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Quantitative Analysis of Sugars in Confectionery Products

by Fourier Transform Infrared Spectroscopy:

Development of Analytical Methodology Using a Mid­

Infrared Fiber Optic Probe and Investigation of the Effects of

Sugar-Water Interactions in Model Systems

Dv

ALINE DIMITRI-HAKIM

Department ofFood Science and Agricultural Chemis~Macdonald Campus of McGiIl UniversitySte-Anne-de-Bellevue, Québec, Canada

A Thesis submitted ta the Faculty of Graduate Studies and Research, in partial

fulfillment of the requirements for the degree ofDoctor ofPhilosophy

May, 2000

©Aline Dimitri-Hakim

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Short Title:

Quantitative Analysis ofSugars in Confectionery Products by FTIR

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Abstract

A mid-infrared chalcogenidefiber optic probe was employed to develop a

Fourier transfonn infrared spectroscopy-based partial-least-squares (PLS) calibration

model for the quantitative analysis of sucrose, glucose, fructose, maltose, total sugar

and water content in chocolate syrup. Based on the comparison of the pure

component and correlation spectra extracted from chocolate syrup and aqueous sugar

solutions based models, it was detennined that the tightness of the concentration

ranges and the ratios of the sugars in the chocolate syrup samples did not allow to

draw adequate information to build a robust PLS calibration mode!. PLS regression

models developed using infrared spectra of chocolate syrup calibration standards

prepared by addition of sugar solutions to increase the concentration range did not

yi~ld conclusive results. A different approach used for standard preparation consisted

of diluting chocolate syrup samples to different degrees. This new method provided

an increased concentration range for the sugars but maintained an aImost constant

sugar to sugar ratios. The PLS models based on these new calibration standards

yielded high calibration correlation coefficients and low errors on the external

validation. Accuracy, repeatability, long-tenn stability and ruggedness were tested

and the results demonstrated that the calibration models were robust and had a better

repeatability than the reference high-perfonnance liquid chromatography method.

The fact that the calibration model was developed using standards having very

similar sugar profiles precluded its use for the analysis of chocolate syrup samples of

different formulations. The resulting fonnulation-specific PLS regression model

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required a preclassification step to ensure that the model is applied to the appropriate

sample type. A probabilistic neural network (PNN) model was developed to fuIfi Il

the preclassification requirement. PNN yielded excellent classification results. The

modeling uncovered a built-in ability of PNN ta reject outliers. The artificial neural

network (ANN) modeling approach was also used ta predict the chocolate syrup

samples without any prior sample preparation. Using a backpropagation and

cumulative delta mIe, ANN was able ta predict aIl the sugar components with high

accuracy. Two-dimensional infrared correlation spectroscopy was employed to study

the complex interactions between sugar and water molecules in solution. The results

revealed that dilution of a sample produces a complex pattern of cross-eorrelation

bands and results in band position change.

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Résumé'

Une probe en fibre optique faite de chalcogenide a été utilisée dans le

dévelopement de modèles de calibration PLS à partir de spectres infra-rouge. Ces

calibrations ont permis d'évaluer le contenu en sucrose, glucose, fructose, maltose,

taux de solides dissouts et eau du sirop de chocolat. Mais au préalable, une

évaluation des spectres mathématiques et statistiques décrivant le système de sucres

en solution aqueuse (représentation des composantes pures) a révélé un manque

d'information qui pourrait compromettre les capacités de prédiction d'un modèle

généralisé dû à la plage restreinte des concentrations des différentes composantes. La

performance des modèles de calibration générés à partir de spectres infra-rouge

d'échantillons préparés par ajouts dosés dans le but d'étendre la plage des

concentrations n'a pas été concluante. Une nouvelle approche de préparation de

standards qui consistait à diluer les échantillons de sirop de chocolat a donc été

envisagée. Cette nouvelle méthode de préparation de standards a permis de varier les

concentrations de chacune des composantes de manière à satisfaire les exigences de

la méthode PLS, toute fois, les rapports sucres-sucres sont restés presqu'inchangés.

Les résultats obtenus à partir des modèles de calibration résultants de cette nouvelle

approche ont démontré une haute performance de modélisation, confirmée par un

haut coefficient de correlation linéaire entre l'absorbance infra-rouge et la

concentration des composantes. Des testes de précision, répétabilité et de stabilité à

long-tenne ont prouvé la superiorité des ces modèles sur la méthode de référence

HPLC. Le manque de varabilité dans les rapports sucres-sucres fait de ces

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calibrations des modèles spécifiquement dédiés à un type de formulation de sirop de

chocolat qui requière donc une étape de présélection d'échantillon. A cet effet, un

réseau d'intelligence artificielle probabilistic (PNN) a été dévelopé. PNN pouvait

faire la classification et avait les capacités de détecter et de rejéter les échantillons

hors-paires. Un réseau d'intelligence artificielle (ANN) a été utilisé pour l'analyse

quantitative des composantes du sirop de chocolat avec une grande précision

analytique. Pour finir, les interactions complexes entre molécules de sucre et

molécules d'eau ont été étudiées par l'entremise de la spectroscopie de corrélation

infra-rouge en 2 dimensions.

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Claim of Original Research

1. This work is the first application of FTIR spectroscopy to the analysis of sucrose,

glucose, fructose, maltose, total sugar and water content in chocolate syrup.

2. This study is the first to report a new approach for FTIR-PLS calibration

development using production-line samples with narrow concentration ranges.

The new approach successfully modeled sucrose, glucose, fiuctose, maltose, total

sugars, and water in chocolate syrup. An attempt at which conventional sample

preparation for PLS calibration models failed.

3. This work is the fust application of a mid-infrared chalcogenide fiber optic

sampling accessory in the quantitative analysis ofa food matrix.

4. The work is the first to investigate the transferability of a PLS calibration model

between two different sampling accessories, a chalcogenide fiber optic probe and

a gennanium horizontal ATR, installed on FTIR spectrometers of different model

types.

5. This work is the first to make use of artificial neural networks for the purpose of

preclassification ofsamples in a quantitative PLS-based FTIR analysis.

6. This work is the first to make use of artificial neural network for the quantitative

analysis ofsugars.

7. This work is the first to make use of the two-dimensional (2D) correlation

approach to specifically study sugar-water and sugar-sugar interactions in sugar

501utions.

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Conferences

Parts ofthis work were presented by the author at different conferences and scientific

meeting as follows:

1. Dimitri~ A.~ Ismail A. A., Druy M. A., Hurst W. J. (1999) Multivariate Analysis

versus Neural Networks in the Quantitative Analysis of Carbohydrates in

Solution, PittCon'99, 7-12 March, Orlando, FL.

2. Dimitri, A., Ismail A. A., Druy M. A., Hurst W. J. (1998) Profiles of Beverages,

Concentrates and Syrups Using a Novel FTIR Fiber Optic Probe, EAS, 15-20

November, Somerset, NJ.

3. Dimitri, A., Ismail A. A. (1998) Calibration, Calibration Stability and Calibration

Transferability in Quantitative Analysis of the Sugar Profile in Syrups by FTIR.

Spectroscopy, 44th ICASS, 9-12 August, Kingston, ON.

4. Dimitri, A., Ismail A. A.~ Hurst W. J., Druy M. A. (1997) PLS Determination of

Sugar Profiles in Syrups by FTIR. Spectroscopy, EAS, 16-21 November,

Somerset, NJ.

5. Dimitri, A., Ismail A. A., Hurst W. J. (1997) Potential Use ofFTIR Spectroscopy

with Fiber Optic Sampling System in the Quantitative Analysis of Sugars in

Chocolate Syrups, 43rd rCASS, 10-13 August, Montreal, Qc.

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Acknowledgments

l wish to thank my supervisor, Dr. Ashraf A. Ismail, for his support and the freedom

he has given me in my research. To Mrs. Sedman for the stimulating conversations.

Ta my fiiends, Janie, Nin~ Nada, Sadi~ and all the rest, thank YOU. Especially,

Mahtab, thank you for your help. To all my students for their support and

encouragement during the writing ofthis thesis. Ta Ben, thank you for jumping in at

a moment notice. Ta my lab colleagues, especially Takrima and Fenny. To Rana,

we've been thraugh a lot....

And ta you, Thank You, Me.. ..

To Mrs. Lise Stiebel and Barbara Laplaine. Many thanks for yoUf patience and

smiles. You always make us feel welcome.

To the professors on my committees, Dr. Ali, Dr. Yaylayan, Dr. Marshall, Dr.

Kermasha and Dr. Raghavan. To Dr. Kermasha for the continuous words of wisdom.

To Dr. Jeff Hurst and th.e staff of the analytical division at Hershey Foods Technical

Center (Hershey, PA) for praviding the pre-analyzed chocolate syrup samples and a

lot of support. To Sensiv, Inc. (Waltham, MA) and the Natural Science and

Engineering Research Council for the financial support in the form of an Industrial

Post-Graduate Scholarship.

And last but not least, to my parents and my brother. Thank you for everything.

Vou've always been there. Your patience and support mean a lot to me. Thank you

for giving me the opportunity and the strength ta go aIl the way. 1 love

you .

vii

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Table of-Contents

Abstract.•....................•................•..............•......•...........•..........•................................... i

Résumé ..•.••••••...•.•.•.....•••....••..•.•...•.•.•....•.......•.•.........••.......•..•...••.•••...•............••.••......• iii

Claim of Original Research v

Conferences •.............••.•....•.•••...•.............•..........•.....•...••..........•................................. vi

Acknowledgments ...•........•......••...•...............•••.....••.•....•...•..•..........................•......... vii

List of Abbreviations ..•...•••..•....•...........•................•.•..••.........••..........•...•................. xv

List of Figures.......•.•.••.....•••...•......•.....••..•.........•......•••..•...•..•...........•.....•..........••..... xvi

List of Tables ...•...••...••..•..•••..•..••..•....•.•.....•.•.•••..•..••..••....•..•.•......•.••...•..........•.......... xxi

Chapter 1: Introduction .•••..•..•••.•••...•......••........•..•..•..••...•.......•...•........•.•.••...•........... 1

1.1. General introduction ...•...•.......•••...................•................................................ 1

1.2. Rationale and objectives of the present research 2

Chapter 2: Literature Review..•................................................................................ 4

2.1. Sugars: Structure and basic chemistry 5

2.1. 1. Monosaccharides: glucose and fructose 6

2.1.2. Disaccharides: maltose and sucrose ~ 10

2.2. Carbohydrates in foods •.•.........•..•.......•.•....•..•..........•••.....•....•...................•.. 13

2.2.1. Key functionalities of sugars in food 13

2.2.1.1. Sweetening agents 13

2.2.1.2. Texturizers 14

2.2.1.3. Flavor development 14

VUI

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2.2.2. Sugars in chocolate 15

2.3. Current methods of sugar analysis...•...••.••....••.........••.......•..•.•..•.........•.•..... 16

2.3.1. Physico-chemical and enzymatic assays in the analysis of carbohydrates 16

2.3.2. Chromatographie methods for the analysis of carbohydrates 17

2.4. Fourier transform infrared (FfIR) spectroscopy 18

2.4.1. Principles ofFI1R spectroscopy 18

2.4.1.1. Conventional IR 19

2.4.1.2. Fourier transform infrared (FITR) spectroscopy 19

2.4.1.3. Overview ofITIR spectroscopy in the mid-IR region 20

2.4.2. Sampling accessories employed for recording infrared spectra 22

2.4.2.1. Transmission ceII 22

2.4.2.2. Horizontal attenuated total reflectance (HATR) 23

2.4.3. Selected mathematical tools for the analysis of infrared spectra 25

2.4.3.1. Two-dimensional infrared correlation spectroscopy 25

2.4.3.2. Principal component analysis 26

2.4.3.3. Partial-least-squares 27

2.4.3.4. Artificial neural networks 30

2.4.4. Food-related applications ofFTIR spectroscopy 34

2.4.5. Analysis of carbohydrates by FTIR spectroscopy 35

2.4.5.1 Physico-chemical studies of carbohydrates by FfIR spectroscopy 36

1. Monitoring ofmutarotation by FTIR spectroscopy 36

2. Evaluation of sugar-water interactions by FI1R spectroscopy 37

3. Assessment ofsugar-sugar interactions by FfIR spectroscopy 39

2.4.5.2. Analysis ofsugars in food by FITR spectroscopy 39

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1. Food classification 40

2. Quantitative analysis of water-based solutions and beverages .40

2.5. Fiber optics for infrared sensing 43

Chapter 3: Material and Methods 46

3.1. Method development for chocolate syrup employing a mid-ir

chalcogenide fiber optic probe .•.•••.••....••••••.....•..••.•.....•..•.....•...•.•......•••...•..•... 46

3.1.1. Description of the fiber optic probe accessory 46

3.1.2. Prelirninary studies of fiber optic probe accessory 50

3.1.2.1. Cable stability study 50

3.1.2.2. Evaluation of the signal-to-noise ratio 52

3.1.2.3. Evaluation of the effects of the chalcogenide cut-off point on spectral

information for the quantitative analysis ofsugar 53

3.1.2.4. Evaluation of the chalcogenide fiber optic probe accessory to perforrn

quantitative analysis of sugars in solution 55

3.1.2.5. Effect of restricted and wide dynarnic concentration range on the

performance of calibration models 57

3.1.3 Chocolate syrup analysis 58

3.1.3.1. Deve10pment ofa cleaning protocol for the fiber optic probe tip 58

3.1.3.2. Development ofPLS calibration models for chocolate syrup analysis 62

3.1.3.3. Validation of chocolate syrup validation models 63

3.1.3.4. Evaluation ofchocolate syrup calibration model performance and stability63

1. Accuracy test 63

2. Repeatability test 63

3.Reproducibility test 64

3.1.3.5. Study of chocolate syrup calibration model ruggedness 64

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3.1.3.6. Prediction ofundiluted chocolate syrup samples 65

3.1.3.7. Transferability test 66

3.2. Modeling systems 67

3.2.1. Partial-Ieast-squares 67

3.2.2. Artificial neural networks 67

3.3. Study of chemical interactions•..........•.......•.....•.......•.•....•..•.•..••........•.........• 68

Chapter 4: Results and Discussion 69

4.1. Quantitative analysis of chocolate syrup using a chalcogenide liber otpic

probe 69

4.1.1. Evaluation of a chalcogenide fiber optic probe for the quantitative analysis of

sugars in solution 69

4.1.1.1. Effect of the stability of the fiber optic probe cables on spectral infonnation

......................................................................................................................69

4.1.1.2. Perfonnance comparison of two speetrometers on the basis of their signal-

to-noise ratio and ruggedness parameters 78

4.1.1.3. Effeet of the chalcogenide material cut-off point on sugar calibration models

......................................................................................................................83

4.1.1.4. Quantitative analysis of sugar profile~ in sugar solutions employing the fiber

optic probe accessory 94

4.1.2. Analysis ofchocolate syrup 102

4.1.2.1. Composition of chocolate syrup 102

4.1.2.2. Examination of the infrared spectrum ofa chocolate syrup sample 103

1. Evaluation of the matrix effect 103

2. Evaluation of the tightness of concentration range and sugar ratïos 109

3. Protocol for cleaning the fiber optic probe tip between samples 113

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4. Standardized sample analysis protocol 119

4.1.2.3. Calibration development and validation for chocolate syrup 120

1. Calibration models based on randomized "sugar-spikedn chocolate syrup and

''water-diluted'' samples 120

2. Calibration models based only on "sugar-spiked" chocolate syrups 126

3. Calibration model build on "sugar-spiked" and "sugar-diluted" chocolates

syrup 128

4. Calibration based on the "water-diluted" chocolate syrup samples 131

4.1.2.4. Testing the performance of the chocolate syrup calibration models 145

4.1.2.5. Testing chocolate syrup calibration ruggedness 155

1. Effect of reducing measurement time 155

2. Evaluation of the effect ofsample standing time , 158

3. Evaluation of the effect ofusing an non-purged spectrometer 158

4.1.2.6. Perfonnance of calibration models based on diluted samples to predict

undiluted chocolate syrup samples 160

4.1.2.7. Transferability 163

1. Transferability between same make spectrometers 165

2. Transferability between FTIR-spectrometers from different manufacturers .. 166

3. Transferability between different sampling accessories '" 168

4.1.3. Integrated software for dedicated analyses 173

4.2. Sugar profile modeling by artificial neural networks 173

4.2.1. ANN terminology 174

4.2.2. Classification ofchocolate syrup by ANN 175

4.2.3. Analysis of chocolate syrup by ANN 179

4.2.4. Application of ANN modelling to aqueous solutions of sugar mixtures 188

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4.3. Investigation of the sugar-suagr and sugar-water interactions in

carnohydrate solutions 202

4.3.1. Effect of increasing the concentration of a single sugar in solution on its infrared

spectrum 204

4.3.2. Potential application of 2D IR correlation spectroscopy for the study of the

change in spectral features of single sugar solutions with increasing sugar concentration

.....................................................................................................................................209

4.3.2.1. 2D IR synchronous mapping of the effect of sugar concentration on the

spectral profile 211

4.3.2.2. 2D IR asynchronous mapping of the effect of sugar concentration on the

spectral profile 211

1. Analysis of the asynchronous map of fructose in solution 211

2. Analysis of 2D IR asynchronous maps of glucose in solution 216

3. Analysis of 2D IR asynchronous maps of sucrose solutions .218

4. Analysis of 2D IR asynchronous maps ofmaltose solutions 220

4.3.2.3. General interpretation of data and hypothesis 222

Chapter 5: Conclusion and contribution to knowledge 224

Appendix 1: IR band assignment for sugars 227

Appendix 2: Concentration distribution of "sugar-spiked" chocolate syrup

samples 240

Appendix 3: Summary of sucrose, glucose and fructose mixture solutions

prepared for ANN study 241

Appendix 4: Table of best fit linear regression equations for the predicted vs.

actual of ail calibrations 244

Appendix 5: PRESS for ail the calibrations developed 246

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Appendix 6: Algorithms for transferability between different sampling

acceSsories on different spectrometers (Section 4.1.2.7.3.) 250

Appendix 7: Concentration dependent band shift in sucrose, fructose, and

maltose 2S1

Appendix 8: 2D IR asynchronous correlation peaks for fructose 254

Appendix 9: 2D IR asynchronous correlation peaks for glucose 256

Appendix 10: 2D IR asynchronous correlation peaks for sucrose ........•.•....•.... 258

Appendix Il: 2D IR asynchronous correlation peaks for maltose 259

References 260

xiv

• List of Abbreviations

2DIR Two-dimensional infrared correlation

ANN Artificial neural network

ANOVA Analysis of variance

ATR Attenuated total reflectance

CLS Classical least squares

DTGS Deuterated triglycine sulfate

FO Fiber optic

FOP Fiber optic probe

FTIR Fourier transfonn infrared

HATR Horizontal attenuated total reflectance

HFCS High fructose corn syrup

HPLC High-perfonnance liquid chromatography

• ILS Inverse least squares

IR Infrared

LOD Limit ofdetection

NIR Near infrared

PCA Principal component analysis

PDS Piecewise direct standardizatîon

PE Processing elements

PLS Partial-Ieast-squares

PNN Probabilistic neural network

PRESS Predicted residual error sum ofsquares

RMS Root mean square

RMSE Root mean square error

sn Standard deviation

SNR Signal-to-noise ratio

SOM Self-organizing map

•xv

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List of Flgures

Figure 2.1: Chemical structure of (a) glucose (open fonn, a-glucopyranose, 13­

glucopyranose), (h) fructose (P-D-fructofuranose, p-D-fructopyranose),

(c) sucrose and maltose '" 7

Figure 2.2: In vivo enzymatic hydrolysis ofstarch 9

Figure 2.3: Schematic representation of an attenuated total internaI reflectance

(ATR) sampling accessory 24

Figure 2.4: Diagram of the three fundamentallayers ofan ANN 32

Figure 3.1: Photograph of the different parts of the fiber optic probe accessory. (a)

fiberlink (b) probe (c) installed in the spectrometer 47

Figure 3.2: Schematic and photograph of the sensing part of the fiber optic probe

accessory 48

Figure 3.3: Schematic ofcable stability test 51

Figure 4.1: Effect of the vertical movement of the fiber optic probe accessory on the

spectral quality in the region of 950-1300 cm-1 (a) with a vertical

movement of fiber optic probe assembly and (h) without movement (c)

DifferentiaI spectnIm 71

Figure 4.2: Differentiai spectrum of consecutive single beams taken at (a) lowest

connector strain position, and (h) highest coupler strain position 72

Figure 4.3: Design of the fiber optic probe stabilization accessory 75

Figure 4.4: Differentiai spectrum from two spectra of water recorded after

stabilization of the fiber optic probe accessory 77

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Figure 4.5: Single beam spectrum of air collected using the fiber optic probe

accessory 84

Figure 4.6: Residual noise in the region between 900 and 1500cm-J 85

Figure 4.7: Spectrum of a 70% w/w sugar solution scanned using the gennanium

HATR 87

Figure 4.8: Actual vs. Predicted for ail sugar components based on full and restricted

sugar region 90

Figure 4.9: Predicted vs. Actual of calibration set for all fiber optic based calibration

models 96

Figure 4.10: Typical spectrum of a chocolate syrup sample showing the water

absorption region (1500-1800 cm-Il, the sugar region (950-1500 cm-I),

and the mono/di-glyceride region (1740-1770 cm-I) 104

Figure 4.11: Typical correlation spectrum obtained from twenty chocolate syrup

smnples 106

Figure 4.12: Typical correlation spectrum for twenty sugar solutions with

carbohydrate profiles similar to that ofchocolate syrups 107

Figure 4.13: Typical pure component spectrum obtained for undiluted chocolate

syrup samples 108

Figure 4.14: Typicalloading spectra obtained for undiluted chocolate syrup samples

.............................................................................................................. 110

Figure 4.15: Concentration variability in restricted vs. wide range s3inples Il 1

Figure 4.16: Typical correlation spectnun of twenty sugar solutions with wide sugar

concentration ranges 112

Figure 4.17: Typical correlation spectrum ofdiluted chocolate syrup samples ...... 114

xvii

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Figure 4.18: A typical mid-infrared spectrum of pure monoglycerides and pure

diglycerides recorded using an ATR sampling accessory 115

Figure 4.19: Scaled spectra of C-H absorption bands from the fat build-up in the

2850-2960 cm-1 region 117

Figure 4.20: Changes in the ester absorption region of mono and diglycerides using

the optimized cleaning protoco!. , 118

Figure 4.21: Component co-linearity testing for Usugar-spiked" chocolate syrup

samples 122

Figure 4.22: Calibration and validation results for sucrose modeling based on

"sugar-spiked" and Uwater-diluted" chocolate syrup 125

Figure 4.23: Calibration and validation resuIts for sucrase modeling based on

"sugar-spiked" chocolate syrup 127

Figure 4.24: (a) Calibration results and (b) PRESS for sucrose modeling based on

"sugar-spiked" and "sugar-diluted" chocolate syrup 129

Figure 4.25: Pure component spectrum of sucrose from "sugar-spiked" and "sugar-

diluted" chacolate syrup 130

Figure 4.26: Plots of Predicted vs. Actual for aIl the optimized "water-diluted"

chocolate syrup-based calibration models 134

Figure 4.27: Typical PRESS for the diluted chocolate syrup models (Sucrose) ..... 135

