Identification of western Canadian wheat classes at

different moisture levels using near-infrared (NIR) hyperspectral imaging

S. Mahesh, D.S. Jayas, J. Paliwal, and N.D.G. WhiteCSBE Annual Meeting 2008

Outline Introduction Objectives Materials and Methods Results and Discussion Conclusions and Future work Acknowledgements

Introduction Wheat production = 26.7 Mt and

export = 14.0 Mt in Canada in 2005 (FAO statistics)

Eight major wheat classes in western Canada:Canada western red spring (CWRS)Canada western hard white spring (CWHWS)Canada western amber durum (CWAD)Canada western soft white spring (CWSWS)Canada western red winter (CWRW)Canada western extra strong (CWES)Canada prairie spring white (CPSW)Canada prairie spring red (CPSR)

Introduction Wheat harvesting – 13 to 15% m.c. (normally

15% m.c.) – drying – storage

Wheat @ 12 to 13% m.c.- safe moisture for effective storage- prevention of spoilage by fungi- sprouting before processing can be prevented

Wheat class identification – Major task in grain handling facilities

Visual method (common method)- to identify different wheat classes - but not to identify their moisture levels

Machine vision, PAGE, and HPLC methods

Introduction Near infrared (NIR) hyperspectral imaging

- Machine vision + NIR spectroscopy

- to develop a rapid and consistent method- Non destructive, non subjective method- Food science, Chemistry, Pharmaceuticals, Animal

science- Grain storage: wheat class identification, moisture

identification, protein and oil content determination in wheat

Objectives To identify western Canadian wheat classes at

different moisture levels by developing statistical classification models

Materials and Methods Hyperspectral imaging system

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1. Bulk wheat sample, 2. Liquid crystal tunable filter (LCTF), 3. Lens, 4. NIR camera, 5. Copy stand, 6. Illumination, and 7. Data processing system.

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Methods and Materials Wheat classes: CWRS, CWSWS, CWHWS, CWRW,

and CWES

Moisture levels: 12, 14, 16, 18, and 20%

100 images/class/m.c. – 960 to 1700 nm – 10 nm interval

Methods and Materials Relative reflectance intensity, R = ([S-D]/[W-D]

where: R = relative reflectance intensity of each slice of the NIR

hyperspectral image of wheat; S = reflectance intensity of each slice of the NIR

hyperspectral image; D = reflectance intensity of the dark current; W = reflectance intensity of a 99% reflectance standard white panel

Linear and quadratic discriminant analyses: statistical classification models

Results Linear discriminant analysis

9598

9396

100

94

83

89

969495

93

8892

100

75

81

87

99

94

58

65

98100

91

0

20

40

60

80

100

CWES CWHWS CWRS CWRW CWSWS

Cla

ssif

icat

ion

acc

ura

cy (

%)

12% 14% 16% 18% 20%

Results Quadratic discriminant analysis

70

57

74

8082

75 74 73

59

89

72

6165

83 84

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48

55

78

69

89

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82

89

0

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60

80

100

CWES CWHWS CWRS CWRW CWSWS

Cla

ssif

icat

ion

acc

ura

cy (

%)

12% 14% 16% 18% 20%

Results Top 10 wavelengths in wheat class identification

No. Wavelength (nm) Partial R2 ASCC

1 1310 0.66 0.032 1450 0.80 0.063 1060 0.76 0.094 1700 0.72 0.125 1330 0.55 0.136 1200 0.33 0.147 1160 0.33 0.158 1090 0.29 0.169 1490 0.28 0.1610 1070 0.26 0.18

ASCC = Average squared canonical correlation

Discussion Identification of waxy wheat – 1 to 10 principal

component scores as input – 42 to 71% (LDA) and 46 to 71% (QDA) (Delwiche and Graybosch 2002)

Classification of barley based on ergosterol levels - 86.6% (LDA and QDA) (Balasubramanian et al. 2006)

Mohan et al. 2005: Mean classification accuracies = 89.1% (LDA, Top 2 Ref. features), 99.1% (LDA, Top 5 Ref. features) – Cereal grains classification

Discussion 81 – 100% (LDA) and 60 – 89% (QDA) – relative

reflectance intensities – Identification of wheat classes at different moisture levels

Conclusions and future work NIR hyperspectral imaging was found useful to

identify different moisture level wheat classes with the extracted relative reflectance intensities as input for classification

This technique could be used to develop an automatic grain assessment tool

Wheat samples from different crop years and locations could be included in the sample space to improve the robustness and classification efficiency of the models

Acknowledgements Dr. Digvir S. Jayas Dr. Jitendra Paliwal Dr. Noel D.G. White