CIE LCh, 284colour as numbers, 280colour strength, 286–7colour tolerance, 282–5FMC-II acceptance tolerance

limits, 285

400 Index

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opacity, contrast ratio and hiding power, 290–1

pigment load, 291tristimulus values computation, 280white pigments tinting strength, 287–8whiteness and yellowness indices, 288–90

pigment quality control, 292–5application techniques, 292–3pigment evaluation, 294pigment testing and making colorant

primaries, 293–4uncertainties in sample preparation, 295

wet paints colour properties measurement, 299–300

non-contact spectrophotometer, 299wet-paint colour matching, 300

Pantone colour system, 26pellicle, 345periodontium, 344permanent hair colorants, 384–5pheomelanins, 386pheomelanosome, 378Philips 83 lamps, 336pigment, 197pigment load, 291PocketSpec Spectrophotometer, 301Point of Sales system, 300

instant colour matching, 300–7Internet-based colour matching, 302–6

access credentials, 303Benevue Colour Management System

colour reader, 303book formula, 304closest colour, 304tint by name, 305tint by reading, 306unit ID, 305

low cost spectrophotometer, 301polyester fabrics, 247–8polyester yarns, 133Pope colour system, 50portable multi-angle spectrophotometer, 307–9

Minolta and X-Rite multi-angle instruments, 308

Minolta instrument, 308–9POS see Point of Sales systempractical colour coordinate system, 37principal component analysis, 156Procion Brillian Red HE 7B, 140protanomaly, 13protanopia, 13, 15pseudo-colour visualisation system, 161psychometric scales, 28–9

interval, 28–9nominal, 28ordinal, 28

quantitative light-induced fl uorescence, 357

RAL system, 49ratio scales, 29raw error, 131recurrent neural networks, 128Red-Green-Blue colour space, 356, 388refl ectance, 79–80refl ectance values, 133, 142refl ective spectrophotometry, 386–7

refurbishment, 206–7retina, 4

saliva, 345saturation, 224, 323scaling, 273sclerotic coat, 4SCOTDIC colour atlas, 35–7

constant hue loci in CIELAB space, 36Scotsort method, 177semi-permanent hair colorants, 385sensitivity, 238sensor, 149severe trauma, 349shade cards, 21–2shade number see sort codeshade sequencing, 180–2shade sorting see colour shade sortingShade Vision, 114ShadePilot, 114SheLyn Inc., 182sigmoid function, 132sigmoid units, 132single number shade passing, 283, 285Smith and Pokorny fundamentals, 7sort code, 171source metamerism, 226spatial characterisation, 152spatial-chromatic dithering, 158

effects on visual appearance and measurement, 158–60

spatio-chromatic dithering in imaging capture process, 159

spatial-CIELAB, 160SPD see spectral power distributionspectral characterisation, 152spectral match, 230spectral power distribution, 73, 90, 113spectral radiance factor, 137SpectraProbe XE, 245–6spectrophotometers, 133, 153, 158, 297,

326, 357spectroradiometers, 158SpectroShade, 114specular excluded mode, 293Spinlab HVI, 262Stearn’s method, 79Stensby formulae, 103strand testing, 384subtractive colour mixing, 196–7Super CCD, 162supervised learning, 129Swedish natural colour space system,

313, 325SYSTAT, 178

Taube formulae, 103TC38, 200, 212TCI/81, 215tele-colorimeters, 150, 159tele-spectrocolorimeter, 159TeleFlash 130, 246, 299TeleFlash 445, 299Tethered spectrophotometer, 301tetracycline, 349textiles, 197

colour as numbers, 222–4

Index 401

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chromatic attributes, 223colour and appearance, 223colour vs appearance, 223geometric attributes, 223

colour inspection of fi nished fabrics, 248–50enzyme wash details, 249fi nishing effect on textile colour

data, 250colour measurement methods, 221–51

future trends, 250–1metamerism, 226–31why colours do not match, 231–2

colour measurement techniques, 236–43fi bre, yarn, fabric and knitted

sleeves, 238–9instrumental considerations, 237–8Minolta non-contact

spectrophotometer, 241parameters for instruments, 236–7sample holders, 240sample preparation method, 239–41yarn orientation effect on values, 240

colour specifi cation, 224–6CIE Lab colour space, 224–5CIE LCh colour space, 225CMC colour difference and tolerance, 226

dry and wet fabrics, 247–8refl ectance spectra, 248

on-line colour measurement, 243–6centre-selvage, 244Hunterlab SpectraProbe XE, 245–6map control, 245map control system, 244–5non-contact spectrophotometers, 246production equipment, 243

sample presentation method, 242–3developing a repeatable technique, 242–3measurement technique, 242sample positioning, 242sample thickness, 242