Figure 4.28: Plot ofpredicted vs. actual concentration of maltose in diluted chocolate

syrup samples 138

Figure 4.29: Comparison of scaled spectral profile in the sugar region of (a) original

chocolate syrup formulation, (b) new chocolate syrup formulation, and

(c) special dark formulation 143

XVlll

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Figure 4.30: Comparison of scaled spectral profiles of sucrose-glucose aqueous. .solutions with (a) 15% w/w sucrose and 15% w/w glucose, (b) 15% w/w

sucrose and 5% w/w glucose, and (c) 15% w/w glucose and 5% w/w

sucrose 144

Figure 4.31: Graphic representation of accuracy testing for Maltose 149

Figure 4.32: Comparison of scaled spectra of a sample collected using (a) a Ge­

HATR mounted on a Nicolet Magna 550 and (b) the fiber optic probe

mounted on a Bornem MB 170

Figure 4.33: Transferability algorithm developrnent (Maltose) 171

Figure 4.34: Residual spectrum from an unclassified spectrum by PNN and a typical

spectrom ofits actual group type 177

Figure 4.35: Error (RMSE) monitoring for chocolate syrup ANN architecture

optimization 181

Figure 4.36: Error (RMSE) monitoring for chocolate syrup ANN learning rate

optimization 183

Figure 4.37: Error (RMSE) monitoring for chocolate syrup ANN mornentum

optimization 184

Figure 4.38: Accuracy of the chocolate syrup ANN model based on ± 1SD error

range 187

Figure 4.39: Co-linearity testing for sugar solutions in ANN training set. 189

Figure 4.40: Error (RMSE) monitoring for sugar mixture solution ANN architecture

optimization 192

Figure 4.41: Error (RMSE) monitoring for sugar mixture solution ANN leaming rate

optimization 193

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Figure 4.42: Error (RMSE) monitoring for sugar mixture ANN momentum

optimization 194

Figure 4.43: Eigenvalue plot from PCA on the spectra of the ANN sugar solution

training set 195

Figure 4.44: Actual vs Predicted for PLS model of sugars in solution 199

Figure 4.45: PRESS for PLS model ofsugar solutions 201

Figure 4.46: Plots of the concentration (5% to 60% w/w) versus the area between

900 and 1500 cm- I for sucrose, glucose, fructose and maltose solutions

.............................................................................................................. 205

Figure 4.47: Band shift in the spectra of samples with increasing glucose

concentration at 1033 cm·1 207

Figure 4.48: 2D IR correlation synchronous map (Sucrose) 212

Figure 4.49: 2D IR correlation asynchronous map for fructose solution spectra the

concentration increases 214

Figure 4.50: 2D IR correlation asynchronous map for spectra of glucose solutions as

a function of increasing concentration 217

Figure 4.51: 2D IR correlation asynchronous map for sucrose spectra as a function of

concentration 219

Figure 4.52: 2D IR asynchronous map from spectra of maltose solutions as a

function of increasing concentration 221

xx

•

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•

List of Tables

Table 2.1: Degree of sweetness relative to a 10% aqueous sucrase solution for

different sugars and sugar alcohols 12

Table 2.2: Most common Artificial Neural Network architectures and their

characteristics 33

Table 3.1: Summary of the main characteristics of the fiber optic probe accessory. 49

Table 3.2: Summary of sugar solutions used to assess the effect of the eut-off point

on the predictive ability ofPLS models 54

Table 3.3: FTIR parameter settings for the study of the eut-off point effect. 56

Table 3.4: Referen.ce solutions of narrow sugar concentration ranges 59

Table 3.5: Reference solutions ofwide sugar concentration ranges 60

Table 4.1: Summary of the RMS noise associated \vith the systems studied in the

region of 1050-1500 cm-1 ,. 79

Table 4.2: Performance of the calibration models used to select the appropriate

spectrometer-FOP for the chocolate syrup analysis methodology 82

Table 4.3: Calibration and validation results for comparison between the full region

and the restricted region based calibrations 89

Table 4.4: One-way ANOVA test results for comparison between full region and

restricted region based calibrations 91

Table 4.5: Calibration results for fiber optic probe based models using sugar

solutions 95

xxi

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•

Table 4.6: Results of the one-way ANOVA test on the extemal validation for the

fiber optic probe based calibrations 97

Table 4.7: Performance of the Ge-HATR based model using a restricted range and

the performance of the FOP calibrations 101

Table 4.8: Table of dilution factors and calculated concentration for the chocolate

syrup samples used as calibration standards 123

Table 4.9: Summary of the performance of the optimized chocolate syrup calibration

models based on diluted chocolate syrup samples 132

Table 4.10: Comparison between predicted Total Sugar content and sum ofpredicted

concentration for individual sugars , 136

Table 4.11: Test results for samples of original, new and dark syrup formulations

predicted employing the water dilution based calibration models 141

Table 4.12: Summary of the HPLC error ranges 147

Table 4.13: FTIR-prediction for 5 samples used to evaluate the accuracy of the

method 148

Table 4.14: Error on precision for chocolate syrup analysis by FTIR.-FOP and HPLC

based on predictions of ten repeats 151

Table 4.15: Summary of the one-way ANOVA testing for same day reproducibility

.............................................................................................................. 152

Table 4.16: Summary of the one-way ANOVA testing forday-to-day reproducibility

.............................................................................................................. 153

Table 4.17: Summary of the one-way ANGVA testing for week-to-week

reproducibility 154

xxii

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•

Table 4.18: One-way ANOVA test results for the decreased analysis time evaluation

.............................................................................................................. 157

Table 4; 19: Gne-way ANGVA test results from sample standing test .. , 159

Table 4.20: One-way ANOYA test results for the comparison of purged and non-

purged systems , 161

Table 4.21: Summary of the one-way ANOYA tests for direct predictions 162

Table 4.22: Table ofRMSE calculated for aH three prediction levels 164

Table 4.23: Summary of performance of the Master Calibration in different

transferability scenarios 167

Table 4.24: Summary of calibration and validation errors and correlation coefficients

for the optimized chocolate syrup ANN mode! 186

Table 4.25: Table of eigenvalues, proportion and cumulative proportions from PCA

on the spectra of the ANN sugar solution training set. 196

Table 4.26: Table of comparison between ANN and PLS perfonnance for the

quantitative analysis of sugar in solution 198

Table 4.27: Observed concentration dependent band shift in glucose solution spectra

'" 208

XXlll

•

•

•

Chapter'l: Introduction

1.1. GENERAL INTRODUCTION

Carbohydrates are the most common naturally occurring organic compounds.

In their most primary synthesis, carbohydrates are produced by plants during

photosynthesis (Candy, 1980). With an empirical fonnula of CnH2nOn, carbohydrates

are defined as compounds derived from reduction, oxidation, substitution of one or

more hydroxyl groups as weil as derivatization of hydroxyl groups to various

moieties in the basic carbohydrate molecules (glucose, fructose, sucrose, maltose,

etc... ) (RobY4 1998). This extended definition includes sugar alcohols, sugar acids,

deoxy sugars, amino sugars, oligosaccharides and polysaccharides in the

carbohydrate family of compounds. In recent years, a new subclass of carbohydrates

emerged, when its implications in physiology, medicine and molecular biology were

recognized; this includes glycosilated proteins and / or lipids. An extensive literature

has demonstrated how these molecules hold a primary raie in protein solubility,

folding, turnover, cell-surface receptors, cellular differentiation, and immunological

recognition (Bhatia and Mukhopadhyay, 1998). Through out this document, the

terms carbohydrate and sugar will be used interchangeably.

In the food industry, both basic carbohydrates and larger derivatives are

employed for a wide array of purposes. The most common carbohydrate ingredients

1

•

•

•

used in the food industry are glucose syrups, high fructose corn syrup, liquid sugar,

and granulated sugar. From sweetening agents to texturizers, carriers, bulking agents

and stabilizers, carbohydrates make up an important part of the human diet and

provide 50 to 60% of the calorie intake.

Owing to their importance, carbohydrates have been studied and analyzed to

improve upon their characteristics and to diversify their use in food and foodstuffs.

Conventional time-consuming and labor intensive devised analytical methods for

carbohydrates are now being slowly replaced by newer and faster techniques without

compronlising analyticaI precision. Among those new rapidly growing methods, one

can mention Fourier transform infrared (FTIR) spectroscopy, which provides the

means of studying biological systems at the molecular level and pennit the study of

intra and extra molecular interactions. In the case of carbohydrates, FTIR

spectroscopy may prove useful for the examination of water-carbohydrate as well as

carbohydrate-carbohydrate interactions in food matrices.

1.2. RAnONALE AND OBJECTIVES OF THE PRESENT RESEARCH

The interaction between sugar and water determines both the

physicochemical parameters of foodstuffs (Wong, 1989) and the perception of sweet

taste (Mathlouthi et al., 1996b). Like most chemical compounds, sugars and their

interacting compounds are infrared absorbant. This property can be measured and

used for their quantitative determination. The overall objective of this work is to

2

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•

study carbohydrates, one of the most important components of food, by FTIR

spectroscopy.

The specifie objectives ofthis work are as follows:

1. evaluation of the potential of a chalcogenide based mid-IR fiber optic

probe (POP) accessory for the quantitative analysis sugar mixtures by

FTIR spectroscopy.

2. development of an FTIR.-PLS calibration model to predict sugar

concentrations, total sugars and water content in chocolate syrup using

the mid-IR. chalcogenide fiber optic probe (FOP) accessory.

3. evaluation of the use of artificial neural networks (ANN) for the

qualitative and quantitative analyses of sugars in chocolate syrup and

sugar mixtures.

4. investigation of water~sugar interactions in aqueous solutions of sucrase,

glucose, fructose and maltose using 2D IR correlation spectroscopy.

3

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•

Chapter'2: Literature Review

Carbohydrates are found in many different forms, often as large molecules

such as cellulose, a structural molecule, starch, a plant energy storage molecule, or

glycogen, of mammalian origine Many polysaccharides, which are constituted of

more than 10 monosaccharides, the basic building blacks of carbohydrates, have

important functional properties in both biological and food systems. In biological

systems, carbohydrates allow the living organism to be maintained in a functional

state. In plants, cellulose is the main molecular component of cell walls and thus

plays a major structural role. Across the food chain, carbohydrates are also

recognized as an important source of energy. In mammals for instance, glycogen is

used to store the carbohydrates, which are released through the process of glycolysis

when needed by the body. Sugars are also present on the cell surface as

glycoproteins and provide interaction sites to allow cell-cell recognition or molecule­

cell recognition. These phenomena are the basis of recognition processes in

fertilization, cell adhesion, blood typing through antigens, and bacterial and viral

infections as weIl as hormonal regulation (Lehamann, 1998). Many metabolic

functions depend on the presence of carbohydrates in the cell intracellular fluid for

energy and source ofcarbon. The ultimate source ofsugars in the hurnan body cornes

from the diet. It bas been estimated that carbohydrates provide 50 to 60% of the

calorie intake (Spallholz et al., 1999).

4

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•

•

Before detailing the use of carbohydrates in food, a brief overview of the

structure and basic chemistry of sorne naturally occurring mono- and disaccharides

will be presented.

2.1. SUGARS: STRUCTURE AND BASIC CHEMISTRY

Glucose, fructose, sucrose and maltose are the most common sugars used as

sweeteners in the food industry. Glucose and fructose are the two most common

monosaccharides, while sucrase and maltose are the disaccharides.

Glucose is the only sugar present in aIl naturaIly occurring oligo- and

polysaccharides. From a metabolic standpoint, glucose is a sugar of major

importance because it is used in cellular transport and as energy source for most

cellular processes. Furthermore, most other sugars are first converted to glucose in

their metabolic process (Spallholz et al., 1999). Fructose is a very sweet sugar

naturally occurring in honey and fruits. From a food manufacturing point of view,

glucose and fructose, two reducing sugars, are the most conunon monosaccharide

sweeteners used in the industry in the fonn of corn syrup and high fructose corn

syrup (HFCS) (Stade and Levine, 1996).

Although not present in large amounts in vegetables, fruits and seeds, sucrose

is extensively used in the food industry as an economic food sweetener (Spallholz et

al., 1999), due to the inexpensive and efficient procedures used for its extraction

from sugar cane and sugar beet. Although it has been demonstrated that sucrase

intake is 2 ta 3 folds higher than recommended and that its consumption is linked ta

5

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•

increased dental caries and diabetes, sucrose continues to be excessively used in the

food industry. Maltose is not a naturally occurring disaccharide but is the product of

starch hydrolysis, a major constituent ofmany foods.

2.1.1. Monosaccharides: glucose and fructose

Glucose exists in cyclic and open fonns (Figure 2.1(a». In solution, these

tautomers are in equilibrium. The glucose ring fonnation is the result of a

nucleophilic reaction between a hydroxyl group and the carbonyl group of the

aldehyde. Furanoses and septanoses are fonned during the process but are unstable

due to strain, bond distances and hindrance. However, the pyranose fonn is favored

both in ring fonnation and in the equilibrium with the open form (ratio of 99 to 1)

(Yaylayan and Ismail, 1992). Ring formation confers a chiral character to the carbon

atom of the carbonyl group involved in the reaction. Glucopyranose exists as a and

f3 anomers (Figure 2. 1(a», which have different specifie rotations (a-D-glucose [a] =

+113°; P-D-glucose [a] = +19°). If a solution of either glucose enantiomers is

prepared, a time-dependent change in rotary power, or mutarotation, is observed due

to the existence of the open fonn of glucose, acting as an intermediate between the ex.

and P forms. The reaction starts with an attack on the cyclic fonn of the sugar by

either an acid or a base catalyst, followed by a slow opening of the ring. The final

specifie rotation of +520 reflects the proportion of ex. and p present at equilibriurn:

65% 13, 35% ex.. Mutarotation can be catalyzed in four different ways: at pH 4, at pH

10, using 2-hydroxypyridine, or mutatrotase, which involves carboxylate and

carboxyl catalytic groups at the active-site (EI-Khadem, 1988).

6

• (a)

HC~~OH

/6HHC4 HC=O

1\ OH / 1

HO \13 2HC--CH

1OH

Glucose - Open Fonn n-Glucopyranose p-Glucopyranose

Cb)

• HO

p-D-F~etofiŒr.anose

OH

P-D-Fruetopyranose

(c)

OH

6C~OH

5 0\

/1:-0'-----1 2

4

OH

o

OH

Sucrose Maltose

•Figure 2.1: Chemical structure of (a) glucose (open form, cx.-glucopyranose, 13­

glucopyranose), (b) fructose «(3-n-fructofuranose, (3-o-fructopyranose),(c) sucrose and maltose

7

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•

•

Fructose also exists in cyclic and open fonns. The presence of a ketone

function on C 2 and the absence of an aldehyde group on CI, provides fructose with

a different range of reactions than glucose. Ring fonnation with the open forro will

yield both a fructofuranose and a fructopyranose ring product (Figure 2.1 (b». Each

compound has two enantiomers, namely (X and f3 with characteristics similar to those

of glucose. The four forms exist simultaneously at a ratio of 2.5:65:6.5:25 (X­

pyranose, p-pyranose, a-furanose and p-furanose, respectively, at 31°C with sorne

open chain fonn (0.8%) (Robyt, 1998). Fructose, like glucose, has the capacity to

rotate light, however it does it in the opposite direction (o-fructose [a] = -92.4°).

Fructose can be extracted from natural sources, however, the industrial fructose is

obtained from glucose by means of chemical or enzymatic catalyzed reactions.

Glucose syrups, from which HFCS is produced, result from the hydrolysis of starch

into a mixture of monomers, dimers and oligomers. Several enzymes, each

catalyzing a specific step of the hydrolytic reaction, are involved in the in vivo starch

degradation (Figure 2.2). The in vivo system has inspired an industrial procedure that

uses enzymes over acid catalyzed hydrolysis to produce corn syrup hydrolyzates

(Rob~ 1998). The production of HFCS makes uses of D-xylase, commonly known

as glucose isomerase, to convert glucose into fructose (Robyt, 1998).

Reducing carbo~ydratessuch as glucose and fructose cao undergo an array of

reactions involving their carbonyl groups. The Maillard reaction, known as non­

enzymatic browning reaction, describes the interaction between reducing sugars and

amino acids or proteins and is one of the most important set of reactions in food and

beverage preparation (flavoring of whiskey for example) (potter and Hotchkiss,

1995). This is the basis for browning and aroma-flavor fonnations associated with

heated foods.

8

•

(1)

(2)

S tareh

dextrins+

unreaetedstare h

salivary .a-amylase· G 2 +G J + G 4 + 8 4 + B s + dextrms +unreaeted stareh

panereatie. G2

+ G] + G 4 +B 4 + B sa-amylase

(3 ) G 2ex -1 ,4.glueosidase 2 G l

(4 ) G]a-l,4-glueosidase

G l + G 2a-l,4-glueosidase 2 G 1

(5) 8 4a-l,6-glueosidase

G 1 + G]a-l,4-glueosidase

3 G.

• (6 ) B sa-l,4-glueosidase Gr + 8 4

a-l,6-glueosidaseG. + G 3 -•

3 G 1a-l,4-g1ucosidase

Figure 2.2: In vivo enzymatic hydrolysis of starch

Gn : chain ofn glucose mo/ecules

Bn : Branched chain ofn glucose mo/ecu/es

Source: (Robyt, 1984)

•9

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2.1.2. Disaccharides: maltose and sucrose

Maltose (Figure 2.1 (c» is a disaccharide resulting from starch hydrolysis or

from glucopyranose hemiacetal condensation. As shown in Figure 2.1(c), the

glycosidic bond is formed between CI of one glucose residue and C4 of the other

residue. Consequently, with one chiral carbon free, maltose cao exist as a and p

enantiomers. Many of the maltose reactions are similar to those of glucose by the

fact that the reactive part of the molecule is glucose.

Sucrase (Figure 2.t(c», the technical term given ta cane and beet sugar, is

probably the most important sugar in the food and beverage industry as it is the

precursor for most fonus of sugar components used in manufactured and processed

foods. 115 reactivity is limited by the fact that the condensation of glucose and

fructose at Cl and C2 respectively blocks the active carbonyl groups of the

monosaccharides moieties rendering the disaccharide non-reducing. Sucrose has

many important physical properties, sorne of which are used to evaluate the

concentration of sugars in solution. Light refraction (gjving rise to the refractive

index) is measured using a refractometer and relates directly ta the concentration of

sucrase. The same concept applies to optical rotation ([a] = +66.53°) and viscosity

(pomeranz and Meloan, 1994). Most often, the concentration of sucrose is reported

in Brix scale, which relates the percentage by weight of sucrose in water solution.

Brix reading is normal1y used to obtain the corresponding specifie gravity or

refractive index of a solution of pure sucrose and water at 200 e (pomeranz and

Meloan, 1994). The Baumé scale, a lesser used index, is another specifie gravity

10

•

•

•

ratio, which measures uapparent solid" or "apparent density" in solutions containing

substances other than pure water and sucrose. This value may differ from the "true"

solids content depending on the type and amount of impurity (DowIing, 1990).

Hydrolysis of the glycosidic bond of sucrose is commonly referred to as

"inversion reaction" and the resulting glucose-fructose mixture is called "invert

sugar". The term "inversion" refers to the change in optical rotation observed when a

solution of pure sucrase undergoes hydrolysis. Effectively, the optical rotation of the

solution goes from dextrarotatory (rotates plane of polarized light to the right) to

levorotatory (rotates the plane ofpolarized light to the left).

A prime source of naturally occurring invert sugar is honey. The inversion

reaction is accelerated by decreasing the pH, increasing the temperature or by use of

enzymes, the most notable of which is invertase (Dowling, 1990). In acidic solution,

sucrase will breakdown into its monosaccharide components. This reaction is the

start point for many sucrose reaction pathways in carbohydrate chemistry as weIl as

in the human metabolism.

The increase in sweetness during inversion is directly related ta the relative

sweetness of sugars (Table 2.1). Sweetness steadily increases until about 50 to 85

percent inversion is reached. Invert syrup, also known as liquid sugar, is commonly

used as a sweetening agent because for a similar volume of sugar, the invert syrup is

sweeter and more stable than sucrase, glucose or fructose alone in solution. A prime

example of invert sugar use is found the soft drink industry, which is at the ongin of

the invert syrup development (Marov and Dowling, 1990).

Il

•Table 2.1: Degree of sweetness relative to a 10% aqueous sucrose solution for

different sugars and sugar alcohols

•

Sugar

Sucrose

p-o-Fructopyranose

p-o-Glucopyranose

a-o-Glucopyr.anose

p-o-Xylopyranose

a-o-Galactose

p-o-Galactose

a-o-Mannose

p-o-Mannose

a- Lactose

p-Lactose

p-Maltose

Raffinose

D-Gluticol

Malitol

Lactitol

Cellobitol

Relative Sweetness

(10% aqneous solution)

1.00

1.80

0.82

0.74

1.80

0.32

0.21

0.32

bitter

0.16

0.32

0.32

0.01

0.50

0.63

0.34

0.11

•Source: (Knecht, 1990;Robyt, 1998)

12

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•

2.2. CARBOHYDRATES IN FOODS

AlI the carbohydrates present in the human body ultimately find their origin

in the ingested food. In the food industry, both simple (monomers and dimers) and

complex (oligomers and polymers) carbohydrates and larger derivatives are

employed for a wide array of purposes ranging from sweetening agents to

texturizers, and carriers to bulking agents and stabilizers. Carbohydrates are most

cornmonly found in the food industry as glucose syrups, high fructose corn syrup

(HFCS), liquid sugar, and granulated sugar (i.e. sucrose). The detailed chemistry

behind the key functionalities of mono and oligosaccharides present or used in food

can be found elsewhere (Wong, 1989).

2.2.1. Key functionalities of sugars in food

2.2.1.1. Sweetening agents

Sugar in naturally occurring foods and manufactured good plays a number of

major roles besides its well-known sweetening ability. Although most

monosaccharides and their sugar alcohols demonstrate sweet taste, only sucrose,

glucose and starch syrup, maltose and malto-oligosaccharides, are actually used as

sweetening agents. Also, research has shown that sorne D-amino acids (Trp, Hid,

Phe, TYr, Leu, Gly and Apn) and proteins such as rnonellin, which is found in red

berries, possess a sweet taste (Wong, 1989).

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2.2.1.2. Texturizers

Peetins and gums are large carbohydrate molecules, with a glucose backbone,

that have a great ability for gel formation. Pectins are used as structural components

in jams and confectionery. Gums are highly branched molecules that are extracted

from a wide array of sources. They aIl have in common the ability of fonning gels

that retain their plasticity even at very high gum concentration (common

concentration range: 0.1-0.3%) (Lineback and Inglett, 1982). They are often used as

thickening agents as a substitute for starch, as emulsion stabilization and as texture

usmoothening" agents in products such as ice cream. Uses ofgums are advantageous

as the gels formed are easily disrupted by physical stress such as the pull of gravity

when pouring and the phenomenon is completely reversible (Doublier and Cuvelier,

1996).

Starch is the ooly major plant polysaccharide that is readily digested hy

mammals. It can he naturally found in cereals, grains and tubers under two main

chemical structures: the linear amylose and the hranched amylopectin. Starch is used

as thickening agent in beverages and frozen food fillings.

2.2.1.3. Flavor development

The close presence of sugars and amino acids in the fonn of proteins in food

inevitably leads to the Maillard reaction when the food is heated. The Maillard

reaction, also known as non-enzymatic browning, describes the chemical pathway of

this sugar/amino acid reaction (Lehamann, 1998). The Maillard reaction is also an

important color and flavor development step in naturally occurring foods such as

date and honey.

14

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•

Chocolate is one of many confectionery products that relies to a great extent

on the Maillard reaction for taste, flavor, and sorne color development.

Monosaccharides present in the cocoa beans, upoo roasting, undergo the Maillard

reaction with the amino acids present in the bean to fonn flavor precursors (Morgan,

1994).

2.2.2. Sugars in chocolate

The sources of sugars in chocolate are multiple. However, the primary ones

are liquid sugar, invert syrup and corn syrup. In sorne products such as chocolate

syrup, HFCS cao aIso be used as a formulation ingredient (Kruger, 1994). The sugar

contents of the chocolate depends on the type of chocolate produced and on its

subsequent use (Pontillon, 1998). The formation of a bar versus a fondant requires

different sugar contents in order to satisfy rehological and S1.veetness characteristics

in the final product.

In the case of chocolate syrup, viscosity is the primary property. As a water­

based confectionery product, a thickening agent, often a gum, is added to obtain the

necessary flow when the syrup is poured. The adequate composition of the sugar

profile will insure that the syrup has the appropriate taste and texture.

Consumer acceptance is the most important criterion in the development of a

formulation (Nuttall, 1994). It is therefore not surprising that the sugar profile of a

manufactured food can change with the availability and the price of the primary

constituents. The extent of the variation depends not ooly on consumer acceptance

but aiso the legislation put in place to prevent frauds and to ensure the safety of the

food product (Vetter, 1996).

15

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2.3. CURRENT METRons OF SUGAR ANALYSIS

There are over 10 different methods accepted by the AOAC for the analysis

of carbohydrates in food. These can be subdivided into three distinct classes: 1)

polarimetric and refractometric, 2) colorimetrie and enzymatic and 3)

chromatographie.

2.3.1. Physico-chemical and enzymatic assays in the analysis of

carbohydrates

Polarimetry is widely used in the sugar cane industry (Cadet et al., 1991) and

is based on the ability of sugars in a solution to polarize light. The extent of the

polarization is measured and related to the sugar concentration through tables. This

method measures the concentration of aIl the optically active compounds without

discrimination. Although rapid, this technique is most useful when no interfering

compounds are present in the sample (Pomeranz and Meloan, 1994).

Refractometry, like in polarimetry, measures the magnitude of the change of

plane of the light in a solution due to the presence of sugars. The change is directly

related to the concentration of individual or total sugars in solution. Each sugar

reflects light ta a different extent depending on its chemical composition and

concentration. As a drawback, refractometry measures total solids content, and not

total sugar contents.

There is an array of enzymatic methods for the analysis of sugars, each

dedicated to one class or one type of carbohydrate (Chaplin and Kennedy, 1994;

Kennedy and Pagliuca, 1994), which makes use of the specificity of an enzyme ta a

16

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•

substrate. Often, enzymatic methods are combined to a colorimetrie method to detect

the end-products of an enzymatic reaction. Glucose peroxidase, for instance, is

utilized for the specifie detennination of glucose by UV or visible

spectrophotometers at 505 to 520 nm, or by titration. These methods are time

consuming and prone to human error. Another common colorimetrie method utilizes

neucuproïne and curpic sulfate for the determination of reducing sugars through the

production ofchromophors that absorbs at 460nm (Chaplin, 1994).

2.3.2. Chromatographie methods for the analysis of carbohydrates

Chromatographie methods have become very common in many laboratories

for a wide array of analyses. Individual carbohydrates can be determined by thin­

layer, paper, liquid or gas chromatography. Here we will hriefly discuss high

perfonnance liquid and gas-liquid chromatography, the two quantitative methods in

this series.

High-perfonnance liquid chromatography (HPLC) has been extensively

employed for sugar separation in food analysis (Hurst et al., 1979). In many cases, it

has become a routine method for the analysis of simple monosaccharide mixture and

oligosaccharide analysis and their purification (Chaplin and Kennedy, 1994;

KeIUledyand Pagliuca, 1994). HPLC has the advantage ofbeing specifie and able to

separate components efficiently.

In the late 70's and early 80's, an HPLC procedure using a refractive index

detector for the analysis of carbohydrate in chocolate products was developed (Hurst

and Martin Jr, 1977;Hurst et al., 1979). The protocol has undergone collaborative

studies (Hurst and Martin Jr, 1980) and is now a fully recognized method for

17

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•

carbohydrate analysis (AOAC 31.5.04). Although, HPLC has been considered as the

greatest advancement in component separation, the method has a few inherent

problems. HPLC analysis requires extraction (Li, 1998) and in sorne cases such as

carbohydrate analysis, derivatization of the separated components is needed for their

detection. The lack of precision reported by Rouch and co-workers (Rouch et al.,

1995) in sugar cane juice analysis, remains in sorne cases a concem when using

HPLC.

Gas Liquid Chromatography (GLC) has a very high sensitivity and is

generally less prone to interferences. GLC separation is based on the differential

extractive distillation of the components in the mixture and thus requires

derivatization to produce volatile compounds. Detection is usually by means of

flame-ionization detector (FID), which has an extremely wide linear range (Chaplin

and Kennedy, 1994).

2.4. FOURIER TRANSFORM INFRARED (FTIR) SPECTROSCOPY

2.4.1. Principles of FTIR spectroscopy

The electromagnetic spectrum of a radiation can be subdivided into a number

of regions based on distinct characteristics and frequency domains. Infrared

spectroscopy deals with the interactions between radiation of 1014 to 10ll Hz

frequency (100 cm·1 to 10000 cm·l) and matter. Molecules can undergo a nurnber of

vibrations such as stretching, bending, rocking, wagging, twisting, defonnation, and

breathing (rings only). In order for an infrared absorption to occur, the vibrations

must induce or enhance a change in the dipole moment of the molecule, hence only

18

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•

frequencies of radiation equal to the vibrational frequency of the molecule's covalent

bonds can be absorbed (Ismail et aL, 1997).

2.4.1.1. Conventional IR

The tirst generation of infrared instruments employed monochromators

containing a dispersive element to resolve the different wavelengths of the incident

light. Initially, prisms were used as dispersive elements, until grating

monochromators became more popular. Within the setup, slits were used to limit the

radiation reaching the detector to a one-resolution width. Slit width could be

mechanically or electrically controlled to compensate for the variation of source

energy with wavenumber. Dispersive instruments, although still available, suffer a

number of limitations mainly associated with length of acquisition time, poor

resolution and relative size and cost. In food systems in particular, these

shortcomings were enhanced by the poor transmission and the high scattering nature

of food matrices (Carballido Estevez et al., 1974).

The next generation of spectrometers remedied most of the above limitations

by replacing the monochromator with the Michelson interferometer in Fourier

transfonn infrared spectrometers.

2.4.1.2. Fourier transform infrared (FTIR) spectroscopy

FTIR spectroscopy uses interferometry to study the interactions between

electromagnetic waves and matter (BamweIl, 1983). Interferometry is based on the

interference of two beams to produce an interferogram, a signal detected as a

function of pathlength difference between the two beams (Stuart, 1996). The time

19

•

•

•

domain so produced IS converted to a frequency domain using the fast Fourier

transfonn.

The use of the Michelson interferometer provides FTIR spectroscopy with

three advantages: the FeIIgett (or multiplex), the Jacquinot (or througbput) and the

Connes (or wave accuracy). The use of a multiplex analyticaI instrument that detects

simultaneously aIl the elements of the signal emitted by the source throughout the .

measurement makes the data collection faster and more comprehensive. This is

known as the FeIIgett advantage. The Jacquinot advantage states that since an

interferometer has no slits, there is no corresponding attenuation. The combination of

the Fellgett and the Jacquinot advantages enhance FTIR. efficiency in situations

where the amount of sample available is minute or when light transmission through

the measured system limits sample detectability (Charalambous, 1984). The Connes

advantage is based on the use of a He-Ne laser as reference beam to increase

wavelength precision and accuracy. The combination of these advantages provides

FTIR spectroscopy with the appropriate stability and sensitivity required for an

analytical tool.

2.4.1.3. Overview of FTIR spectroscopy in the mid-m. region

The infrared light can be subdivided into three main parts: the near infrared

(NIR) which extends from 10000-5000 cm-l, the mid-infrared from 5000-400 cm-l,

and the far infrared from 4000-10 cm-l.

In the pas!, mid-IR. (5000-400 cm-I) has been left aside by food analysts due

mainly to the sampling and instrumental limitations associated with conventional

spectrometers using diffraction grating (Wilson and Goodfellow, 1994). Most foods,

20

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•

in their original fonn, are not suited for infrared anaIysis because they contain large

amounts of water, which absorbs greatly in the mid-IR region. The convenience and

efficiency of FTIR spectrometers as a routine infrared analyticaI tooi renewed the

interest of the industrial world in infrared spectroscopy, more 50 that the size and

cost of instruments have dropped dramatically.

A mid-infrared spectrum can° be roughly subdivided into four regions

covering different characteristics. Vibrations in the 4000-2500 cm-1 region is

attributed to X-H stretching, where X can be carbon, nitrogen or oxygene The 2500­

2000 cm-I and the 2000-1500 cm-1 regions are respectively associated with triple and

double bonds stretching. FinaIly, the 1500-600 cm- I region is thefingerprint region

where similar molecules give unique absorption patterns (Wilson and Goodfellow,

1994). In generaI, band assignment of functional groups is straightforward owing to

well-resolved absorbance bands in the mid-IR region. The identification of discrete

bands facilitates quantification, allowing the use of the simple Beer-Lambert Law to

correlate peak height or area to the parameter of interest. Owing to clear band

assignments, FT mid-IR. can aIso provide infonnation about physical and cheinicai

states of individual components. Yaylayan and co-workers (Yaylayan et al.,

1993;van de Voort et al., 1994b), for instance, were able to employ FTIR

spectroscopy to monitor the effect of pH and temperature on changes in the

concentration of the keto form of D-fructose. In a more applied approach, van de

Voort and co-workers (Yaylayan et al., 1993;van de Voort et al., 1994b) monitored

the thermally induced oxidation ofedible oils by mid-FTIR spectroscopy.

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Although FT mid-IR is amenable to quantitative wode, it suffers a major

limitation: water absorbance. Indeed water absorbs greatly between 1550 and 1700

cm-1 as weil as between 2800 and 3600 cm-l, thus masking important information

such as the Amide 1 and II bands, essential for the analysis ofproteins. In sorne cases

the broadening of the water bands due ta temperature fluctuations cao prove

detrimental to the analysis.

2.4.2. Sampling accessories employed for recording infrared spectra

The choice of a sampling accessory is directly related to the purpose of the

analysis and ta the type and physical state of the sample.

2.4.2.1. Transmission cell

In transmission methods a beam of light is passed through a sample, and the

intensity of the transmitted radiation is detected and compared to its initial intensity.

In practice, the incident light is partly scattered, reflected and transmitted (Smith,

1996). Consequently, for highly scattering samples, transmission is not adequate.

Liquid analysis makes use of a flow-through cell constituted of two windows

separated by a spacer to provide the desired pathlength. Interference fringes are

produced in flow-through transmission cells with narro\v pathlength, masking

important peaks and consequently impairing the accuracy. In many cases, using

spectral subtraction cao overcome the problem of fringes; although such spectral

manipulation can itself lead to added artifacts (Crocombe et aL, 1987). Although the

use of transmission cells for very viscous or semi-solid samples is impossible in a

flow-through configuration, a demountable cell may be used (Stuart, 1996).

22

• 2.4.2.2. Horizontal attenuated total retlectance (HATR)

InternaI reflectance, also known as attenuated total reflectance (ATR), is

achieved by using a high refractive index prism made of infrared transmitting

material cut at an angle 50 that the light enters undeflected and reaches the crystal-

sample interface at a predetennined angle, al (Figure 2.3). The angle of entry must

be superior to the critical angle ac (Urban, 1996). The minimal angle at which total

internaI reflectance is produced, is defined as:

Eq.l.l

•where nI = refractive index ofthe surrounding medium

and nl = refractive index ofthe crystal

The evanescent wave produced by the incident IR. radiation at the interface is

attenuated at each reflection, or bounce, and the total attenuation is measured by the

detector at the exit. Given the 1f1m depth of penetration of the IR radiation for this

type of sampling accessory, a good contact between the sample and the crystal is

crucial to the quality of the final spectrum.

The generalized depth of penetration ofan A TR is defined as:

•dp = ÎJ(2rc Irn(ç)Û

where Â.: wavelengthç: imaginary component

Eq 1.2

23

•

• Ta theDetectar

\

\\

"'~

isamp,e

•••••••••••••••••~ CrystaVSampleInterface

•Figure 2.3: Schematic representation of an attenuated total internai reflectance

(ATR) sampling accessory

•24

• For transparent samples, the depth ofpenetration equation becomes:

Eq 1.3

•

•

2.4.3. Selected mathematical tools for the analysis of iofrared

spectra

The wealth and complexity of the infonnation obtained by· infrared

spectroscopy requires powerful mathematical tools to relate the recorded data to the

parameters under study. The qualitative and quantitative methods used in the work

presented are reviewed in this section.

2.4.3.1. Two-dimensional infrared correlation spectroscopy

Two-dimensional correlation applied to infrared (2D IR) spectroscopy is a

mathematical method used to extract information regarding the effect of an external

perturbation on a system. Data collected as the external perturbation is applied, is

tenned dYnamic spectra. Although, an electrical and a pulsed magnetic field are

typical examples of perturbation, the application of increased temperature or the

effect of time are aiso considered to be perturbations if changing these parameters

will affect the molecular structure of the system under study.

Typicai changes occurring in an IR spectrum are intensity variation, band

shift and changing in directional absorption. These changes can be correlated to the

applied perturbation by application of 2D IR correlation spectroscopy if a

perturbation effects the system in a unique and selective way. In effect, 2D IR

correlation relates a macroscopie stimulus to a molecular change (Noda, 1993). This

25

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new analytical approach is based on the concept of dipole-transition moment. Each

functionaI .group has its own dipole-transition moment, which is influenced by its

enviromnent. Two types of correlations derived from the synchronous and

asynchronous functions oÏ the complete cross-correlation function are calculated in

order to understand the relationship between the environmental disruptions

introduced and the molecular changes (Noda, 1997).

The synchronous function, also known as coincidaI function, gives

information regarding the coupling of different functional groups while the

asynchronous function, aiso known as quadrature function, measures the relative

speed between the groups that reorient simultaneously. The resulting calculations

yield data that can he graphed in three-dimension and collapsed into 2D images

tenned synchronous and asynchronous maps.

2D IR spectroscopy can be employed as a tool to investigate the interactions

between different Molecules (McClure et al., 1996). In the work presented, 2D IR

correlation spectroscopy is applied for the investigation of the interactions between

water and sugar Molecules.

2.4.3.2. Principal component analysis

Principal component analysis (pCA) is based on finding the right set of

weighted linear combinations of the studied variables to model a system (ManIy,

1994). Each linear combination results in an index, cornmonly known as principal

component. The calculated principal components are uncorrelated and thus measure

underlying dimensions in the data.

26

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•

Principal components are ordered in decreasing variance, which is described

by the eigenvalue that is calculated from the covariance matrix of the original data. If

p variables are studied, then p eigenvalues are calculated and thus a maximum of p

principal components can be found.

An important property of eigenvalues states that the sum of aIl the

eigenvalues is equal to the sum ofthe variances associated with the studied variables.

In effect, the total variance of the system is accounted for by the overall variance of

the principal components. When the original data is highly correlated, the fust few

principal components are sufficient to describe more than 99% of the system's

variability.

Practically, calculating the principal components will result in a data

compression without variability loss and with the added advantage of integrating

redundant infonnation. One can therefore use PCA to cluster data with similar traits

that may not he easy to detect. Given that each principal component is representative

of a specifie characteristics in the set of standard, classification based on a specifie

principal component is equivalent to classifying the data based on a particular trait

(Manly, 1994).

In this study, PCA was used as a data reduction step for the artificial neural

network based quantitative analysis of sugars in solution.

2.4.3.3. Partial-Ieast-squares

Partial-least-squares (PLS) IS a multivariate analysis technique based on

linear regression. ft combines the advantages of c1assical least squares (CLS) and

inverted least squares (ILS) (Thomas, 1994).

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CLS requires that ail the components in the modeled system be identified and

their exact contribution known because it is based on the sum of the contributions of

the components at given wavenumbers. CLS therefore attributes any absorbance due

to ~ unidentified source as a contribution from one of the known components.

ILS, on the other hand, expresses the concentration as a function of a set of

frequencies. Although this solves the problem of the contribution from unknown

sources of interference, ILS suffers from inherent mathematical limitations. For

instance, the number of frequencies cannot exceed the number of calibration

standards in the training set. Furthermore, any co-linearity will extensively limit the

selection offrequencies (Thomas, 1994).

PLS, which combines the above two methods, is a factor analysis method that

relies on the decomposition and the compression of the spectral data into a set of

mathematical spectra representative of the whole system, these are called loading

spectra or factors. The spectrum of each calibration standard is then decomposed into

a weighted sum of the loading spectra, and the weights given to each loading

spectrum are known as scores. When presented with an ~\mknown", a linear

combination of the factors is used to reconstruct the spectrum of the sample and the

scores are used for prediction. A detailed outline of the PLS protocoi has been

published by Haaland and Thomas (Haaland and Thomas, 1988).

Although PLS does not suffer from the limitations of ILS and CLS, two

problems can be encountered: 1) overfitting and 2) underfitting. In the former, too

Many loading spectra are used and therefore noise will be modeled into the system

since the spectral variability associated with the concentration is localized in the tirst

28

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few loading spectra. Underfitting, is the opposite phenomenon where too little

loading spectra are used. In bath cases, calibration results could seem acceptable,

even very good, but the prediction performance on an extemal validation set would

be very poor. Ta find such weaknesses, one can use a set of standards not included in

the calibration model to validate the performance of the calibration model.

Alternatively, one can use the leave-one-out cross-validation procedure. This method

is based on performing the calibration, as many times as there are standards while

leaving a different standard out each time. The errors on the predictions obtained by

cross-validation are expressed as root mean square error (RMSE) and are used to

compute the predicted residual error SUIn of squares (PRESS). The plot of the

PRESS as a function of the number of factors employed in the calibration gives the

optimum number of factors at which the model error is minimized. The RMSE is

calculated as follows:

n

•

RMSE = [(~ (ct - ct,)2)/(n-l)]~i=l

where n = number ofsamp/es

and Ci = actual concentration

and Ci ' = ca/cu/ated concentration.

Eq.2.1

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•

The main advantage of the PLS calibration lie in its mathematicaI ability to

deal with large and complex sets of data. Although PLS can use the whole spectrum

for calibration purposes, it is more efficient to restrict the regions of the spectrum

showing the highest variability. This not only reduces the processing time and

storage space, it improves the precision significantly. Furthennore, PLS is able to

compensate for unidentified contributions because, as ILS, the concentration is

treated as the independent variable. In addition, the powerful reduction capabilities

of PLS allow for establishing relationships between quality attributes or

physicochemical properties and spectral data.

PLS is currently the most commonly used method for quantitative analysis of

systems where a linear correlation between the components and the monitored

parameter exists. It is also a very usefuI tool for studying highly overlapping bands,

as is the case in the mid-IR spectra of sugar mixtures in solution. In this work PLS is

the muitivariate analysis algorithm employed in the analysis ofchocolate syrup.

2.4.3.4. Artificial neural networks

Artificial neural networles (ANN) is a new modeling approach that can he

applied to very complex systems. ANN were developed to mimic the learning

patterns of the human brain and thus to develop the potential ability to solve complex

problems. A well-developed ANN should be able to learn from experiences and use

acquired knowledge to solve problems never encountered before. To gather

infonnation, ANN's neurons work in paraUel to extract patterns from a set of data,

just as the human brain does. The input information, the neuron layer or hidden layer

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where most of the uthinking" takes place, and the output results or conclusions are

the three fundamentallayers ofan ANN (Figure 2.4). The leaming process can either

be supervised by feeding the network a number of actual known values or

unsupervised by lettÏng ANN identify relevant infonnation in the data. In the latter,

the neural network (NN) looks for patterns in the training set and classifies the

samples accordingly. In this case, only a qualitative analysis is achieved. The

connection between the different layers depends solely on the architecture chosen by

the analyst. Table 2.2 lists the most common architectures and their characteristics

(Liu et al., 1993). Currently, the use of the backpropagation networks, which

function by adjusting the connection weights in order to minimize the final error by

comparing the output -to the input values, is widespread, owing to their ability ta

learn complex decisions and build arbitrary non-lïnear boundaries between input and

output layers.

In this work, ANN is used both as a qualitative and a quantitative method for

analyzing infrared spectra. First, ANN used as a classification approach to

distinguish between the different types of chocolate syrups analyzed. Second,

although it has not been extensively used as a quantitative analysis method, ANN

will be employed to predict the concentration of sucrase, glucose and fructose from

the mid-IR spectra of sugar solutions.

Although the training of a neural network (NN) may take several hours, once

the problem is ulearned", the weights are stable and execution becomes very rapid.

NN keep internaI representations ofextemal patterns in the hidden layer and thus,

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Figure 2.4: Diagram of the three fundamentallayers of an ANN

32

• Table 2.2: Most common Artificial Neural Network architectures and their

characteristics

•

•

Architectural

Type

BackpropagationNN(BPN)

Jordan Elam NN

Probabilistic NN(PNN)

HopfieldNetwork

WardNN

JumpConnectionNNGeneralRegression NN(GRNNlKohonen Self­OrganizingFeature Maps

•••

••

•

•

•

•

•

•

•

•

•

•

••

Characteristics

Most widely used

Very suited for chemometrics

Functions bv adiustinJ!; the connectionweiJ!;hts in order to minimize final error bvcomoarin2 outnut ta the inDut values"Recurrent Network"

Hidden laver and input laver send feedbackto the input layer

Weil suited for time series

Simple architecture where patterns areclassified by a smoothing factor

Cannot be used for continuous values foroutoutSelf-organizing system

Each node is connected to every other nodeand to it self

No effective laver exists~ ail nodes receiveinformation from the environment

Uses associative memory: the stored patternthat is the c10sest to the input will heoutouttedOutput laver can spot linearitv in input dataand detect features found bv each hiddenlaverEach laver is connected to ail the others~

thus views the features detected bv otherlayers

Each laver is connected to the next. Simpleand straight-forward system

Unsupervised NN

Finds comman patterns and classifies samplesaccordin21v

Schematic

Representation

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when a pattern is completely or partially recogniz~ the appropriate response is send

on to the next layer, providing the NN with the capacity to deal with unseen or

incomplete data. The power of ANN aIse lies in its ability to discover new

relationships among the input data, even based on reasoning that may be unclear or

completely subjective (Cartwright, 1993).

2.4.4. Food-related applications of FTIR spectroscopy

The use of FTIR spectroscopy to analyze food components is very attractive

and represents a rapid and often non-destructive alternative to the labor intensive

conventional methods. Although a number of attempts have been made to analyze a

wide array of food and food components by FTIR. speCtrOScoPY, the analysis and

monitoring of fat in foods and the analysis of milk are the two most prominent

examples ofsuccessful application ofFTIR spectroscopy in the food industry.

FTIR-based methods have been investigated and successfully applied for the

determination of fat in foodstuff (Cronin and McKenzie, 1990). Van de Voort and

co-workers (van de Voort et al., 1993) have applied FTIR spectroscopy for the

analysis of high-fat products with moisture detennination, aIl in one step, a task,

which would have required independent tests with the conventional approach. The

determination of free fatty acids (Ismail et al., 1993), as weIl as cis and trans content

(van de Voort et al., 1995) in fats and oils was carried out by FTIR spectroscopy.