visual vs numerical pass/fail, 232–6CIELAB colour differences, 233–4colour difference equation, 234–5DE acceptable limits, 234M&S equation, 235setting the pass/fail value, 233tips for instrumental pass/fail, 235–6

thermochromism, 238tinting strength, 287–8tooth

colorimetric evaluation, 343–62future trends, 361–2optical properties of teeth, 345–6tooth whiteness, 351–2

colour, 347–8distribution, 347–8variation, 347

colour measurement, 352–6digital image analysis techniques, 355–6instrumental measurement, 354–5visual assessment, 353–4

factors that impact tooth colour and its perception, 348–52

extrinsic tooth stain, 349–50intrinsic tooth discoloration, 348–9other factors, 350tooth colour perception, 350–1

human dentition and its environment, 344–5saliva and pellicle, 345teeth and gums, 344–5tooth anatomy, 344

measurement of extrinsic stain, 356–7clinical indices, 356–7instrumental methods, 357

methods to improve tooth colour, 357–61microabrasion, 361non-vital tooth bleaching, 360vital tooth bleaching, 359–60whitening toothpastes, 358–9

tooth enamel, 344tooth whiteness, 351–2total radiance factor, 95traditional whiteness formulae, 102–4

coeffi cient in the Berger whiteness formulae, 103

hue preference comparison, 104training set, 134translucency blurring, 194TRF see total radiance factortrichromatic cameras, 163trichromatic colorimeters, 325–6trichromatic theory, 6–7, 319trichromatic vision, 387tristimulus, 280tristimulus match, 297tristimulus system, 285tristimulus values, 77–80tritanomaly, 14tritanopia, 13, 15truncation, 273Turbid-Medium theory, 296Two Constant Theory, 297

ultraviolet light, 382–3uncertainty analysis, 185–6

determination, 184relationship of parameters, 187uncertainty values in units of the

value itselfsingle baby blue smooth glossy plastic

plaque for Instrument 100, 189single baby blue smooth glossy plastic

plaque for Instrument 500, 189single yellow smooth glossy

plastic plaque for Instrument 100, 188


universal colour language, 61–2universal colour names, centroid colours and

hue angle boundaries, 63Universal Cotton Standards, 255US cotton classing system, 276USDA cotton colour grades, 253–77USDA Cotton Program, 255, 268USDA universal standards, 253

VeriColour Solo, 246VeriColour Spectro, 246VeriColour spectrophotometers, 299VeriColour System, 246video image analysis, 339vinyl sulphone dyes, 139, 140visual evaluation

402 Index

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instrumental measurement for whiteness and yellowness, 88–117


assessment of whiteness, 90–4future trends, 115–17introduction, 88–90limits of white objects and optimal

colours, 89measuring techniques and instruments,

95–100spectral refl ectance factor

measurements, 89whiteness and yellowness indices, 100–11

visual perception, 4–5visual sequencing, 180visual tapering method, 180Vita Bleachedguide 3D-Master, 354Vita Lumin Shade Guide, 351, 353vital tooth bleaching, 359–60Vitapan 3D-Master Shade Guide, 351, 354Vivitar Electronic Flash 1900, 259–60Von Kries coeffi cient law, 16

Walking Bleach Technique, 360water base paints, 293wavelength calibration, 237Weber and Fechner law, 43wet fabrics, 247–8whiteness


cosmetics, 114dentistry, 114–15food, 113leather, 113paper and pulp, 112–13textiles, 111–12

future trends, 115–17new whiteness formulae, 115–16open question in instrumental

evaluation, 116–17measuring techniques and instruments,

95–100CIE WI of textile samples, 99Gaertner-Griesser UV calibrating

device, 97

practical daylight simulators, 96–8refl ectance curve, 99total radiance factor, 95TRF of non-fl uorescent vs fl uorescent

white textile, 96UV radiation levels, 98white vs near-white samples, 98–100

visual and instrumental evaluation, 88–117illumination effect, 91–3practical daylight simulators, 93–4sorting and ranking methods, 94

whiteness and yellowness indices, 100–11brightness function, 101CIE whiteness and tint formulae, 106–8colour difference, 104Ganz-Griesser method, 108–10Ganz linear whiteness and tint

formulae, 104–6luminance factor Y, 100paper brightness, 100–2SPD for light source between CIE

illuminants A and C, 101traditional whiteness formulae, 102–4

whiteness index, 100–11, 288–9whitening toothpastes, 358–9WI E313, 288

X-Rite, 243, 246, 299XYZ values see tristimulus values

yarn, 238–9yellowness


cosmetics, 114dentistry, 114–15food, 113leather, 113paper and pulp, 112–13textiles, 111–12

visual and instrumental evaluation, 88–117whiteness and yellowness indices, 100–11

coeffi cients of the ASTM E 313–05, 111data points of the 18 Gardner fi lters, 110Gardner scale value, 111yellowness indices, 110–11

yellowness index, 100–11, 289–90

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