The monitoring of ail quality was also the subject of several publications with

regards ta oxidation (Dubois et al., 1996; Sedman et al., 1996; van de Voort et al.,

1994b), and the rapid determination of several important ail quality characteristics

34

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(Ma et al., 1997; van de Voort et al., 1994a). An attempt at quantifying oil in water

emulsions by FTIR. spectroscopy (Kemsley et al., 1994) has demonstrated the

influence of the particle size on the accuracy of the analysis. FTIR spectroscopy was

used to successfully detect and classify adulterated oils (Lai et al., 1994); however,

the problem of this approach lies in the necessity to have samples FROM a very wide

range ofadulteration products in order to classify unknowns.

Mille analysis is the tirst successful routine quantitative analysis by filter­

bared mid-IR spectrometers applied to an industrial product. The use of multivariate

analysis in the analysis ofFTIR spectra allows for the simultaneous detennination of

fat, protein and lactose content in homogenized rnilk. Conventional analysis of rnilk

would have required using three different methods: Kjedhal for protein, Babcock or

Majonnier for fat and HPLC or polarimetry for lactose. The results obtained by van

de Voort et al. (van de Voort et al., 1992) showed that the FTIR-based method met

AOAC standards for milk analysis.

In both the fat/oil and mille analyses, the spectroscopie methodologies

developed are now commercially available. They provide a rapid non-destructive

method of analyzing food samples.

2.4.5. Analysis of carbohydrates by FTIR spectroscopy

The analysis of carbohydrates by infrared spectroscopy can be traced back to

the beginning of the 20th century (Coblentz, 1906) when the absorption bands

characteristics of the anomeric configuration in the soIid state were identified. Near

infrared spectroscopy was originally used to study sugar solutions because the OH

stretching band in the mid-infrared results in a broad band with a very high intensity

35

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•

(Back et al., 1984). The advent of the FT technology combined with the development

of ATR accessories, and new mathematical techniques to resolve highly overlapped

bands has allowed spectroscopists to regain interest in mid-infrared (Wilson, 1994).

Pioneering investigation of the Binningham School and the extensive work done by

the US National Bureau of Standards led to earlier reports on infrared band

assignments for sugars (Mathlouthi and Koenig, 1986) (Appendix 1).

2.4.5.1 Physico-chemical studies of carbohydrates by FTIR spectroscopy

Traditionally, polarimetry and NMR were the main techniques used to study

the structural characteristics of carbohydrates in solution (Back and Polavarapu,

1983). Infrared spectroscopy has also emerged as a method ofchoice for the study of

carbohydrates, particularly fructose, glucose and sucrose.

1. Monitoring ofmutarotation by FTIR spectroscopy

Based on the fact that the infrared spectra of anomers are different (parker

and Ans, 1966), mutarotation, one of the most basic chemical reactions of glucose

and fructose, was extensively studied by infrared spectroscopy. The mutarotation

values were calculated early on from infrared data (parker, 1968) but the limit of

detection of infrared method did not allow for the detection of the Ieeto fonn, which

represents less than 1% of sugar concentration at equilibrium (Angyal, 1984). This

perspective changed when Yaylayan and Ismail (Yaylayan and Ismail, 1992) proved

that FTIR had the sensitivity to detect the absorption of the carbonyl group of the

acyclic fonn of fructose. The band at 1728 cm-1 was assigned to the Ieeta fonn.

Further work allowed the group to study the effect of pH and temperature on the

presence of keto forro (Yaylayan et al., 1994). They aIso showed that the

36

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concentration of the open-chain fonn were higher by FTIR than previously reported

data employing NMR.. This difference was attributed to the fact that FTIR accounts

for aIl the open-chain fonn rather than just the 2-keto open-chain fonn (Yaylayan

and Ismail, 1992). FTIR. spectroscopy can also BE applied to study simple and

complex mutarotation (Back et al., 1984). As for all sugar reactions, mutarotation is

affected by the environment in which the reaction is taking place (Robyt, 1998). This

interaction between the sugar and its solvent has been shown to have a tremendous

impact on the infrared profile of the studied compound.

2. Evaluation ofsugar-water interactions by FTIR spectroscopy

The 10ss ofcrystallinity of solid sugars when put into solution is translated by

an increased similarity between the infrared profiles of the different sugars despite

the fact that the profiles of the crystal sugars are very different (Reeves III, 1994).

The interaction between sugar and water occurs through a complex web ofhydrogen

bonding between the water and the sugar molecules. AIthough infrared has proven ta

be one of the most suited methods for the analysis of water interaction with other

compounds (Marechal, 1996), it suffers several limitations. First, water absorbs

greatly in the mid-infrared region. Furthermore, there are great difficulties associated

with subtracting the spectrum of water from that of a solution (Max et al., 1998b;

Max et al., 1998a; Rahmelow and Hübner, 1997). The difficulties are attributed to

the hydrogen bonding between water and the solvated compound although studies in

the NIR could not identifY the origin of band shifts observed when the water

proportions change (Reeves ID, 1994; Reeves lIT, 1995c). Comparison of the effect

of water on NIR and mid-infrared spectra indicated that the effect was less severe in

37

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•

the latter although still significant (Reeves III, 1995b; Reeves III, 1996). In mid­

infrared spectra of sugar-water solutions, two maxima were observed at 3459 and

3268 cm-1 (Mathlouthi and Vinh, 1980). When the concentration of the sugar

increased, the peak at 3459 cm-1 shifted towards lower wavelengths indicating that

this band represented the water associated with the sugar Molecules through

hydrogen bonding.

The interaction between sugar and water is related in a complex way to the

hydration level of the sugar, a physicaI property that depends essentially on the

number of equatorial OH groups (Kacurakova and Mathlouthi, 1996) and is specific

to each sugar. For instance, the hydration number of sucrose is 5, and the study at the

molecular level demonstrated that the fructosyl part of the sugar is more hydrated

and thus had a significantly higher number of hydrogen bonds than the glycosyl part

of the Molecule (MatWouthi et al., 1996b); these observations are consistent with the

hydration numbers of fructose and glucose, 3 and 2, respectively. The infrared

profile of a sugar shows significant changes as the concentration increases,

especially in the region of 1300-1200 cm-1 where the asymmetrical defonnation of

the 't(CH2) and the out-of-ring CH defonnation occur (Kacurakova and Mathlouthi,

1996).

The importance of studying sugar-water interactions is two folds: (1) the

chemical interactions May affect the quantitative ability of calibration models based

on sugar solution; (2) sugar-water interaction is known to be part of the CUITent

model of sweet taste perception (Mathlouthi et al., 1996a). Investigation of the

38

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potential effect of the interactions on calibration models in the NIR did not come to

any definite conclusions (Reeves III, 1995a).

3. Assessment ofsugar-sugar interactions by FTIR spectroscopy

Sugar-sugar was not a subject of interest in the scientific field until it was

demonstrated that many cellular processes are based on the interaction between

different sugar moieties on the cell surface (Candy, 1980). Often the sugar moiecules

are covalently bound to a protein or to a lipid, through the process of glycosylation.

Researches however, have been limited to understanding the effect of sugars on the

configuration of proteins or lipids in order to elucidate the pathway of information

transfer from the elements present in the extracellular fluid to the regulation of

internai processes of the cel!.

The study of the sugar-sugar interaction in solution has been limited due to

the complex three-dimensional chemical structure of carbohydrates and their high

intricate hydrogen bonding patterns. Mathlouti and co-workers (Mathlouthi et al.,

1996b) showed that as the concentration of sugar increases in a solution (> 1.8M),

the interaction between sugar and water molecules decreases in favor of sugar-sugar

interactions. This seems to be the extent of the development on the subject for the

past few decades.

2.4.5.2. Analysis of sugars in food by FTIR spectroscopy

The analysis of food by FTIR spectroscopy in comhination with multivariate

methods can he subdivided into two classes: the classification of foods and the

quantitative analysis of food components.

39

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•

1. Food classification

The classification of food has been mainly used to assess the potential of

FTIR to detect adulteration. A substantial amount of work has been done on the

detection of adulterated jarns (Defernez and Wilso~ 1995) and purees (Defernez et

aL, 1995). Both methods were developed based on the sugar and water regions of the

mid-infrared spectrum between 800-2000 cm· l. Interestingly, purees with reduced

sugars could be distinguished and jams containing extra fruits could aIso be

separated using PCA as a clustering approach. Discrimination based on whether the

fruits were freezed or not as weIl as separation based on the maturity of the fruits and

their origin could not be achieved.

The combination of FTIR spectroscopy and ANN has proven to be a rapid

and efficient tool in classifying starches based on the modification they have

undergone (Dolmatova et al., 1998). Amr had the advantage that combination of

two modifications could be detected. Although the supervised (feedforward) and

unsupervised ANN (Kohonen network) leaming approaches were tested and both

yielded excellent classification results, the supervised feedforward had the advantage

of identifying the type ofmodification.

2. Quantitative analysis ofwater-based solutions and beverages

A significant amount of literature has been published on the use of infrared

spectroscopy for the quantitative analysis of individual and total sugars in solutions

and water-based drinks and beverages (McKelvy et al., 1998; Putzig et al., 1994).

Here we will only review the work that has been done using FTIR spectroscopy in

the mid-infrared region. The presence of high water content prevents the use of

40

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•

•

transmission cells unless the pathlength is in the order of a few microns. The advent

of ATR has allowed a more systematic application of FTIR to high moisture foods

and beverages (Sedman et al., 1998).

Initial work in the field of quantitative analysis of beverages indicated that

the feasibility of the ATR-FTIR spectroscopy was positive when used with P-matrix

modeling (Kemsley et al., 1992a). This mathematical tool was quickly replaced by

the more powerful PLS multivariate analysis method. The ability to calibrate on

sugar solution and adequately predict water-based drinks and beverages including

degassed carbonated drinks was demonstrated on severa! occasions (Lendl and

Kellner, 1995; Rambla et al., 1998). Although a simple ATR was the preferreci

sampling accessory, flow injection systems linked to a transmission cell were also

successfully used (Schindler et al., 1998). The approach of using the absorbance

spectra with little pretreatment was predominant although attempts at quantifying the

sucrose based on spectral subtraction was also successful (Lendl and Kellner, 1995).

In other work, the comparison between a univariate approach, which used an enzyme

to hydrolyze the sugars, and a multivariate approach where sugar mixtures were used

as a training set yielded comparable results (Kellner et al., 1997). However, the

univariate approach was not amenable to on-line analysis because of the enzymatic

step. A series of work using FTIR spectroscopy in combination with ATR to predict

the sugar contents of sugar cane have been published (Cadet et al., 1991; Cadet et al.,

1997). The quantitative analysis of this biological matrix has required data

pretreatment in the fonn of spectral classification and baseline correction (Cadet,

1996b). The error on predictions of the optimized models as weil as the bias of

41

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•

predictions was significantly lower than those obtained for the reference method~

polarimetry.

The speed of data acquisition and analysis made FTIR spectroscopy attractive

for the monitoring of industrial processes such as starch hydrolysis (Bellon-Maurel et

al., 1995a). Work published indicates that the FTIR methodology can predict starch

hydrolyzate samples in a repeatable and reproducible manner. However~ the

predictions of maitoriose and maltodextrines yielded poor results due to the low

concentrations of these compounds. The presence of protein and mL'lerals did not

seem to affect the prediction of carbohydrate in solutions, an observation also made

in sugar cane analysis (Cadet, 1996a). Interestingly, the testing of different spectral

regions of calibration showed that using a restricted region between 910 and 1180

cm-1 improved the errors compared ta using the complete sugar region (Bellon­

Maure1 et al., 1995b). Monitoring sucrose enzymatic hydrolysis by FTIR

spectroscopy (Cadet et al.~ 1995) provided a rapid means of calculating kinetic

parameters of the reaction. In other work~ enzymatic sucrose hydrolysis and starch

hydrolysis by 1,4-a.-glucosidase were monitored simultaneously through the

resulting carbohydrate mixture (Schindler and Lendl, 1999). The enzymatic

isomerization of glucose syrups was also successfully monitored using FTIR

spectroscopy (De Lène Mirouze et aL, 1993).

Although the potential of FTIR spectroscopy for the analysis of food and

foodstuff has been investigated in the field ofconfectionery (Belton et aL, 1988) and

a few successful uses of NIR spectroscopy have been presented (Bollinger et aL,

1999; Dauberte et al., 1987; Varadi and Toth, 1992), the application of the FTIR

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spectroscopy as a quality control technique in the chocolate industry remains

undeveloped. The work presented in section 4.1 will therefore concentrate in part on

developing a rapid FTIR. spectroscopie based quality control method for chocolate

syrup analysis, one of the most popular chocolate end-products in North America.

2.5. FmER OPTICS FOR INFRARED SENSING

Fiber optics (FO) technology was developed several decades aga and became

widespread in fields such as telecommunications, remote sensing, chemical analysis

and medicine for remote viewing (Berthold, 1991).115 uses in the food industry, and

particularly coupled to IR spectroscopy, has been very limited.

Fiber optics is a dielectric waveguide in which the light is propagated by

internaI reflectance (Shotwell, 1997). The main problem associated with fiber optics

is related to the transmission properties of the material which detennines the cutoff

point and the fibers sensitivity (0sterberg, 1998). In combination with FTIR

spectroscopy, fiber optics can either he used to relay the infrared beam to the sample

and back to the detector (Kemsley et al., 1992b), or serve as sensing material

(Afanasyeva, 1999).

Near infrared fiber optics are weII-established (Baughman, 1989) and have

been extensively used in many fields ranging from monitoring chemical processes

(Bj0Tsvik, 1996) to analysis of sugars in foods (Kemsley et al., 1992b). The

advantages of using NIR fiber optic accessories are their higher sensitivity at room

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temperature and the robustness of the NIR fiber optic material, which suffers very

liule loss as the fiber optic length increases (Gort: 1996).

Although calibration models based on mid infrared data has demonstrated to

be more powerful than the calibrations in NIR. owing to weIl defined band

assignment, the mid-IR fiber optic area has experienced a very slow growth. Sorne of

the hurdles ta overcorne were the high energy loss suffered by the limited selection

of optical materials transmitting in the mid-IR and the adequate coupling of the

fibers to ensure maximum throughput (Doyle, 1992).

In recent years, these obstacles have been partially overcome and a number of

fiber optic accessories for mid-IR sensing have been developed. The commonly used

mid-infrared fiber optic materials are silver halide (AgBrxClx_1) (Kellner et al., 1995),

chlorinated hydrocarbons (CHC) (Gobel et al., 1997), sapphire (Ah03) (Gotz et al.,

1997) and chalcogenide (As-Se-Te) (Saïto and Kikuchi, 1997).

Despite the high transmission and reflection lasses (Saito and Kikuchi, 1997)

of the chalcogenide material (O.5db/m at a wavelength of 6 Ilm), the high level of

purity of this material allows the chalcogenide to remain a significant player in the

field of mid-IR fiber optics (Churhanov, 1995). Although aIl fiber optics are used as

ATR sampling accessories, the sample contact surface can either be a bare core fiber

(de Rochemont et al., 1993) or a tapered end in the fonn of a probe (Druy et al.,

1993). The latter has the advantage of being used as stabbing end, in the analysis of

semi-solids and highly viscous samples without any sample preparation. The

chalcogenide fiber optic accessory employed in this study has been used in a few

applications such as monitoring epoxy resin formation in chemistry (Druy et al.,

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1993) and lubricant cure and aging (Glatkowski et al.~ 1994). A preliminary

evaluation of the quantitative potential was presented for lubricants, oil and fuel

(Druy et al., 1994).

The encouraging results obtained in this limited study warranted further

investigations of the chalcogenide fiber optic probe as a method of collecting data for

quantitative analysis purposes. The application of the FTIR. spectroscopy in

combination with the chalcogenide fiber optic probe to a food matrix bas not been

undertaken. Ye~ this approach represents an attractive potential as an at-line or on­

line quality control methodology in the chocolate industry due to the viscosity of the

products being manufactured.

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Chapter"3: Material and Methods

3.1. METHOD DEVELOPMENT FOR CHOCOLATE SYRUP EMPLOYING A

MID-IR CHALCOGENIDE FIBER OPTIC PROBE

3.1.1. Description of the fiber optic probe accessory

The fiber optic probe accessory has three functional parts. Figure 3.1 is a

picture of the different parts of the fiber optic probe, (a) fiberlink, (b) probe and (c)

placed in the spectrometer. The fiberlink, which is placed in the sample compartment

of the spectrometer (Figure 3. 1(a)), uses mirrors to direct the IR beam into the cable

and redirect the beam from the retum cable towards the detector.

The fiberoptic cables are made ofa cha1cogenide (As-Se-Te) core of750 Jlm

and glass cladding, and housed in a stainless steel tubing for durability and

ruggedness. These semi-flexible cables measure 1.5 m each.

The sampling crystal is housed at the tip of the probe. The sampling head is 5

mm in diameter allowing it to fit in small tubes. The effective sampling surface is -1

mm2, which makes the accessory ideal for the analysis of micro volumes. The

chalcogenide crystal is U shaped and is embedded in a phenolic resin material

(Figure 3.2). Chalcogenide has a refractive index of 2.81 at a wavelength of 10 Jlm, a

value very close to that of a zinc selenide crystal. The different parts of the accessory

are connected together by SMA couplers. Table 3.1 summarizes the main properties

of the accessory.

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b) Probe

c) InstaIled fiber opticprobe accessory

Figure 3.1: Photograph of the different parts of the fiber optic probe accessory.(a) tiberlink (b) probe (c) installed in the spectrometer.

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Figure 3.2: Schematic and photograph of the sensîng part of the fiber opticprobe accessory

The chalcogenide U shaped crystal is embedded in a phenolic resin

material. (Courtesy ofSensiv, Inc, Waltham, MA)

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3.1.2. Preliminary studies offiber optic probe accessory

3.1.2.1. Cable stability study

The development of viable quantitative analysis methods requires among

other things, the use of high quality and reproducible spectra. Reproducibility will

depend on the consistency of the physical and chemical properties of the sample, and

the stability of the hardware/software system used.

The cables of the fiber optic sampling accessory (Figure 3.1) are the part,

which undergoes the most movement during the analysis procedure. Firs~ the cables

change positions, as the probe tip is dipped into the sample and then moved up for

cleaning. Furthennore, the cables are bent into a curve that is not stable as both

cables have a certain freedom to tilt to either side of the accessory. The cables are the

infrared beam carrier, therefore the signal relayed by an unstable IR beam carrier can

adversely impact on the quality of the resulting spectrum.

The effects of cable instability on the quality of the spectral data was assessed

using a Bornem MB Series spectrometer (ABB-Bomem, Quebec, Qc). AlI spectra

were collected as single beams using 128 co-added interferograms. The resolution

was set to 8 cm- l and the level ofzero filling was set to 1.

In the setup of this experiment, only the probe part of the accessory was

supported using a pH electrode holder. The cables were not stabilized and could

freely tilt to one direction or the other. The probe was held at different heights from

the top of the workbench (Figure 3.3). The height was measured from the workbench

to the tip of the fiber optic probe. An initial position was set at a height of 10 cm. A

spectrum of water was colleeted before and after moving the probe to another

position. The probe was then moved back to the initial position to colleet the last

spectrum of water. The same test was carried out twice for each height and for the 4

different heights: 10, 15,20 and 25 cm.

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Height(h)

Workbench

Figure 3.3: Schematic of cable stability test

Spectrometer

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3.1.2.2. Evaluation of the signal-to-noise ratio

The signal-to-noise ratio (SNR) of a system is an important parameter in

relation to producing high quality spectra that can he used for quantitative analysis

method development. The SNR of the system depends, among other things, on the

strength of the initial infrared beam reaching the detector, the inherent noise of the

spectrometer and the degree of signal attenuation associated with the sample and the

sampling accessory. The noise affects aIso the lower limit of detection; the point at

which the signallevel is equal or below the noise level.

The following FTIR instruments are employed in the present work:

• Magna 550 spectrometer (Nicolet, Madison, WI) equipped with a DTGS

detector and a 45°, six-bounce germanium horizontal ATR (Spectra-Tech,

Ine., Stamford, CT)

• Magna 550 speetrometer (Nicolet, Madison, WI) equipped with a DTGS

detector and the mid-IR. chalcogenide fiber optic probe (Sensiv, Inc.,

Waltham, MA)

• Bornem MB Series (ABB-Bomem, Quebec, Qc) equipped with a DTGS

detector and the mid-IR ehalcogenide fiber optic probe (Sensiv, Inc.,

Waltham, MA)

The spectrum of air was recorded (with the sampling accessory in place)

twice back-to-back using a combination of different resolutions (4, 8, and 16 cm-Il

and different numbers of scans (8, 16, 32, 64, and 128). The data was treated by

taking the difference of two consecutive spectra and the amplitude of the residual

spectrum was ootOO.

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3.1.2.3. Evaluation of the effects of the chalcogenide cut-off point on spectral

information for the quantitative analysis of sugar

Chalcogenide is infrared transparent in most of the mid-IR region. However,

it displays a complete absorption between 2100 and 2200 cm-1 as weIl as below 900

cm-1• The latter is called the cut-offpoint. Although 900 cm-1 is the lower end of the

sugar fingerprint region, a high level of residual noise may render the 900 to 1020

cm-1 region unusable for quantitative analysis of sugars. It is therefore important to

assess the effect of loss ofspectral information in this region on the perfonnance of a

calibration model for sugar analysis. The well-established HATR sampling method is

used as a reference technique.

For this study, 25 samples were gravimetrically prepared by dissolving

known amounts of sucrase, glucose, fructose and maltose in distilled water to obtain

a 50g solution (Table 3.2). Each sample was scanned using a Magna 550

spectrometer (Nicolet, Madison, WI) equipped with a deuterated triglycine sulfate

(DTGS) detector. DTGS is a feroelectric material that becomes electrical polarized

when its temperature is changed. This response can be observed as an electrical

signal if electrodes are placed on opposite faces of the sensing material. The thermal

noise associated with this detector, termed pyroelectric, is almost null; thus the main

source of the noise is due to the amplifiers used to increase the signal passing

through the electrodes. A 45°, six-bounce germanium horizontal attenuated total

reflectance (Ge-HATR) (Spectra-Tech Inc., Stamford, CT) was used as the sampling

accessory. Bath the sample compartment and the spectrometer were purged with dry

53

• Table 3.2: Summary of sugar solutions used to assess the effect of the eut-off

point on the predictive ability of PLS models

Sample Maltose Fructose Glucose Sucrose Total Sugar Water

# (0/0) (%) (%) (%) (%) (%)

1 2.98 5.97 10.07 17.48 36.51 63.49

2 2.39 4.44 9.06 13.99 28.87 71.13

3 2.10 4.07 7.13 12.58 25.88 74.12

4 4.46 8.74 14.96 26.19 54.35 45.65

5 2.81 5.49 9.42 16.42 34.14 65.86

6 4.58 8.50 15.29 26.87 55.24 44.76

7 3.40 7.41 11.19 19.92 41.92 58.08

8 2.84 5.19 9.59 16.69 34.31 65.69

9 2.13 4.50 6.90 12.39 25.92 74.08

10 2.00 3.96 6.75 11.87 24.59 75.41

• Il 2.41 4.64 8.08 14.13 29.25 70.75

12 2.49 4.82 8.40 14.61 30.33 69.67

13 4.78 9.47 15.96 27.85 58.06 41.94

14 3.20 6.14 10.61 18.55 38.49 61.51

15 3.59 6.38 12.03 20.95 42.96 57.04

16 3.29 6.29 Il.53 18.49 39.59 60.48

17 4.30 7.76 13.78 24.12 49.96 46.38

18 4.62 10.07 15.28 27.16 57.12 42.88

19 4.78 8.71 16.05 28.08 57.61 42.39

20 3.18 6.39 10.58 18.52 38.67 61.33

21 5.06 9.47 16.66 29.11 60.30 39.70

22 2.98 5.72 9.91 17.45 36.06 63.94

23 3.80 7.32 12.73 22.47 46.33 53.67

24 2.62 5.36 7.98 14.70 30.66 69.34

25 3.09 5.58 10.25 17.99 36.90 63.10

• The concentrations are expressed in % weight by weight.

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air to minimize water vapor absopriton. A gain of 4 was used to sufficiently amplify

the signal in order to obtain an interferogram with a maximum signal of 3.80 volts.

64 scans at a resolution of 4 cm-1 and 1 level of zero filling were co-added to obtain

the final spectrum. The spectra were saved as absorbance spectra The collection

parameters are summarized in Table 3.3.

A spectrum of air was collected every 5 samples and used as the background

for the subsequent collections. The surface of the germanium crystal was thoroughly

washed with distilled water and wiped dry after each sample.

Fifteen samples were randomly selected to form the calibration set used to

develop PLS linear regression models. Individual calibrations were build for each of

the 6 components (sucrose, glucose, fructose, maltose, total sugar and water) using

the PLS feature of TurboQuant (Nicolet, Madison, WI). The remaining 10 samples

were used as the extemal validation set to assess the performance of the calibration

models.

3.1.2.4. Evaluation of the chalcogenide fiber optic probe accessory to

perform quantitative analysis of sugars in solution

Sugar solutions (Table 3.2) were used to test the ability of the chalcogenide

fiber optic probe ta produce data useful for the development of quantitative analysis

methodologies.

The spectra were collected using a Bornem MB Series spectrometer (ABB­

Bornem, Quebec, Qc) equipped with a DTGS detector and the mid-IR chalcogenide

fiber optic probe (Sensiv, Inc., Waltham, MA). Both the sample compartment and

the spectrometer were purged with dry air. A gain of 32 was used. 64 scans, at a

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Table 3.3: FfIR parameter settings for the study of the eut-off point effect

Parameter Setting

Resolution 4 cm-1

Co-added scans 64

• Zero filling 1

Apodization Triangular

Gain 4

Collection Region 600-4000 cm-1

The study was conducted using a Magna 550 Spectrometer (Nicolet,

Madison, Wl) equipped with a germanium HATR sampling accessory.

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resolution of 4 cm-1 and 1 level of zero filling, were co-added. The sample spectrum

was ratioed against the background spectrum to yield an absorbance spectrurn CAs =

-ln Oslle). A spectrum of air was collected before every sample and ratioed out of

the sample spectrum. The probe tip was thoroughly washed with distilled water and

wiped dry between each sample.

The spectra of 20 randomly selected samples were used to build PLS

calibration models. Individual calibrations were build for each of the components:

sucrose, glucose, fructose, maltose, total sugar and water. A set of 5 samples was

used as the extemal validation set ta assess the performance of the calibration

models.

3.1.2.5. Effect of restricted and wide dynamic concentration range on the

performance of calibration models

Preliminary PLS calibration models using the chocolate syrup samples

yielded calibrations with poor perfonnance. Examination of the pure component

spectra of the sugars and the variance spectrum calculated from the training set lead

to the investigation of the possible effect due to the narrow concentration ranges of

production line samples. The syrup samples used were obtained from a commercial

supplier. The concentrations of the components are therefore very close ta target

values. For instance, a ± 0.9% absolute error is allowed on a average value of 54.0%

total sugar concentration, implying that the highest possible concentration is 54.9 and

the lowest 53.1, a range that may not be wide enough to develop a robust calibration.

The other concem in developing a calibration for chocolate syrup is the possible

underlying interactions present in complex matrices; termed matrix effect. The

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syrup system was therefore studied for possible matrix effect and restricted

concentration range effect.

To assess the existence of a matrix effect, 12 sugar solutions of sucrose,

glucose, fructose and maltose mixtures were prepared by dissolving analytical grade

sugars (Sigma-Aldrich, Oakville, ON) in distilled water. The sugar profiles of the

solutions were similar to those of the pre-analyzed chocolate syrups obtained from

the commercial supplier. Table 3.4 summarizes the final concentrations for each

sample.

The performance of the models developed with the restricted concentration

range samples was compared to that of a set of samples with a wider range in

concentration (Table 3.5). The concentration of the sugars was randomized within

predetermined ranges of interest. The IR. spectra of aIl the samples were recorded

using the same setup and parameter settings as described in section 3.1.2.3.

3.1.3 Chocolate syrup analysis

A chocolate confectionery manufacturer provided the chocolate syrup used

through out the study. AlI the samples were collected from the production line and

pre-analyzed at the analytical laboratory of the manufacturer by HPLC with a

refractive index detector.

3.1.3.1. Development of a cleaning protocol for the liber optic probe tip

The fiber optic probe tip required thorough cleaning between successive

analyses to prevent cross-contamination of the samples. Chocolate syrup is

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•Table 3.4: Reference solutions of narrow sugar concentration ranges

Sample Maltose Fructose Glucose Sucrose Total Sugar Water

# (%) (0/0) (%) (o/c.) (0A») (%)

1 4.54 8.68 15.25 26.59 55.06 44.94

2 4.58 8.95 15.74 26.83 56.10 43.90

3 4.67 8.45 14.92 26.38 54.42 45.58

• 4 4.63 8.92 15.50 26.64 55.68 55.32

5 4.60 8.92 15.35 26.53 55.40 55.60

6 4.65 9.27 15.36 26.88 56.16 43.84

7 4.44 8.65 15.36 26.65 55.10 44.90

8 4.36 8.83 15.37 26.41 54.96 45.04

9 4.47 9.08 15.11 26.57 55.24 44.76

10 4.65 8.83 15.02 26.27 54.77 45.23

Il 4.46 8.70 15.60 26.50 55.25 44.75

12 4.62 9.28 15.58 27.02 56.49 43.51

•The concentrations are expressed in % weight by weight.

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•Table 3.5: Reference solutions of wide sugar concentration ranges

Sample Maltose Fructose Glucose Sucrose Total Sugar Water

# (01'0) (%) (%) (%) (%) (%)

1 2.30 5.33 23.86 28.89 60.37 36.96

2 2.16 7.77 16.52 18.39 44.83 55.17

3 3.98 6.48 5.55 34.99 51.01 48.99

• 4 3.56 11.58 18.17 35.07 68.38 31.62

5 2.61 11.78 5.63 14.94 34.95 65.05

6 2.99 7.32 21.14 24.04 55.48 44.52

7 3.05 8.95 10.59 17.01 39.60 60.40

8 3.90 10.39 24.55 21.77 60.61 39.39

9 3.55 5.15 23.72 15.07 47.49 52.51

10 3.72 9.60 5.88 29.07 48.27 51.73

Il 2.17 6.15 10.30 24.94 43.56 56.44

12 2.93 4.50 14.46 33.52 55.41 44.59

•The concentrations are expressed in % weight by weight.

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constituted mainly of water-soluble components and vegetable oils, which leaves a

layer of ail on the surface of the sampling accessory. The oil layer, which builds-up

with time, could adversely affect the performance of the calibration models

especially the modeling of fat in chocolate syrup samples. To minimize the impact of

this source oferror, different cleaning procedures were experimented with:

• distilled water

• isopropanol

• distilled water followed by isopropanol

• Tergazyme ® (Fisher Scientific, Nepean, ON), an enzyme containing soap

The cleaning experiments were carried out using the fully stabilized fiber

optic probe accessory coupled to a Bomem MB spectrometer (ABB-Bomem,

Quebec, Qc). Spectral collection was carried out using the parameter settings

described in section 3.1.2.3. The study was conducted using the following protocol:

1. collect the spectrum of distilled water

·2. dip the dry tip into a chocolate syrup

3. collect the spectrum of the sample

4. clean the tip of the probe with the selected cleaning protocol

5. dip the dry tip into distilled water and scan

Each cleaning protocol was tested for 5 successive sample collections ta

evaluate the long-term perfonnance of the cleaning protocol. The appearance of

mono and diglyceride bands at 1720-1760 cm- l and 2820-2940 cm- l were monitored

in the spectra of distilled water.

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• The most effective c1eaning is achieved when the tip is c1eaned with distilled

water followed by isopropanol. The optimization of the cleaning procedure was

aimed at developing effective cleaning guidelines, easy to use and amenable to

automation.

The retained cleaning procedure consisted of dipping the fiber optic probe in

distilled water, drying of the tip followed by a dip in isopropanol. The

standardization of the protocol required that the effective "dipping-time" be

determined. The test was conducted for the following sets oftimes:

1. 15 sec water and 15 sec isopropanol

2. 30 sec water and 30 sec isopropanol

3. 30 sec \vater and 15 sec isopropanol

• 4. 15 sec water and 30 sec isopropanol

5. 45 sec water and 15 sec isopropanol

6. 45 sec water and 30 sec isopropanol

•

3.1.3.2. Development of PLS calibration models for chocolate syrup analysis

Individual PLS regression calibrations were developed to model sucrose,

glucose, fructose, maltose, total sugar and water content in chocolate syrup. The PLS

feature of the Omnic TurboQuant Software (Nicolet, Madison, WI) was used to

develop the calibration models.

Duplicate IR spectra of each standard were collected using the optimized

setup and parameter settings in section 3.1.2.3. A background spectrum was

collected before every sample. 64 scans at a resolution of 8 cm-1 and 1 level of zero

filling were co-added. The probe tip was cleaned by dipping the tip in water for 30

sec, drying the tip and then dipping it into isopropanol for 15 sec.

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3.1.3.3. Validation of chocolate syrup validation models

AU the validation samples were scanned using the same protocol as described

for the standard set and not included in the calibration development. The

concentration of the different sugars was predicted trom the spectra using the

optimized PLS models. The data was used to further retine the calibration models (if

needed) by calculating the RMSE on the prediction of the extemal validation set.

3.1.3.4. Evaluation of chocolate syrup calibration model performance and

stability

1. Accuracy test

The accuracy study was based on the data collected for the validation set. The

PLS-based predictions of sugar concentrations were compared to the reference

concentration values and the error associated with each component obtained using a

HPLC method.

2. Repeatability test

Repeatability was tested using five chocolate samples. Each sample scanned

ten times back-to-back with no probe cleaning between spectral collections. The

parameter settings for data collection were the same as the ones used for collecting

the standards and the validation samples.

The ~pectra were predicted using the optimized PLS models and the

concentrations were corrected using the appropriate dilution factor. The corrected

predictions were statistically evaluated.

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3.Reproducibility test

Reproducibility was conducted over a three-week periode It dealt with same-

day, day-to-day and week-to-week stability of the calibration models and the

spectrometer probe system. Same-day testing addressed short-tenn stability. Day-to­

day reproducibility was done over three consecutive days at approximately 24 hour

intervals while the week-to-week study stretched over a three-week periode

The study was based on five samples. AIl samples were freshly prepared ta

minimize composition alteration over time. The spectra were collected using the

protocol and parameter settings described for the standard set. The spectra were

predicted using the optimized PLS models and the values obtained were corrected

using the appropriate dilution factor. An ANOVA test was used to statistically

evaluate the corrected concentrations.

3.1.3.5. Study of chocolate syrup calibration model ruggedness

Testing calibration ruggedness is aimed at evaluating the effect of changes in

the environmental and/or operating conditions on calibration perfonnance. The

experiment was designed to assess the impact of changes in the following

parameters:

1. effect of decreasing the number of co-added scans of the sample and

backgroundspectra

2. effect of decreasing the number of co-added scans for background collection

only

3. effect ofstanding time on spectral characteristics

4. effect ofusing an non-purged system

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For each test, 5 freshly prepared chocolate syrup samples were used. Spectra

were collected using a Bornem MB Series spectrometer (ABB-Bomem, Quebec, Qc)

equipped with the fiber optic probe accessory. A spectrum of air was collected

before each sample and used as the background spectrum. Each sample was analyzed

following the optimized protocol described previously with sIight modifications to

accommodate the tested parameters as follows:

(a) the number of co-added scans for both the sample and the background

were decreased from 64 scans to 32 scans

(b) the number of co-added scans for the background were decreased to 16

scans while the number ofco-added scans for the sample remained at 64

(c) each sample was diluted, was left to stand for 30 min as opposed to 2-5

min as indicated in the optimized protocol

(d) the flow of dry purging air was stopped and the spectrometer was left

over night to allow the system to equilibrate with its environment

Each set of spectra from this experiment was predicted using the optimized

sucrose, glucose, fiuctose, maltose, total sugar content and water content calibration

models. The concentration values obtained for each set were compared to the actual

concentrations using the ANGVA test.

3.1.3.6. Prediction of undiluted chocolate syrup samples

In order to reduce sample preparation time, undiluted portions of 5 samples

selected for the validation sampies were scanned using the optimized protocol

described previously. The probe tip cleaning protocol was modified to minimize

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contamination from the highly viscous samples to insure long-term reproducible

results. The water-cleaning step was lengthened from 30 sec to 45 sec. The

isopropanol step remained the same. The spectra were predicted using the calibration

models based on the diluted standards. Using Qrigin (Microcal Software, Inc.,

Northampton, MA), the predictions were statistically compared to the HPLC values.

3.1.3.7. Transferability test

Transferability tests the ability of an existing calibration in predicting

components from spectra collected on a different system. Transferability will insure

that optimized calibrations are viable if a part of the spectrometer or the spectrometer

itself is changed for reasons ranging from tear and wear to upgrading the equipment.

Transferability testing is also necessary in case the sampling accessory is replaced

for damage or other reasons.

The optimized calibrations developed in section 4.1.2.3 were used as Master

Calibrations. The transferability between spectrometers of the same make was first

assessed using the Bornem MB and the Bornem WorkIR. (ABB-Bomem, Quebec,

Qc). Then transferability between spectrometers from different companies was

studied using the Bornem MB (ABB-Bomem, Quebec, Qc) and the Magna 550

(Nicolet, Madison, WI), and between different sampling accessory was also tested.

A set of five samples collected on each of the tested systems was predicted

using the master calibrations and the predicted values were statistically evaluated to

assess the possible prediction deterioration caused by the fact that spectra were

recorded on a system different from the one used to scan the calibration set of the

Master Calibrations.

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3.2. MODELING SYSTEMS

3.2.1. Partial-Ieast-squares

Partial-Ieast-square (PLS) is the most commonly used quantitative analysis

method for the modeling of complex systems with highly overlapping bands. PLS is

the main modeling system used throughout this study and is the basis of comparison

for aIl other alternative approaches to modeling developed in this work.

3.2.2. Artificial neural networks

Artificial neural network (ANN) can be used as a qualitative and a

quantitative analysis modeling approach in the analysis ofcomplex systems.

An ANN model was developed to classify chocolate syrup samples collected

directly from the production by formulation type. This model was based on 52

spectra covering three different chocolate syrup fonnulations. A second ANN model

was trained to quantify the sugar content of chocolate syrup. The model was also

based on chocolate syrup samples taken directly form the production line. For both

the ANN the spectra used were collected using the optimized analysis protocol

developed in section 4.1.2.2.4.

The perfonnance of the ANN model developed to quantify sugar

concentration in solution was compared to the commonly used multiple Iinear

regression method, PLS. A series of 69 reference solutions (Appendix 3) were

gravimetrically prepared using sucrose, glucose and fructose. Each sample was

scanned using a Magna 550 (Nicolet, Madison, WI) equipped with a Ge-HATR

sampling accessory. The spectra were collected using 64 co-added scans at a

67

•

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•

resolution of4 cm-1 and 1 level of zero filling. A set of 49 randomly chosen samples

were used as the training set while the remaining 20 were used to validate the

developed models.

AlI the ANN models were developed using NeuroShell (Ward Systems

Group, Frede~c~MD).

3.3. STUDY OF CHEMICAL INTERACTIONS

Understanding sugar-water interaction is a crucial step towards understanding

the structural changes that occur in both water and sugar molecules in solution.

Grasping the nature of the interactions and their effects on the mid-IR. spectral profile

of the solution could provide significant information to improve sugar components

modeling in water-based beverages, drinks, juices, syrups etc...

Single sugar reference solutions of sucrose, glucose, fructose and maltose

(Sigma-Aldrich, OakvilIe, ON) were prepared by dissolving known amounts of each

sugar in distilled water to cover a concentration range of 5 to 65% weight by weight

with 5% concentration increments. The spectra of each sample was recorded using

the setup and parameter settings described in section 3.1.2.3. Sugar-water

interactions were studied using 2D correlation software for Grams 4.0 (Galatie,

Salem, NH).

68

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•

•

Chapter'4: Results and Discussion

4.1. QUANTITATIVE ANALYSIS OF CHOCOLATE SYRUP USING A

CHALCOGENIDE FIBER OTPIe PROBE

4.1.1. Evaluation of a chalcogenide liber optic probe for the

quantitative analysis of sugars in solution

Chalcogenide fiber optics have been mainly used in the chemical industry as

a quick and safe technique to record FTIR. spectra for process monitoring, chemical

identification and classificatio~ and for the identification of hazardous wastes in

environmental applications (Druy et al., 1994). Despite a few hints at the analytical

potential of the chalcogenide fiber optic probe (Baulsir and Simler, 1996), its

application for quantitative analysis has not yet been established. This section is

aimed at assessing the capabilities of the fiber optic probe for the development of a

methodology for the quantitative analysis ofchocolate syrup.

4.1.1.1. Effect of the stability of the fiber optic probe cables on spectral

information

The development of stable and rugged IR based quantitative methodologies

requires good spectral reproducibility. The stability of the hardware is one of the

main sources of reproducibility problems second ooly to the alteration of the sample

by decomposition and mold growth arnong other things. The fiber optic probe (POP)

69

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•

sarnpling accessory (Figure 3.1) uses 2 fiber optic cables to relay the modulated IR

beam to the sample media and back to the detector. In any demountable accessory,

the unstable points are the connection points as they are subject to a number of

problems ranging from improper connection to an inefficient relay of the light. The

connection points of the FOP cables ta the probe and the fiberlink represent the­

potentially unstable points of the fiber optic probe accessory. This instability could

be further accentuated by the sampling protocol that may require moving the probe

into and out of the vials containing the sample. In this particular case, any instability

at the connectors can be detected as a change in the quality of the recorded spectrum.

Figure 4.1 shows two spectra recorded (a) by moving the cable system into

and out of the sample, thereby changing the curvature of the cables and (h) by

bringing the sample into contact with the probe tip with no movement applied on the

cable fixtures. The decreased spectral quality ofspectrum (a) may be attributed to the

vertical movement and the resulting curvature change, which may effect the level of

straining on the couplers. The instability can be seen in the differential spectrum in

the fonn of a mixture of noise and fringing (Figure 4.1(c». These observations

demonstrate the decreased spectral quality in the 950-1800 cm- l region when the

cable system is submitted to a vertical movement.

Furthennore, the differential spectrum of consecutive absorbance spectra of

water taken at a height of 10 cm and a height of 20 cm shows fringes (Figure 4.2).

Spectra ofwater collected at a ~eight (h) of 10, 15, and 20 cm were subtracted from

70

•

1000 9501300 1200-1

Wavenumber (cm )

0.20

0.15

0.10

0.05

0.20 (h)QJ

0.150

â..c 0.10~

0en

..c 0.05<:

• 0.02

0.00

-0.02

-0.04

1400

Figure 4.1: Effect of the vertical movement of the fiber opti~lprobeaccessory onthe spectral quality in the region of 950-1300 cm (a) with a verticalmovement of fiber optic probe assembly and (b) without movement (c)Differentiai spectrum

./These spectra show the decrease in spectral quality in the 950-1300 cm

region when the cable ·system is submitted 10 a vertical movement.

•71

•

0.010a)

lU 0.000C,)

â~....

-0.0100a'J~

< -0.020

0.010

• Cl)0 0.000§~....0 -0.010CI)

.D<:

-0.020

1400 1200

Wavenumber (cm-1)

Figure 4.2: Differentiai spectrum of consecutive single beams taken at (a) lowestconnector strain position, and (b) highest coupler strain position

•72

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•

the spectrum taken at a height of 25 cm. The amplitude of the fringes increased

proportionally to the difference between the initial and the rmal position.

The effect of moving the fiber optic cable assembly on spectral

reproducibility was studied by recording the spectrum of a syrup sarnple before and

after a vertical movement. The residual spectrum. obtained showed fringes with an

amplitude of 0.03 Abs unîts. This value is significant because it represents 14% of

the maximum absorbance for a chocolate syrup sample (0.22 Abs units at 1052cm-1).

Changing the fiber optic probe tip position in a vertical movement is directly

related to changing bath the curvature and the angle (a) at which the cables curve

from the base of the SMA couplers (Figure 3.3). The highest fringe amplitude is

obtained when the spectrum collected at a height of 25 cm was subtracted from the

spectrum scanned at a height of 10 cm. This implies that the position at 25 cm does

oot strain the couplers. As the angle Cl approaches 90°, the stress on the connectors is

minimized and the cable curvature that minimizes the fringing is obtained. The

change in cable curvature affects primarily the iotensity of the signal. The bending of

the cables results in an increased loss of the evanescent wave, leading to an increased

signal attenuation of up to ID folds (Shotwell, 1997). The connectors, on the other

hand, are the most important part in the calibration of the accessory to deliver a

reproducible beam of light. When a light beam is sent into the fiber optic cable, the

light is totally internally reflected if the incidence angle is below the numericaI

aperture (NA) of the fiber core-cladding composition. The NA is related to Schnell's

law and defined as:

73

•Eq.4.1

•

•

where () = acceptance angle

As the connectors get strained, the gapping between the fiberlink and the

cables is altered, thus changing the angle of entry of the IR beam into the cables

(Osterberg, 1998). Similar observations were made for the effect of a horizontal

movement of the fiber optic probe assembly. As the tip is moved away from the

spectrometer, the angle cp (Figure 3.3) becomes larger. This causes a change in the

opticaI coupling bet\veen the fiber optic cable and the fiberlink, which leads to

fringing.

The compiled observations suggest that in order to ensure spectral

reproducibility the fiber optic probe accessory will have to be held at a fixed angle of

approximately 90° and ideally, the cables must be heId at constant curvature.

Figure 4.3 shows the design of the support used to stabilize the fiber optic

probe assembly. The support is made ofaluminum and was designed for an easy and

quick assembly. Part A is the base plate of the support, attached to the spectrometer.

Part B is the backbone of the structure, which allows a vertical translation for precise

74

•

•PartE PartD parte

PartB

Part A

•

Figure 4.3: Design of the fiber optic probe stabilization accessory

Part A: Base plate

Part B: Structure backbone

Parts C. D. & E: Extendible arm

Part E: Probe ho/der

Parts F& G: Cable support

75

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•

height adjustments. Parts C, D and E fonn an extendible ann to support the probe.

The height of the probe is adjusted by moving Part C. Part E is the probe tip holder.

The probe tip is locked in position using three screws placed at each side of Part E.

Part G is a welded arch to fix cables in constant curvature. Finally part F is an

extension that allows positioning of Part G with respect to height and angle to avoid

straining the connectors.

The chalcogenide fiber optic probe tip is heId in place at a height of 24 cm

from the workbench top, which causes the cables to curve from the connectors at an

angle of a. ::::: 90°, thereby minimizing the straining on the connectors. To minimize

rnovement of the fiber optic probe accessory, the sample is set on a vertically

movable plate which brings the sample upward into contact with the tip of the probe.

Manual movernent of the plate may be required in a laboratory setting where fewer

analyses are performed. Routine analyses in an analytical laboratory or at a

production plant (at-Line analysis) could require automation of the plate to

accommodate auto-sampling procedures and to increase the reproducibility in sample

positioning with respect to the fiber optic probe tip.

The stability of the fiber optic probe after placing it in the support was

assessed. The spectrum of water was recorded 5 consecutive times and the

reproducibility of the spectra was evaluated using the residual of a set of any two

spectra. No mnges were observed in the differential spectrum (Figure 4.4).

76

•

0.008

0.004

a) 0.000uC~

.D-0.004....

0(1')

.D« -0.008

• -0.012

-0.016

_00.,0

1800 1600 1400 1200

•

Wavenumber (cm- l)

Figure 4.4: Differentiai spectrum from two spectra of water recorded afterstabilization of the fiber optic probe accessory

77

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•

4.1.1.2. Performance comparison of two spectrometers on the basis of their

signal-to-noise ratio and ruggedness parameters

The effect of low throughput of the fiber optic probe can be evaluated by

comparing the noise levels obtained from the same spectrometer equipped with a six­

bounce gennanium horizontal ATR (Ge-HATR) sampling accessory to that of the

fiber optic probe in the region. The region between 1050 and 1500 cm- I was selected

because the chalcogenide cut-off is approximately 980 cm- I. At ail resolutions and

different number of co-added scans tested, the noise expressed in root Mean square

(RMS), which is a statistical measurement of the noise variation, was on average 5

times higher for the fiber optic probe at the optimized signal throughput (Table 4.1).

The throughput, a parameter that affects the strength of the signal, is Iargely

dependent on the area of the beam and the power of the beam at the focus point. For

the chaIcogenide fiber optic probe, this translates into a loss of energy due to

attenuation that occurs in the cables and to the inefficiency of coupling of the IR

beam to the chalcogenide fibers. A superior optical coupling cao be obtained if a

smaller diameter beam is used because it can be directed more efficientIy into the

750 J.lm diameter chalcogenide fiber core. Furthennore, the transport of an infrared

beam through fiber optic cables is based on total internaI reflection, relying on the

difference of refractive index between the core and the cladding of the cables. As

reflectance occurs at the core-cladding interface, Iight is lost because the reflectance

is not perfecto This phenomenon leads to an attenuation of the signal thus decreasing

the strength of the beam. Attenuation of the signal cao aIso be caused by scattering in

78

e e e

Table 4.1: Summary of the RMS noise associated with the systems studied in the region of 1050-1500 cm-·

BomemMB-FO Magna 550-FO Magna 550-Ge-HATRGain

4em-) Sem- l 16em-) 4em-) Sem- l 16em-) 4em-) 8em-) 16em-1

8 0.00739 0.00507 0.00303 0.00328 0.00175 0.00163 0.00056 0.00034 0.00027

16 0.00443 0.00329 0.00205 0.00202 0.00157 0.00094 0.00042 0.00033 0.0002

32 0.00336 0.00324 0.00132 0.00149 0.00107 0.00087 0.00032 0.00023 0.00013

64 0.00215 0.00149 0.00122 0.00116 0.00078 0.00060 0.00021 0.00015 9.04 x 10-s

128 0.00159 0.00108 0.00082 0.00086 0.00060 0.00052 0.00016 9.6 x 10-5 8.74 x 10-5

79

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•

the fiber due to impurities or imperfections such as microbumps or variation in the

surface of the core of the fiber (Shotwell, 1997). Another limitation has to do with

the size of the area of the sample and the sensing material interface. The sampling

head of the fiber probe is 5mm in diameter, however the effective sampling surface

covers only a Imm2. This surface confers the sensing crystal an effective one

bounce, which could result in a weak signal attenuation due to the limited sample-IR

beam interaction. The weak signal can however be amplified by 4, 8, 16 or even 32

folds by adjusting the gain. The disadvantage of increasing the gain is the resulting

amplification of the noise. To optimize the throughput of the spectrometer equipped

with the fiber optic probe, the gain was increased to 8 and the mirror velocity was

decreased to 0.1581 from 0.6329 to increase the DTGS response by increasing the

measurernent time, which is inversely related to the noise level. Despite these

changes, the interferogram obtained with the fiber optic system showed a maximum

signal of 2.32 volts, compared to 3.32 volts obtained with the Ge-HATR system at a

gain of 2 and a mirror velocity of 0.6329. The difference in the optimum signal

parameters emphasizes the difference in noise level between the fiber optic and the

HATR accessories.

The performance of two spectrometers, the Bornem MB Series spectrometer

(ABB-Bomem, Quebec, Qc) and the Nicolet Magna 550 spectrometer (Nicolet,

Madison, WI), both equipped with the fiber optic probe, were compared.

Comparison of the signal-to-noise ratio (SNR) inclicate that the Nicolet Magna 550

has a better perfonnance (Table 4.1). Calibration models for sucrase, glucose,

fructose, maltose, total sugar and water were developed using sugar mixture

80

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•

solutions to assess the effect of having a higher noise level with the Bornem MB

Series spectrometer on the calibration perfonnance. The results of this experiment

are shown in Table 4.2.

Both spectrometers yielded comparable results for aIl components analyzed

with respect to correlation coefficient, number of factors, PRESS values at the

selected number of factors, and the RMSE on the cross-validation and on the

externaI validation. A one-way ANOVA test comparing the predictions frorn the

Magna 550 based models and the Bornem MB based roodels to the actual

concentration detected no significant difference at 0.05 and 0.01 significance levels

(Table 4.2).

A second set of tests was carried out to evaluate the stability and the

ruggedness of hardware. Ruggedness is defined as the ability to operate under a wide

array of environrnental situations without a deterioration of the perfonnance of the

hardware. The MB Series spectrometers have the advantage of being highly stable

and easy to move without requiring mirror readjustment. The latter characteristic,

which is attributed to the use of fixed corner cube mirrors to generate the

interferogram, allows the spectrometer to be positioned in a number of orientations,

without affecting the performance of the system. This was tested by comparing

single beams of air recorded with the spectrometer in different orientations. The

result showed no signal loss.

The high noise level of the Bornem MB Series warranted further

investigation to detennine the lower limit of detection (LOD). The LOD determines

the concentration a~ which the signal attributed to the sample faIls under the noise

81

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Table 4.2: Performance of the calibration models used to select the appropriate spectrometer-FOP for the chocolate syrup

analysis methodology

Nicolet Magna 550 Spectrometer Bornem MB Series Spectrorneter

R2Nurnber PRESS

of valueFactors

RMSE on RMSE onCross- External

Validation ValidationR2

Nurnber PRESSof value

Factors

RMSE on RMSE onCross- External

Validation Validation

F

Sucrase 0.997 2 2.10 0.24 0.62 0.995 2 1.83 0.24 0.62 3.552

Glucose 0.998 2 0.56 0.13 0.17 0.995 2 0.60 0.57 0.29 2.188

Fructose 0.995 3 O.3S O.OS 0.04 0.992 1 0.23 0.08 0.12 1.940

Maltose 0.996 1 0.07 0.08 0.13 0.970 2 0.19 O.OS 0.25 1.940

Total 0.997 2 8.03 0.58 0.39 0.995 2 8.0S 0.60 0.50 1.637SugarWater 0.998 2 6.40 0.54 0.31 0.998 2 4.01 0.52 0.34 0.217

82

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•

leveL The test was conducted using fructose and maltose solutions. Fructose was

selected because at 20% w/w it is the sugar for which the detector response was the

lowest. Maltose was also tested because it is the sugar present in least amounts in

chocolate syrup. The LOD employing the fiber optic probe mounted on a Bornem MB

Series spectrometer was experimentally detennined ta be 0.1 % w/w for maltose and

0.5% w/w for fructose. The LOD value for maltose is much below the concentration of

maltose (4% w/w) in chocolate syrup.

Based on the results obtained from the noise test, the calibration models and the

characteristics of the hardware, the Bornem MB Series spectrometer was selected to

develop the chocolate syrup analytical methodology since. A resolution of 8 cm- I and

64 co-added scans were selected as collection parameters based on the LOD value and

the length of measurement time. The latter criterion was considered because the

number of analyses per hour must be balanced to allow for a maximum sample

turnover without jeopardizing the perfonnance of the analysis.

4.1.1.3. Effect of the chalcogenide material eut-off point on sugar calibration

models

The potential and limitations of chalcogenide as an IR transmitting material

have been recognized early on. Chalcogenide fiber are used to relay the IR beam from

and to the spectrometer and as the sensing material that cornes in contact with the

sample. Chalcogenide absorbs greatly in the mid-IR region between 2100 and 2200

cm- I and below 900 cm-I (Figure 4.5), aIso known as the Ucut-off poinf~. The eut-off

point at 900 cm-} results in a high noise level that extends from 900 to 1050 cm- I

(Figure 4.6).

83

•

2.4

2.0 ~

~ .2·2 ...~ e

0~ 1.6 en

a .0d

b ta~ ...

1.20

< ~• t

0.8

0.4 Cut-off point

10.0

4000 3000 2000 1000

Wavenumbers (cm-1)

•

Figure 4.5: Single beam spectrum of air collected using the fiber optic probeaccessory

-/The spectrum shows the eut-off point al 900 cm and the region where

-/chalcogenide completely absorbs the mid-IR signal (2100-2200 cm ) .

84

•High noise region

__---À-----.( '\

0.30

0.20

Q)(.)

a0.10.c

M0en.c

• «0.00

-0.10

1400 1200Wavenumbers (cm-1

)

1000

•

-1Figure 4.6: Residual noise in the region between 900 and 1500cm

-/The eut-offofthe fiber op/ie probe al 900 cm leads to a high residual noise

-/in the 900-1050 cm region, rendering the region unusable for quantitative

analysis.

85

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•

The presence of such noise in the fingerprint region (900-1500 cm-l) of the spectrum

prevents access to infonnation helow 1050 cm-1, leaving the region between 1500 and

1050 cm-1 available for the development of calibration models. This region will he

referred to as "restricted region" in this document.

The effect of employing a limited spectral region on the quantitation of sugars

is assessed by comparing the predictive ability of 2 PLS calibration models based on

the sugar absorption region (900-1500 cm-l) and the narrower region (1050-1500 cm-I).

The spectra used for the models were recorded with a six-bounce Ge-HATR sampling

accessory. The absorhance of sugar concentrations, up to 70% w/w, remained in the

linearity of the detector (Figure 4.7).

The variance spectrum, a mathematical spectrum representing the correlation

between spectral variability and the concentration, indicates that the 900-1500 cm-1

region is suitable for modeling sugars. To compensate for baseline drift observed in the

sugar absorption region, a baseline anchor was placed at 1900 cm- I. Each of the 5

components, sucrase, glucose, fructose, maltose, and total sugar was optimized

independently in the development of calibration models. The PRESS values were used

to determine the number of factors at which the error is best minimized. The number of

factors was optimized on the hasis of parameters such as the loading factors and

loading scores to prevent overfitting. The correlation coefficient and the prediction

error generated from the leave-one-out cross-validation test were used to assess the

performance of the calibration. The developed calibration models were then used to

predict a set of spectra of known sugar concentrations not included in the training set,

86

•

100015002000250030003500

0.2

0.04000

1.0 W~er f!lter Sus..ars(

"'y

"'1 1 11 1 11 1 11 1 11 1 1

0.8 1 1 11 1 11 1 11 1 11 1 11 1 11 1 1

CIJ 0.61 1 1

10 1

â 11

"f0en

J:J< 0.4

•Wavenmbers (cm- I

)

Figure 4.7: Spectrum of a 70Gk w/w sugar solution scanned using the germaniumHATR

The figure shows the spectrum ofa sample containing 25.25% w/w sucrose,

25.43% w/w fructose and 19.66% w/w glucose, 70.34% w/w total sugar

content and 29.66% w/w water.

• 87

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the externalyalidation set. This validation step provided a more accurate assessment of

the models' predictive ability.

PLS calibration models for sucrase usmg both the full and the restricted

spectral regions yielded similar correlation coefficients of 0.998 (Table 4.3, Figure

4.8). Although the optimum number of factors was 3 for both calibration models, the

error associated with the calibration based on the restricted region was lower than that

of the full region, with PRESS values of 1.84 a drop from 5784.57 and 3.33 a drop

from 6324.58, respectively (Appendix 5). The RM8E frOID the cross-validation

predictions was similar for bath models. Furthermore, the RMSE based on the

predictions of the external validation showed lower values for the restricted region,

0.05 compared to 0.32 for the full region (Table 4.3). These observations indicate that

the region between 890 and 1050 cm-1 may not be contributing any necessary

information for modeling the concentration profile of the training set. A one-way

ANGVA test comparing the predictions of the extemal validation set to the actual

values did not detect any significant difference between the two calibration models at

0.05 and 0.01 significance levels (Table 4.4). These results indicate that, despite a

similar performance, the calibration model based on the full region has a slightly

higher error associated ta il. This error remains however statistically insignificant. The

use of the restricted region in the development of a PLS calibration model for the

determination of glucose modeling did not effect the perfonnance of the model. Three

factors were required for both models with errors of 0.75 a drop

88

• • •

Table 4.3: Calibration and validation results for comparison between the full region and the restricted region based

calibrations

Full Region Restricted Region

Component RMSEon RMSEon RMSEon RMSEonR2 Factor Cross External R1 Factor Error Cross External

Validation Validation Validation Validation

Sucrase 0.998 3 0.35 0.32 0.998 3 1.84 0.16 0.05

Glucose 0.998 3 0.18 0.21 0.998 3 0.63 0.16 0.21

Fructose 0.999 5 0.10 0.19 0.996 5 0.33 0.10 0.14

Maltose 0.996 2 0.06 0.04 0.996 3 0.6 0.04 0.03

Total Sugar 0.999 5 0.12 0.13 0.999 5 0.87 0.18 0.06

89

30.0

~ Full Regiono Restricted Region

20.0 25.0Actual

15.0

15.0

30.0 Sucrose

4.0 5.0 6.0 7.0 8.0 9.0 10.0 1l.0Actual

~ 25.0-(.):.0lU...

c.. 20.0

• 5.5Maltose

5.0

4.5"0~(.) 4.0

:.0lU1-

3.5c..

3.0

2.5

2.02.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

Actual

18.

GlucoseIII

~

~ 14.0(.)

:.0lU1- 12.0c..

10.

• 8.0

lln lO.O 12.0 14.0 16.0 18.0

A~tll~1

60.0

Total Sugar

"0 50.0lU

:~~J: 40.0

30.0

20.020

.0 JO.O 40.0 50.0 60.0

ActuaI

•Figure 4.8: Actual vs. Predicted for ail sugar components based on full and

restricted sugar region

The linear best-fit regression equations can be found in Appendix 4.

90

•Table 4.4: One-way ANOVA test results for comparison between full region and

restricted region based calibrations

Restricted

Compo- Actual Full Region Region d.f. F

nentMean Var. Mean Var. Mean Var.(%) (%) (%)

Sucrose 18.65 36.14 18.67 34.08 17.67 35.74 7 0.00004• Glucose 10.66 Il.88 10.52 11.24 10.58 10.94 7 0.00347

Fructose 6.05 3.48 6.12 3.97 6.12 3.89 7 0.00356

Maltose 3.17 1.07 3.20 1.04 3.19 1.06 7 0.00168

Total 38.54 152.24 38.51 154.09 38.63 151.64 7 0.0002Sugar

d.j:: degrees offreedom; Var.: Variance

The concentrations are expressed in % weight by weight.

The results indicate that there are no significant differences between the two

calibrations (a = 0.05 & 0.0/).

• 91

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•

from 4875.71 for the full region based calibration and 0.63 a drop from 4678.82 for

the restricted region (Appendix 5). The correlation coefficients obtained were

identica~ at 0.998 (Table 4.3, Figure 4.8) and the RMSE on the cross-validation were

comparable at values of 0.18 for the full region and 0.16 for the restricted region

calibration models (Table 4.3). These results were also supported by the RMSE

calculated based on the external validation predictions and the one-way ANDVA test

at 0.05 and 0.01 significance leveis (Table 4.4).

The modeling of fructose proved to be the most difficult. This calibration

required 5 factors to minimize the model error (Appendix 5) for both the full and the

restricted spectral region. Examination of the loading scores revealed that although

the tirst 3 factors could account for 99.5% of the spectral and 99.7% of the

concentration information in the sugar region in the standard set, factors 4 and 5

were needed to bring the model error to its lowest for both the restricted and the full

spectral region. The use of two extra factors indicates that the information provided

by factors 4 and 5 is significant for developing the PLS model. A total of 5 factors

were also required to optimize the calibration based on the restricted region,

indicating that the 890 to 1050 cm-I region did not contribute significant information

as noted for sucrose. The correlation coefficients obtained for the two fiuctose

calibration models were comparable at values of 0.999 for full region and 0.996 for

restricted spectral region (Table 4.3, Figure 4.8). The one-way ANDVA test carried

out on the predictions of the external validation set did not show a significant

differences between the predicted and the actual values for either calibration models

al 0.05 and 0.01 significance Ievels (Table 4.4). Based on the PRESS values and

92

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•

RMSE of the cross-validation test, the errors for the restricted region calibration

model and the full region were comparable.

Maltose is the sugar present in the least amounts in the chocolate syrup

samples studied. Interestingly, a PLS calibration model was developed with only 2

factors for the full region and 3 for the restricted region (Appendix 5), Yielding a

correlation coefficient of 0.996 in both cases (Table 4.3, Figure 4.8). The RMSE on

cross-validation and external validation were comparable at values of 0.06 and 0.04,

respectively (Table 4.3). The one-way ANOVA test showed no significant difference

between the restricted and full sugar region based calibration models at 0.05 and 0.01

significance leveis (Table 4.4).

Total sugar is defined in this work as a component that models the total

concentration of the four individual sugars. Restricting the calibration region did not

affect the calibration perfonnance. The correlation coefficient was 0.999 for both

models (Table 4.3, Figure 4.9). Five factors were needed to minimize the error in the

PLS model (Appendix 5). The RMSE from the cross-validation test predictions did

not indicate a significant difference in the error with values of 0.12 for the full

spectral region mode and 0.18 for the restricted spectral region (Table 4.3). The error

for prediction of the external validation predictions and the one-way ANOVA test

confirm that the modeling of total sugar content is not affected by using the restricted

spectral region (Table 4.4).

These results indicate that the modeling ability of calibrations based on a

restricted sugar absorbance region (1050-1500 cm-I) is comparable to that of

93

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•

calibrations based on the full sugar region (900-1500 cm- I) using the Ge-HATR

sampling accessory.

4.1.1.4. Quantitative analysis of sugar profiles in sugar solutions employing

the liber optic probe accessory

Pure sugar solutions were used to assess the potential use of the fiber optic

probe for the quantitative analysis of sugar in chocolate syrup. PLS regression

calibration models for predicting sucrose, glucose, fructose, maltose, total sugar and

water content in aqueous solutions were developed (Table 4.5, Figure 4.9)

employing the fiber optic probe accessory. Each component was treated separately.

The PLS regression models were optimized as outlined in section 4.1.1.3.

The variance spectrum relating the change in concentrations to the absorbance

showed a high correlation in the 900 to 1500 cm- I region for aIl the sugar

components. Due to the high noise residual around 1050 cm-l, the spectral region

used to calibrate these components was limited to 1050-1500 cm- l. This region was

subseque~tlyrefined for each component.

The modeling error for sucrose was mînimized by applying 3 factors,

yielding a correlation coefficient of 0.999 (Table 4.5, Figure 4.9). The RMSE on the

cross-validation test was 0.26 (Table 4.5). The RMSE of the external validation set

was equal to 0.43. The one-way ANGVA test carried out to compare the actual and

the predicted values of the validation set has shown no significant difference at 0.05

and 0.01 significan~e levels (Table 4.6). The modeling of glucose yielded results

comparable to those ofsucrose (Table 4.5).

94

•

Table 4.5: Calibration results for fiber optic probe based models using sugar

solutions

PRESS RMSEon RMSEonComponent R2 Factor Cross ExtemalValues

Validation Validation

• Sucrase 0.999 3 1.73 0.26 0.43

Glucose 0.998 3 1.01 0.20 0.27

Fructose 0.992 2 0.46 0.16 0.24

Maltose 0.998 3 0.10 0.06 0.07

Total Sugar 0.999 3 8.18 0.55 0.77

Water 0.997 5 34.81 1.118 1.62

• 95

• 5.5 10

5.0 Maltose9 Fructose

4.58

"0 "0~ 4.0 lUU .~ 7:.a "0~ 3.5 lU

Loc.. c.. 63.0

2.5 5

2.0 4

2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 4 5 6 7 8 9 IDActual ActuaI

18

Glucose30

Sucrase16

~ 14 "325

Ü ti:.a =ii~ 12 ~

c.. Q..20

10

• 815

8 10 12 14 16 18 15 20 25 30Actual ActuaI

75

60 Total Sugar 70 Water65

"0 50 "0lU

~ .~ 60u=ii "0

lU~ 40 ô: 55c..

5030

45

20 4020 30 40 50 60 40 45 50 S5 60 65 70 7S

Actual ActuaI

•Figure 4.9: Predicted vs. Actual of calibration set for aIl liber optic based

calibration models

The best- fit linear regression equations can be found in Appendix 4.

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Table 4.6: Results of the one-way ANOVA test on the extemal validation for the

fiber optic probe based calibrations

Actual Predlcted

Concentratîons* Concentrations*Component F

Mean Variance Mean Variance

• Sucrose 18.65 36.14 18.60 32.27 0.0035

Glucose 10.66 Il.88 10.52 11.04 0.0076

Fructose 6.05 3.48 6.08 3.14 0.00118

Maltose 3.17 1.07 3.17 0.98 0.00002

Total Sugar 38.53 152.24 38.31 147.77 0.0014

Water 61.46 152.44 61.20 131.91 0.0019

* Concentrations are expressed in % weight by weight.

• 97

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The calibration model for fructose showed sensitivity to subtle spectral

variations. Therefore twelve had to be removed from the training set in order to

model the component with a high correlation and a low error on prediction (Table

4.5, Figure 4.9). The discarded spectra were predicted using the optimized

càlibration model and the results were compared to the actual values. To no surprise

the one-way ANOVA test detected a significant difference between the two sets at

significance levels of 0.05 and 0.01 (Table 4.6). Preparation error is not a plausible

explanation for the high deviation between the actual and predicted values in these

spectra because the same solutions were used in the development of the PLS model

employing the Ge-HATR accessory in section 4.1.1.3, and no such deviation was

shown there. SampLe decomposition and microbial growth were also ruled out as the

sampLes were simultaneously scanned on the Ge-HATR and the FOP systems. The

difficulties encountered in modeling fructose may be due to the different

confonnations that fructose exhibits when it reaches equilibrium in solution (Angyal,

1984). The absence ofthis phenomena in the Ge-HATR based calibration model may

be due to the higher spectral quality obtained with the HATR sampling accessory.

The fiber optic based calibration model yielded a correlation coefficient of 0.992 at a

factor number of 2 with a PRESS value of 0.46 a drop from 832.97 (Table 4.5,

Figure 4.9, Appendix 5). The RMSE on the cross-validation and the external

validation are comparable at 0.16 and 0.24, respectively. The one-way ANOVA test

comparing the external validation predictions to the actual values showed no

significant differences at levels ofsignificance of 0.05 and 0.01 (Table 4.6).

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The modeling of maltose, the sugar present in the Ieast amounts, proved to be

very effective. The high correlation coefficient of 0.998 (Table 4.5, Figure 4.9) was

supported by low RMSE values from the cross-validation test (0.06) and the external

validation (0.07) (Table 4.5). The number of factors was 3 and the PRESS value at

the minimized error for the calibration model was 0.22 from 394.36 (Appendix 5).

The one-way ANOVA test conducted to compare the predictions of the external

validation set to the actual concentrations supported the results previously obtained

for this calibration model, i.e. there was no significant difference at 0.05 and 0.01

significance levels (Table 4.6).

The total sugar calibration model resulted in good predictions. The error of

the model was minimized with 3 factors, which Yielded a correlation coefficient of

0.999 (Table 4.5, Figure 4.9). The drop in the model error between the first and the

third factor for the total sugar component was the most important difference among

aIl the calibration models developed (Appendix 5). The RMSE on the cross­

validation test was 0.55 while that from the external validation set was 0.77 (Table

4.5). The one-way ANOVA test conducted confirmed that the FTIR predictions were

not significantly different from the actual values at significance levels of 0.05 and

0.01 (Table 4.6).

The calibration model for water content was based on the water band

between 1550 and 1720 cm- I. The region was corrected using a baseline anchored at

1740 cm- l. The model required 5 factors to minimize the error with a PRESS value

of 34.81, a significant drop from the initial error of 4119.45 (Appendix 5). The

correlation coefficient calculated was 0.997 (Table 4.5) and the RMSE on the

99

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•

•

external validation set was 1.62 (Table 4.5). The one-way ANOVA test simply

confinned the ability of the calibration model to predict the water content with no

significant difference from the actual concentrations at both 0.05 and 0.01

significance levels (Table 4.6).

The absence of bias in the predictions obtained from ail the models indicate

that there is no drift in the data. This may be directly related to the approach taken to

collect the absorbance spectra whereby a spectrum of air (the background spectrum)

is recorded before every sample, efIectively ratioing out any change in the

spectrometer and the fiber optic probe sampling accessory. Although the calibration

models using the fiber optic probe data perfonn weil when compared to the actual

values, a comparison with the Ge-HATR based models indicates a lower overall

performance for the fiber optic probe-based PLS models (Table 4.7). This may be

attributed to the low SNR associated with the fiber optic probe, which can be in part

overcome by either shortening the Iength of the fiber optic cables, which will in turn

decrease the total attenuation due to the internai reflectance inside the cables and

thus increase the signal reaching the sample, or increasing the sensing surface.

OveralI, despite the possible higher variances in the data predicted from the

fiber optic probe based calibration models, the statistical analysis of the data

indicates that these calibration models are adequate for quantitative analysis of

sugars in solutions when compared to the well-established HATR.

100

• • •Table 4.7: Performance of the Ge-HATR based model using a restricted range and the performance of the FOP calibrations

Fiber optic probe based calibration models Ge-HATR restricted region calibration models

R2 Factor PRESS RMSE RMSECrossV EV

F R2 Factor PRESSRMSE RMSE

CrossV EVF

Glu 0.998

Mal 0.998

Fm 0.992

Tsug 0.999

0.14 0.0027

0.03 0.0017

0.06 0.0002

0.21 0.0053

0.05 0.00006

0.18

0.10

0.04

0.16

0.161.84

0.63

0.33

0.6

0.87

3

3

5

3

5

0.998

0.77 0.0014 0.999

0.07 0.00002 0.998

0.24 0.00118 0.998

0.27 0.0076 0.998

0.43 0.0035

0.06

0.16

0.55

0.20

0.261.73

1.01

0.46

0.10

8.18

3

3

2

3

3

0.999Suc

Suc: Sucrose; Glu: Glucose; Fru: Fructose; Mal: maltose; Tsug: Total Sugar

RMSE CrossV: RMSE on Cross-validation; RM8E EV: RM8E on External validation

lOI

•

•

•

4.1.2. Analysis of chocolate syrup

4.1.2.1. Composition of chocolate syrup

C~ocolate syrup is considered a confectionery product and with a wide

variety ofapplications ranging from flavoring and sweetening to coloring. Despite its

importance in the food industry, chocolate syrup is not regulated and therefore the

amount of cocoa and other ingredients in the syrup are not controlled by health or

food organizations. This implies that ail fonnulations and manufacturing methods are

proprietary and cao change from company to company. Furthennore, since

fonnulation changes are subject to availability and pricing of raw materials,

fonnulations can be altered as long as the quality of the final product with respect to

taste and texture is maintained. Chocolate syrups unlike solid chocolate are water

rather than fat based cocoa products. In order to reach the appropriate level of

sweetness, a combination of manufacturing sugar products in the fonn of liquid

sugar, corn syrup and high fructose corn syrup (HFCS) cao be used. The final sugar

profile of the syrup is composed of sucrose, glucose, fructose and maltose. The

proportion of sugar in the syrup is important to prevent crystallization, which alters

the texture of the product.

A high sugar content IS aIso a critical parameter in attaining the right

viscosity and improving the preservation properties by decreasing water activity. The

shelf life of chocolate syrup is further lengthened by decreasing the pH to 5.5

through the addition of citric acid and/or sodium benzoate and/or potassium. The

concentration of sugar and the amount of cocoa particulates can increase the

viscosity and affect the rheology of the syrup. Gums and vegetable fat are used to

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•

further control and maintain the viscosity of the syrup. The use of vegetable fat

allows the manufacturer to claim a Ufat-free" product in accordance with the

definition provided by bodies of food regulations.

4.1.2.2. Examination of the infrared spectrum of a chocolate syrup sampie

The spectrum of a chocolate syrup sample collected using the fiber optic

probe accessory is shown in Figure 4.10. Although the spectrum shows the presence

of sugars (950-1500 cm-I), water (1550-1800 cm- I) and mono- and diglycerides

(1760-1770 cm-I), it was necessary to evaluate whether there was enough extractable

information to build a PLS model capable of predicting the sugar and water profiles

in chocolate syrup. As stated earlier, PLS uses mathematically constructed spectra

representing most of the variation in the calibration data set, called Ioading spectra,

ta model a system. Only the tirst few should be used to represent the totality of the

modeled system to avoid the contribution of minor variations and noise to the model.

The quality of the spectra used in the modeling will directly affect the performance

of PLS calibration models. In this work, two main concems were addressed: a

possible matrix effect and a limitation inherent to the narrow concentration ranges

offered by production line samples for the development of a calibration model.

1. Evaluation ofthe matrix effeet

"Matrix effect" is the generai term given to aIl interferences associated with

interaction occurring benveen the different components of the sample. If the matrix

effect is significant and has a consequence on the chemicaI structure of the

components, the changes will alter the IR spectral profile. If these changes are

unaccounted for during calibration development, the performance of the

103

•VVater Sugars

--L . Â""-___ _-------~-------_(r y- ')1 11 11 11 11 11 11 1

111

Mono & Di­glycerides

0.40

0.30

0.20

0.10

0.00 i=:T:;:::::..~~--__r_-__+_~~--T-----__r_----__,.~•1800 1600 1400 1200 1000

b -1Wavenum ers (cm )

Figure 4.10: Typical spectrum of a chocolate syrup sample showing the waterabsorption region (1500-1800 cm-l), the sugar region (950-1500 cm-l),and the mono/di-glyceride region {1740-1770 cm-Il

This spectrum was collected using the fiber optic probe sampling

accessory mounted on a Bomem MB Series spectrometer.

• 104

•

•

•

system on predicting a set of spectra not included in the calibration couId be poor.

Chocolate syrups~ like other foodstuffs, have a great potential for matrix effects due

to~ among other things~ sugar-sugar and sugar-water interactions.

The matrix effect was assessed by comparing 20 chocolate syrup samples to

20 aqileous sugar solutions mimicking the sugar profile of the syrups. The

correlation spectrum extracted from 20 chocolate syrup samples spectra did not show

a distinct correlation between any specific region in the infrared spectrum and any of

the sugars or components (Figure 4.11). This resuit was similar to that found using

20 samples of aqueous solutions of sugar (Figure 4.12). If a matrix effect was taking

place, the correlation spectrum of the 20 sugar solutions would have shown a distinct

correlation between the sugar components and the sugar region of the spectrum

(1050-1500 cm-1) since the solutions have matrices different from that of chocolate

syrup. The absence of such a correlation is a strong indication that a matrix effect is

not at the origin of the lack of correlation.

Despite the absence of a significant correlation spectrum, a calibration based

on undiluted chocolate syrup sample spectra was developed to evaluate how PLS

calibration models will perfonn in the absence of correlation between the spectra and

the limited sugar concentration range. It was not possible to develop PLS models.

This was most apparent by examination of the pure component and the loading

spectra. The pure component spectrum, which is the spectrum of the modeled

component as calculated by the PLS algorithm, resembled noise instead of the

expected sugar profile (Figure 4.13) for all 6 components. The loading factors aiso

105

•

0.30

0.26

0.22

4.)0.18(,)

§.D~

0 0.14en.D<

• 0.10

0.06

0.02

1800 1600 1400

-1VVavenurnbers(cnl )

1200 1000

•

Figure 4.11: Typical correlation spectrum obtained from twenty chocolatesyrup samples

This correlation spectrum was calculated based on the concentration of

sucrose, ranging.from 26.38% to 27.01%.

106

•

10001400

0.0018

-0.0002 -L...,-----..--...--.....----y-----~---L-r_-......-------_.__

1600

0.0014

eu(.)

0.0010a..c'""0en

..c<

0.0006

• 0.0002

Figure 4.12: Typical correlation spectrum for twenty sugar solutions withcarbohydrate profiles similar to that of chocolate syrups

This correlation spectrum is calcu/ated based on the concentrations of

sucrose, rangingfrom 26.38% to 27.0/%.

• 107

•9.0

8.0

7.0

Q)6.0

(,)a 5.0..D

'"'"0en

..D 4.0oc<

3.0

• 2.0

1.0

0.01600 1400

Wavenumbers (cm-1)

1200

•

Figure 4.13: Typical pure component spectrum obtained for undilutedchocolate syrup samples

The pure component of sucrase has the typical fiatures of a pure

component spectrum obtained from a calibration based on undiluted

chocolate syrup samples.

108

e·

e

e

resembled spectra of random noise (Figure 4.14). The combination of these

observations suggested that there is insufficient variability in the concentration range

to extract a correlation between the spectral information and the concentration to

develop a PLS model.

2. Evaluàtion ofthe tightness ofconcentration range and sugar ratios

The limited variability in the concentration range of the sugars and the ratio

of the sugars to one another reflects the quality control required for mass-production.

In the case of chocolate syrup, the total sugar content ranges between 53.3 and

55.0%, while the sugar ratio ranges from 1:1.8:3.1:5.3 to 1:2.1:3.7:6.2 for maltose,

fructose, glucose and sucrose, respectively.

The correlation spectrum of 20 samples of aqueous solutions of a mixture of

sucrose, glucose, fructose and maltose covering a concentration range wider than that

of the production Hne concentration range (Figure 4.15) was compared to the

correlation spectrum calculated from 20 aqueous solutions with sugar profiles similar

to that of chocolate syrup. The correlation spectrum of the narrow concentration

range solutions showed no substantial correlation between the concentrations and the

variance in the absorbance intensities (Figure 4.12). On the other hand, the

correlation spectrum calculated for the samples with a wider concentration range

showed a significant correlation between the concentrations and the changes in the

water and sugar absorbance regions (Figure 4.16). The latter is most likely due to the

elimination of the consistent ratio of the different sugars in the chocolate syrup.

Introducing in the training set a sample that has a predominant sugar provides

sufficient spectral variability to assign a peak to the sugar in question. The same

109

•

0.3 Loading Spectrum 10 0.2ua 0.1-e0

-0.0CI]

.0< -0.1

-0.2

0.3eu 0.2

Loading Spectrum 2ua.0 0.1$-<

• 0CI]

-0.0.0

<-0.1

-0.2

1500 1400 1300 1200

Wavenumber (cm- l)

Figure 4.14: Typical loading spectra obtained for undiluted chocolate syrupsamples

These loading spectra were obtainedfor the calibration ofglucose based

on undi/uted chocolate syrup samples.

• 110

•

•

35

30

25

20

15

10

5

CJ Restricted Range tSS1 Wide Range

Maltose Fructose Glucose Sucrase TotalSugars

Water

•

Figure 4.15: Concentration variability in restricted vs. wide range samples

This figure compares the variability range between the diffèrent

components concentration range in the solutions mimicking the

chocolate syrup sugar profile and the wide range sugar solutions.

III

•

•

•

result can be obtained when the sugar has sufficient variability relative to the other

components· without being predominant.

Employing the spiked samples to develop a PLS calibration model resulted in

distinct correlation spectra (Figure 4.1 7) and pure component spectra for each sugar.

Preliminary calibrations based on a limited set of spiked chocolate syrup samples

resulted in correlation coefficients above 0.89 for all components.

Direct standard addition to increase the variability in the concentrations of the

sugars in the chocolate syrup samples was not possible. Spiking the choco:ate syrup

samples is a difficult task due to its elevated viscosity and the limited free water

available to solubilize the addOO sugar. It Was therefore elected to add the sugars in

the form of aqueous solutions. This approach 100 to the dilution of the chocolate

syrup samples. By varying the concentrations of the sugars, the ratio of the sucrose,

glucose, fructose and maltose, to one another changed, thus minirnizing the effect of

effect due to the tightness of the range in the original samples.

3. Protocol for cleaning the fiber optic probe tip between samples

Chocolate syrup is constituted mainly of water-soluble components and

vegetable oils, which leave a layer of fat on the surface of the sampling accessory.

The fat layer, which can build-up with time, could adversely affect the performance

of the calibration. Figure 4.18 shows a typical diglyceride and monoglyceride mid­

infrared spectrum.

113

•

0.90

0.80

0.70en....·2 0.60;j

i 0.50

~ 0.40

• 0.30

0.20

0.10

1800 1600 1400 1200-1Wavenumbers (cm)

1000

•

Figure 4.17: Typical correlation spectrum of diluted chocolate syrup samples

This correlation spectrum was based on the concentration ofsucrase after

dilution ofthe chocolate syrup samp/es.

114

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•

Although a PLS calibrations can model underlying absorption, it cannot

compensate for an eventual build-up of fat on the tip of the probe. This phenomenon,

termed memory effect, is a major problem inherent to ATR sampling accessories

(Sedrnan et al., 1998). For purpose of comparison and to demonstrate the memory

effect, the probe was tirst dipped in chocolate syrup for 90 sec (a length of time

equal to the spectral acquisition time) and then cleaned using water only, and a

spectnun ofwater was collected. This procedure was carried out 5 consecutive times

and the fat bands at 1736, 2929 and 2852 cm-1 were monitored (Figure 4.19). The

absorbance associated with the fat build-up at 2929 cm-1 increased from 0.010

absorbance units after the first washing to 0.021 after the fifth spectrum. Different

cleaning procedures were assessed for the minimization of the fat deposition.

Tergazymet.\ an enzyme containing soap, was tested because it is considered

a safe and environmentally friendly cleaning solution. Unfortunately, Tergazyme®

did not result in adequate cleaning of the FOP sensing surface. Washing the probe tip

with isopropanol gave satisfactory results. Nonetheless, a build-up was observed

(0.038 to 0.040 absorbance units) after 5 washings.

The optimum cleaning protocol was found to be a two step approach using

distilled water tirst followed by isopropanol. After 5 and even 10 consecutive

washings, no build-up was observed (4.20). The success of this method may be

explained by the presence of the water-soluble components, such as sugars, which

are removed with water and the fatty components which are washed away with

isopropanol. These results were used to establish a cleaning protocol. The dipping

length required to ensure a clean tip was investigated using 6 dipping length

116

•/S,"Wasbing

0.020

0.016lUuC~

0.012.L:J~

0en

.L:J< 0.008

0.004

• 0.000

2960 2940

ISC Washing

2920

Wavenwnber (cm-1)

2900

•

Figure 4.19: Scaled spectra of C-H absorption bands from the fat build-up inthe 2850-2960 cm- I region

117

• 0.160

1st cleaning200 cleaning3rd cleaning41h cleanine

0.130

0.100

0.070

0.040

0.0103000 2900 2800 2700

0.090 Wavenumber (cm- I)

0.080• Cl.)(.)

0.070a.D...0

0.060en.D~

0.050

0.040

0.0301780 1760 1740 1720 1700

b -1Wavenum ers (cm)

•

Figure 4.20: Changes in the ester absorption region of mono and diglyceridesusing the optimized cleaning protocol

The optimum eleaning protoeol is a IWo step protocol using water (30see

for diluted and 45sec for non-diluted chocolate syrup sample) followed by

isopropanol (15see) .

118

•

•

•

com~inations. The fat build-up in the water spectrum was monitored using 5

consecutiv~ spectra collections and probe tip washings. The results showed that

dipping the probe tip in water for 30 sec was required to obtain the complete

dissolution of the water-soluble components of the syrup samples. The isopropanol­

cleaning step yielded highly reproducible results with 15 sec of dipping. Fresh

cleaning solutions were used for each repeat.

The optimized cleaning protocol was a1so applied to the cleaning of the probe

tip after placing in an undiluted chocolate syrup sample. The water spectrum

collected after the tip cleaning (30 sec in water followed by 15 sec in isopropanol)

showed traces of sugars but no sign of fat build-up. To compensate for the viscosity

of the sample being rinsed away, the water-cleaning step was extended to 45 sec.

This optimized protocol was successfully tested for 15 consecutive washings and

used throughout this work.

4. Standardized sample analysis protocol

Based on the results of the tests reported in the previous sections, the

following optimized analysis protocol was established.

Sample preparation for routine analysis

1. Weigh approximately exactly 5.0 g ofchocolate syrup.

2. Add distilled water to bring to a total weight of 10.0 g.

3. Shake gently to solubilize the syrup.

4. Calculate the dilution factor.

119

•

•

•

IR spectra recording

The spectra. of the chocolate syrup samples are recorded using the stabilized fiber

optic probe mounted on a Bornem MB Series spectrometer (ABB-Bomem, Quebec,

Qc) with 64 co-added scans, at 8 cm-1 resolution, and 1 level of zero filling, with again of E, equivalent to a gain setting of 32 of the Magna 550 (Nicolet Instruments,

Madiso~ WI).

1. Collect a background ofair.

2. Bring the sample to the fiber optic tip to constant depth.

3. Record the spectrum of the sample.

Cleaning protocol

Note: Avoid moving the fiber optic probe at all times

1. Remove sample vial without moving the fiber optic probe.

2. Bring vial of distilled water into contact with the probe tip. Let stand for 30

sec. Extend to 45 sec for viscous samples.

3. Wipe probe tip gently with Kimwipes®.

4. Bring vial of isopropanol into contact with the probe tip. Let stand for 15 sec.

5. Wipe probe tip with Kimwipes®.

4.1.2.3. Calibration development and validation for chocolate syrup

1. Calibration models based on randomized "sugar-spiked" chocolate syrup and

"water-diluted" samples

The tight concentration range of each sugar in chocolate syrup samples

provided by the manufacturer was compensated for by spiking the syrup samples

with sucrose, glucose, fructose and maltose in solution. The final concentrations

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•

•

were randomized values within predetennined ranges for each sugar (Appendix 2).

Sucrase vaqed between 6% w/w and 20% w/w.. glucose between 6 and 15% w/w,

fructose between 2 and 12% w/w and maltose between 1 and 6% w/w. The plots

presented in Figure 4.21 show that there is no co-linearity between the

concentrations of the different components. These samples, tenned "sugar-spiked"

chocolate syrups.. are in effect a 3 fold dilution of the original chocolate syrup

samples. This implied that the other components such as fat, vanillin, and other

carbohydrates in the chocolate syrup samples that may be present from the use of

HFCS and corn syrup as sugar based ingredients were held constant in the "sugar­

spiked" chocolate syrup samples. These possible interferences could oot he varied by

standard addition because a complete profile of an the different chemical species

present in the chocolate syrup were not available. It was therefore elected to use a

water dilution of the chocolate syrup samples ta provide variability and allow

modeling of these underlying infrared absorbing species. These samples were diluted

to caver a 0.25 to 4 fold dilution and were tenned "water-diluted" chocolate syrup

samples.

The standard set used to train the PLS models was composed of duplicate

spectra of 24 "sugar-spiked" chocolate syrup samples and duplicate spectra of 18

"water-diluted" chocolate syrup samples (Table 4.8). Each componeot was modeled

separately to obtain the best possible calibration. The calibration model for sucrose

yielded a correlation coefficient of 0.930 with an RM8E of 0.95 on the calibration

and 1.03 on cross-validation. These results were improved upon by discarding 59 of

the 84 spectra in the training set. The resulting correlation coefficient was 0.994

121

• 16 14

c • •--

12 • •.9 14 c • •ai - - .Et:: • ~ 10c l::lU 12 • c • •0 lU -C • u 80 • cu

10 • 0 -- U •lU • -on lU 6 • •0 CIl0 • 0 •::s 8 • 0 •0 • - ::s 4 •

w• U. • • •• •

6 • • • 2 •

6 8 10 12 14 16 18 20 22 6 8 10 12 14 16 18 20 22Sucrase Concentration Sucrase Concentration

146 •c c 12 • -0 • 0 • •• ",.::.~ 5 • • • ca

t:: .t: 10c c• lU •lU • 0 •0 4 • c •c 80 • 0

U U• lU • •lU 3 • - CI) 6 • ~en 0 •g • • • Ü

ëii • ::s • •::E 2 • .... 4 •• • ~

• • ••• 2 •• 6 8 10 12 14 16 18 20 22 6 8 10 12 14 16Sucrase Concentration Glucose Concentration

6 • c 6 •c .g.E , • •~ • - ca ••S • t:: 5 •l:: cC lUlU • • 0 • •0 4 • c 4 •c 00 • U •U

lUlU 3 •• • CIl 3 • • -en .9g • • • •• ëii •ëii • ::E •::E 2 • • 2 • •• •

• •

6 8 10 12 14 16 2 4 6 8 10 12 14Glucose Concentration Fructose Concentration

•Figure 4.21: Component co-Iinearity testing for "sugar-spiked" chocolate syrup

samples

122

•Table 4.8: Table of dilution factors and calculated concentration for the

chocolate syrup samples used as calibration standards

Total

Sample Dilution Maltose Fructose Glucose Sucrose Sugar Water

# Factor (%) (%) (%) (%) (%) (%)

1 0.38 1.77 3.38 5.75 9.84 20.74 74.79

2 0.66 2.91 5.95 10.19 17.33 36.38 55.56

3 0.49 2.15 4.35 7.62 12.75 26.87 67.27

4 0.68 3.01 5.94 10.52 17.76 37.22 54.11

5 0.47 2.03 4.11 7.14 12.29 25.58 68.04

• 6 0.40 1.71 3.37 6.03 10.19 21.29 73.20

7 0.59 2.6 5.26 9.10 15.35 32.30 60.37

8 0.52 2.20 4.88 8.29 13.32 28.68 64.66

9 0.44 1.97 3.89 6.78 Il.20 23.85 70.46

10 0.63 3.10 5.50 9.67 16.36 34.62 57.42

Il 0.57 2.73 4.88 8.69 14.82 31.12 61.61

12 0.55 2.54 4.69 8.50 12.24 29.98 62.84

13 0.50 2.36 4.38 7.65 15.60 27.41 66.10

14 0.48 2.20 4.11 7.17 12.19 25.68 68.06

15 0.42 1.81 3.66 6.39 10.90 22.76 71.60

16 0.39 1.75 3.46 5.95 10.15 21.31 73.83

17 0.35 1.45 3.08 5.39 9.07 18.99 76.01

18 0.58 2.44 4.99 8.99 14.97 31.36 60.81

The concentrations are expressed in % weight by weight.

• 123

•

•

•

(Figure 4.22(a» with an RMSE of 0.24 on calibration and 0.34 on cross-validation.

When predi~ted, these spectra showed a high error level at an RMSE of 1.80 (Figure

4.22(b». The extemal prediction based on "sugar-spiked" chocolate syrup samples

produced a RMSE of 0.94 (Figure 4.22(c», a value aImost three folds higher than the

cross validation.

A series of chocolate syrup samples diluted ta different extents using sugar

solutions provided a set of spiked samples where the final chocolate syrup dilution

varied randomly. These samples are termed "sugar-diluted" chocolate syrup

samples. Prediction on the f'sugar-dilu.ted" sample set using the optimized

calibration yielded a high error with an RMSE of 1.22.

Finally, a set of five original chocolate syrup samples diluted to 50% with

distilled water were predicted and the calculated RMSE was 0.94. The last set of

extemal validation will be referred to as diluted chocolate syrup. The calculated error

is based on the values predicted by the PLS algorithm using the optimized prediction

matrix and is therefore not the actual error, which should be based on the corrected

value obtained using the appropriate dilution factor for each sample. After correction

of the prediction, the error of the extemal validation increases to 1.95. These

observations imply that predicting sucrose in chocolate syrup, yields an actual error

of ±1.95 on a concentration range of 25.4 to 26.1% w/w. The high error on aIl

external validation sets compared to the relatively consistent cross validation and

calibration errors indicate that the model, although optimized based on the PRESS,

correlation coefficient, error on calibration and cross-validation, is in practice

inadequate to predict extemal validation sets. This may be due to the fact that the

124

• s:: 22Ca)o~ 20-~s:: 18eu .-

o ~s::- 16o ~u~

14"'O~eu-0 12 R2=0.997;e~ 10 RMSE =0.24l:l-.

8

8 10 12 14 16 18 20 22AcnmIConcentration

(%w/w)

s:: 220 (b)0.0 20g

18 •s::eu .-o ~ 16§~ •u~ 14-o~eu 12-0;a

ID R2 =0.944eu~

l:l-.8 RMSE = 1.80

6

• 6 8 10 12 14 16 18 20 22ActuaI Concentration

22(%w/w)

s::0 (c)..:= 20~s:: 180.-C,.) ~s::_ 168~-o'*- 14 •eu '-'"ti 12;a

R2 = 0.979~Q.c 10 RMSE=0.94

8

8 10 12 14 16 18 20 22ActuaI Concentration

(%w/w)

•Figure 4.22: Calibration and validation results for sucrose modeling based on

"sugar-spiked" and "water-diluted" chocolate syrup

Plots of ca) Calibration. (b) samples rejected from the training set, (c)

External Validation with sugar spiked chocolate syrup samples

125

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PLS when modeling did not have sufficient infonnation to build a generalized model

even though discarding 59 spectra did not affect the range of concentrations over

which the calibration model was being developed. Optimized models for glucose,

fructose, maltose, total sugar, and water content yielded results similar to those of

sucrase.

2. Calibration models based only on ttsugar-spiked" chocolate syrups

In order to acquire a better understanding as to how the PLS algorithm is

treating the "sugar-spiked" chocolate syrup samples in the standard set, a calibration

model based ooly on the "sugar-spiked" samples was developed. The values given

ta the component were the final sugar concentration after spilcing. The model for

sucrase thus obtained resulted in a correlation coefficient of 0.997 (Figure 4.23(a»

with 5 factors and a calibration RMSE of 0.31. The cross-validation error was 0.53 .

These errors are higher than those obtained for the optimized model using bath

"sugar-spiked" samples and "water-diluted" samples. However, when compared ta

a calibration model based on the "sugar-spiked" samples where the components

were given the percentage represented by the added sugar, no significant difference

cau be seen (Figure 4.23(b». A similar result was obtained for the remaining five

components (glucose, fructose, maltose, total sugars and water). These observations

indicate that PLS is modeling the added sugar rather than the total amount of sugar in

the spiked samples.

126

•22 (a)c

0 20',c

~ 18clU _

(,) ~ 16c_o :::u 0 14"O~lU - 12Ü:a 10~ R2=0.997lO-. 8 RMSE = 0.31

66 8 10 12 14 16 18 20 22

Actual Concentration(% w/w)

16 (b)c.g 14asJ:: 12c

• ~ ~ 10c-o ~ 8u~"0 Q 6B-u:a 4

R2 = 0.9961IJ... 2lO-.

0 RMSE = 0.34

0 2 4 6 8 10 12 14 16Actual Concentrations

(% w/w)

•

Figure 4.23: Calibration and validation results for sucrose modeling based on~~sugar-spiked" chocolate syrup

Plots of Actual vs. Predicted for (a) calibration set of "sugar-spiked"

chocolate syrup samp/es with total concentration as output, and (b)

calibration set of "sugar-spiked" chocolate syrup samples with added

sugar weight as output

127

•

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•

3. Calibration model build on "sugar-spiked" and 'I:sugar-diluted" chocolates

syrup

In order to increase sugar variability, a set samples were prepared that varied both

the sugar concentration and the effective final chocolate syrup dilution. These

samples are termed "sugar-diluted" chocolate syrup samples. The PLS model based

on duplicates of 12 "sugar-diluted" samples and duplicates of 22 "sugar-spiked"

chocolate syrup samples yielded a 0.992 correlation coefficient with 6 factors for the

sucrose model (Figure 4.24). The error on the calibration was 0.37 and 0.62 on the

cross-validation. The error on an external validation set composed of five 2 fold

diluted chocolate syrup samples was 1.06 on the predicted values and 2.12 on the

corrected values. AlI the other components showed sunilar performances.

The comparison of the pure component spectra obtained from the calibration

developed in sections 4.1.2.3.1, 4.1.2.3.2, and 4.1.2.3.3 shows for each component a

significaot resemblance to the sugar being modeled (Figure 4.25). However, as the

number of diluted chocolate syrup sample spectra are added to the standard set of the

"sugar-spiked" or "sugar-diluted" sample spectra, the sharpness of the profile is

slowly lost (Figure 4.25). It cao be hypothesize that with the addition of a sufficient

number of diluted samples, the profile of the sugar would resemble that of a total

sugar profile rather than the component in particular.

The conventional approaches for preparing training standard samples for the

development of a quantitative FTIR based analytical methodology for chocolate

syrup has resulted in high errors for a11 calibration models. These observations

128

25

R2=O.992RMSE =0.37

10 15 20AcmalConcentration

(% w/w)

55

•25

1:: (a).52= 20l:I.1::~(,J-

I:: ~o-C) ~

~~ 15... '-"(,J

;;:;QJ...~

10

•

o 2 4 6Factor Number

8 10

Figure 4.24: (a) Calibration results and (h) PRESS for sucrose modeling basedon "sugar-spiked" and "sugar-diluted" chocolate syrup

•129

•

<1.) 0.020 Sucrase pure component from sugar spiked and(,) water diluted chocolate syrup samples~co.e0en

:<0.0

<1.) 0.010 Sucrose pure component from sugar spiked(,)

;; chocolate syrup samples.e 0.0050en

:<0.000• <1.) 0.20 Spectrum of 10% aqueous sucrase solution(,)

~co.e 0.160en.0<: 0.12

1400 1200 1000

Wavenumber (cm- I )

Figure 4.25: Pure component spectrum of sucrose from llsugar-spiked" and((sugar-diluted" chocolate syrup

•130

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•

strongly indicate that although PLS can fit the training data, the algorithm cannot

generalize the model to chocolate syrup samples that have not been previously seen.

This inability ta predict chocolate syrup samples may be explained by the fact that

PLS is modeling the sugar added rather than the total concentration of a specifie

sugar in the samples. In addition~ the complexity of the chocolate syrup matrix could

be a cause for the modeling difficulties. (1) Underlying absorption cannot be ruled

out as aIl the contributing compounds in the sugar regÏon have not been varied

sufficiently to be accounted for. (2) The disruption of the chocolate syrup gel DlatrÏx

during dilution and addition of sugar solution interactions may be altering the

"sugar-diluted" and "sugar-spikedu

chocolate syrup samples sufficiently to prevent

the PLS algorithm from building a generalized model. (3) The combination of

dilution and spiking of the chocolate syrup samples may be uncovering or creating a

matrix effect that may be preventing PLS from reaching a stable generalized model.

4. Calibration based on the "water-diluted" chocolate syrup samples

The "water-diluted" chocolate syrup samples prepared for section 4.1.2.3.1

were used as a training set to develop PLS calibrations models ta predict the sugar

components of the chocolate syrup samples. A separate calibration model was

developed for each component (Table 4.9) and their performances were evaluated

with a validation set of 10 "water-dilutedu chocolate syrup samples. The spectral

regions used for model development were optimized individually for each sugar

using the correlation between the spectral variation and the concentration changes

from sample to sample. The calibration region for ail the sugar components covered

approximately the spectral region between 1060 and 1180 cm- l with slight variations

131

ta ta ta

Table 4.9: Summary of the performance of the optimized chocolate syrup calibration models based on diluted chocolate syrup

samples.

R1Factor

Number

RMSEon

CrossV*

Mean

EV**

Variance

EV

RMSE

on EVF

Sucrose 0.995 2 0.24 25.87 0.331 0.62 1.14

Glucose 0.995 2 0.57 15.29 0.099 0.29 0.69

Fructose 0.992 1 0.08 8.60 0.003 0.12 0.004

Maltose 0.970 2 0.08 4.45 0.009 0.25 10.13

Total Sugar 0.995 2 0.60 54.39 1.148 0.50 2.81

Water 0.998 3 0.52 30.31 2.917 0.34 0.18

Cross V: Cross-validation; EV: Externa/ validation

*: RMSE on cross-validation is ca/culated based on the predicted concentrations before correcting using the dilution factor

**: Statistical tests on externa/ validation calcu/ated based on the correctedpredictions

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of baseline or limits. The calculated concentrations of the 6 components of interest,

namely sucrase, glucose, fructose, maltose, total sugar and water contents, were used

as the values for the training set. After prediction, a dilution factor is applied to

correct the concentrations back to their reference values obtained by HPLC.

Calibration models for sucrose, glucose and total sugar YÎelded similar

perfonnances with a correlation coefficient of 0.995 (Table 4.9, Figure 4.26) at the

second factor (Figure 4.27, Appendix 5). The RMSE on the external validation was

0.62 for sucrose, 0.29 for glucose and 0.50 for total sugar (Table 4.9). The sn on the

external validation data supports the findings based on the RMSE. The one-way

ANGVA test applied to the external validation indicated that the predicted values

obtained from all three calibration models did not deviate significantly from the

actual values at the significance levels of 0.05 and 0.01 (Table 4.9). However, the

calculated F and p values suggested that the sucrose model had a higher variance in

prediction than that of glucose and total sugar. Total sugar content obtained by

adding the individual predicted sugar concentrations were similar to the total sugar

concentration predicted from the optimized calibration model (Table 4.10). Using an

ANOVA test no signjficant difference (a = 0.05 & 0.01) could be found between the

SUffi ofpredicted individual sugar concentration and the predicted total sugar content.

The calibration model for fructose YÎelded a correlation coefficient of 0.992

(Table 4.9, Figure 4.26), a value that remains comparable to the 0.995 obtained for

sucrose, glucose and total sugar. An examination of the predictions, in both cross­

validation and external validation, showed that the RMSE calculated for fructose are

the lowest at 0.08 and 0.12 for cross and extemal, respectively (Table 4.9).

133

6.3.2

• Maltose 6. Fructose3.0

S.2.8

"0 2.6 "0 S.lU ~t) 0

:.0 2.4 :.a 4.lU e'-p..,

2.2c..

4.

2.03.

1.83.

1.61.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.0 3.5 4.0 4.5 5.0 5.5 6.0

Actual ActuaJ

Il 18Glucose Sucrase

1016

"0 9 "0Co) ~.~ 0 14"0 8

:e4J e'-p.., Q..

127

610

• 5 85 6 7 8 9 10 11 8 10 12 14 16 18

Actual ActuaI40 7S

Total SugarWater

3 70

"0

"'04J-lU 0 65

030 :a:.a ee c..

Q.. 6025

SS

20

5020 25 30 35 40 50 S5 60 65 70 75

Actual ActuaI

•Figure 4.26: Plots of Predicted vs. Actual for aIl the optimized "water-diluted"

chocolate syrup-based calibration models.

Best-fit linear regression equationfor each plot can he found in Appendix 4.

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100

80

60

20

a

•

\li--.--.--.--.--.--.~-.--·--.

o 2 4

Factor

6 8 10

•

Figure 4.27: Typical PRESS for the diluted chocolate syrup models (Sucrose)

135

•·Table 4.10: Comparison between predicted Total Sugar content and sum of

predicted concentration for individual sugars

Sum of PredictedSucrose Glucose Fructose Maltose

Concentrations Total Sugar

24.94 14.76 8.56 4.30 52.56 52.80

26.16 15.41 8.60 4.50 54.67 54.63

26.10 15.46 8.60 4.48 54.64 54.63

25.73 15.23 8.56 4.43 53.95 54.14

• 25.96 15.45 9.04 4.62 55.07 55.25

26.03 15.42 8.84 4.38 54.67 54.54

26.00 15.44 8.95 4.49 54.88 54.84

25.65 15.60 8.85 4.31 54.41 54.39

25.62 15.11 8.40 4.39 53.52 53.58

Using an ANOVA test no significant difference (a = 0.05 & 0.01) could found

between the sum ofpredicted individual sugar concentration and the predicted total

sugar content.

• 136

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•

The one-way ANOVA test confirms the performance of the fructose calibration

model by detecting no significant difference between the predictions and the actual

concentrations at both 0.05 and 0.01 significance levels (Table 4.9).

The calibration model for maltose yielded a performance similar to that of

fructose although only 2 factors were needed to minimize the error of the model

(Appendix 5), with a correlation coefficient of 0.970 (Figure 4.28). This decreased

correlation may be due to the tightness of the maltose concentration range (1.50/0 to

3% w/w) and to the Iimit of detection of the fiber optic probe for this sugar (0.1 %

w/w). Compared to the other models, this calibration produced comparable error

(RMSE of 0.25) on the extemal validation (Table 4.9). Despite these observations,

the one-way ANOVA test applied to the external validation did not detect any

significant difference between the actual and the predicted values at the 0.05 and

0.01 levels of significance (Table 4.9).

The calibration for water content of chocolate syrup was the most

challenging. Modeling the system on the water band in the region of 1500 to 1700

cm-1 and at the 2900-3500 cm-1 regions proved to be inadequate. Using the water

band has always been a somewhat difficult approach because of its sensitivity to a

number of parameters. For instance as the temperature increases, the water band

tends to broaden due to an increased vibrational activity of the free water Molecules

(Iwata et al., 1997). The calibration model for water was therefore developed using

an unconventional approach that is, using the sugar region as the area where PLS

would extract information concerning the amount of water in the sample. This

approach yielded a high correlation coefficient of 0.998 (Table 4.9, Figure 4.26) and

137

•

3.2

3.0

2.8c0.~ 2.6'-.... --..c

~vu

~ 2.4c0

~u-0 '-" 2.2v.....~

• "CV 2.0'-~

1.8

1.61.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2

•

Actual concentration

(%w/w)

Figure 4.28: Plot of predicted vs. actual concentration of maltose in dilutedchocolate syrup samples

y= -0.002 +1.001x

R 2= 0.970

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•

a successfulleave-one-out cross validation test with an RMSE of 0.52, with 3 factors

to model th~ system and minimize the error (Appendix 5). An examination of the

calibration parameters showed that by the second loading factor, no shape trending

was detected, indicating that PLS has sorne difficuIties in mathematically

representing the contribution of water relative to the sugar components. This

observation is in agreement with the main assumption of the indirect method, which

was based on the fact that total sugars is equivalent to total soluble solids, a premise

easily proven wrong since the total sugar and water amount to 88% but not a 100%.

Thus the introduction of any soluble solid absorbing in the sugar region will

constitute a tremendous source of error. The prediction of an external validation set

yielded a low error (RMSE = 0.34) and the one-way ANOVA test indicated no

significant difference could be found between the prediction and the actual values at

0.05 and 0.01 significance levels.

The chocolate SYruP samples analyzed in this work have a ratio of sugars to

one another ranging from 1:1.8:3.1:5.3 to 1:2.1:3.7:6.2 maltose: fructose: glucose:

sucrase, respectively. Given the restricted variability in the inter-sugar ratio, the

concentration of each sugar could have been associated by PLS to the total sugar

content. This calibration approach raises a fundamental question: how is PLS

building the mathematical prediction matrix. From the results of the validation set, it

can be shawn that the inter-sugar ratio is different for aU the validation sampIes, and

varied between 1:1.9:3.4:5.7 (maltose:fructose:glucose:sucrose) and 1:2.0:3.5:5.9.

This indicates that PLS has not build-in an average value of the inter-sugar ratios it

was presented with in the training set. Furthermore, having started with a different

139

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•

•

stock chocolate syrup sample for each dilution, the PLS calibration were not

developed using a seriaI dilution of a single syrup sample. This new calibration

approach has the advantage ofbeing totally dedicated to sample type.

A comparison of the calibration models developed employing the

conventional approach to increasing the concentration range through addition of

different sugars, and the approach where the standard set of chocolate syrup samples

were only diluted, indicates that the latter method yields a better calibration and

higher validation results. AIthough there is a significant improvement employing the

latter method, the PLS calibration models based on water diluted chocolate syrup

samples perform poody when predicting spectra of "sugar-spiked" chocolate syrup

samples or chocolate syrup samples of a new formulation. The latter was

demonstrated using four chocolate syrup samples diluted 2 folds, scanned and

predicted using the approach based on the diluted chocolate syrup sample training

set. Of the four chocolate syrup samples, two were of a new formulation, one was a

special dark formulation similar to the formulation used in the training set, and one

was of a formulation similar in sugar ratio to the training set. The results shown in

Table 4.11 indicate that the original formulation was weIl predicted with values

falling within ± 1SD of the HPLC reference values. The special dark and the two

new formulation samples were not weIl predicted, with values falling within a ± 1sn

and ± 23SO of HPLC depending on the sample and the component. An examination

of the predicted values showed that the inter-sugar ratio for the dark formulation was

1: 1.33 :2.73 :4.87 (maltose:fiuctose:glucose:sucrose), while the new formulation has

an inter-sugar ratio of 1:1.3:2.7:2.4. Both ofthese ratios are not included in the ratio

140

• • •

Table 4.11: Test results for samples of original, new and dark syrup formulations predicted employing the water dilutio~

based calibration models

Original Formulation New Formulation New Formulation Dark Syrup

OPLCSample 1 Sample 2 Formulation

ErrorBPLC FTIR-FOP HPLC FrIR-FOP HPLC FTIR-FOP OPLC FTIR-FOPValue Prediction Value Prediction Value Prediction Value Prediction

(0,/0) (%) (0/0) (%) (%) (%) (%) (0A.)

Maltose fO.3 4.8 4.5 6.9 4.6 7.7 5.16 5.2 4.8

Fructose fO.3 8.6 8.7 9.0 9.1 8.9 10.1 6.9 9.4

Glucose f 0.5 14.9 15.0 18.7 15.8 18.6 17.7 14.2 15.9

Sucrase ±0.6 26.0 25.5 16.4 27.0 16.4 30.2 25.3 27.3

Total Sugar ± 1.0 54.3 54.3 51.0 56.6 51.6 62.9 51.6 58.6

Water ± 0.3 nia 33.8 nia 33.4 nia 30.69 nia 33.1

The concentrations are expressed in % weight by weight.

141

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•

range of the standard training set and thus may explain the high prediction error with

the different formulation samples. The most interesting observation however is

related to total sugar content. The total sugar content for both the new and the special

dark formulation was lower than that of the original chocolate syrup samples.

However, in all cases PLS predicts a higher total sugar content. The PLS prediction

of the total sugar is justified by the fact that spectral features of the new and the dark

formulations showed a higher overall absorbance (1050-1500 cm-1) (Figure 4.29) due

to the different sugar concentrations.

The contribution of varying sugar concentration to the change of the overall

profile of the mixture solution was demonstrated by varying the concentration of one

sugar while maintaining the others constant. This experiment was conducted using

aqueous solutions of sucrose and glucose to enhance the effect of the varied

concentration of carbohydrate species. Figure 4.30(a) shows spectrum of a solution

containing 15% w/w sucrose and 15% w/w glucose. Figure 4.30 (b) represents the

infrared profile of a solution with 5% w/w sucrose and 15% w/w glucose, and Figure

4.30 (c) shows the spectrum of an aqueous solution of 15% w/w sucrose and 5% w/w

glucose. The following observations can be drawn from these spectra: 1) the overall

infrared spectrum varies depending on the ratio of the different sugars, and 2) the

overall infrared spectrum changes according to the predominant sugar. Similar

results are found for any combination of 2 or more different sugars. These results

indicate once again that the calibration build on the diluted chocolate syrup samples

only could not have the ability to predict other formulation because it cannot

correlate the changes in the spectrum to the specifie sugar. Based on the results

142

•

0.2Original chocolate syrup formulation

0.1

Q)u

Special dark chocolate syrup formulationa~ 0.3;...0en~

<0.1

• New chocolate syrup formulation0.3

0.1

1400 1200 1000

Wavenumber (cm-1)

Figure 4.29: Comparison of scaled spectral profile in the sugar region of (a)original chocolate syrup formulation, (b) new chocolate syrupformulation, and (c) special dark formulation

• 143

0.35High glucose, low sucrose

0.25

0.15

QJ

0.30 Low glucose, high sucroseua-e0 0.20tI:I

.D«

• 0.10

0.3 Equal concentration of glucose and sucrose

0.2

0.1

•

1400 1200

Wavenumber (cm-1)

1000

•

Figure 4.30: Comparison of scaled spectral profiles of sucrose-glucose aqueoussolutions with (a) 15% w/w sucrose and 15% w/w glucose, (b) 15%

w/w sucrose and 5% w/w glucose, and (c) 15% w/w glucose and 5%w/w sucrose

144

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•

obtained for the different calibration models developed, the prediction algorithm

bùild employing the Uwater-di/uted" chocolate syrup will be retained for the

remainder of this study. The results and precision obtained with the method based

ooly on diluted chocolate syrup samples suggests that this approach is fonnulation

specific. An efficient way to use this approach would be to combine the method with

a preclassification, which could select the appropriate calibration method based on

he sample type recognized by the preclassification step.

The results obtained from the calibration models developed for individual

sugars, total sugar and water content of chocolate syrup demonstrate that the fiber

optic probe can be used as a sampling accessory for the quantitative analysis of

chocolate. In the following section, further testing of the calibration models which

were based on Uwater-di/uted" chocolate syrup standards will be undertaken to

establish the level of accuracy, repeatability, long-tenn performance and the

ruggedness of the FTIR fiber optic probe (FTIR-FOP) system for chocolate syrup

analysis. These tests are aimed at evaluating the viability of using the FTIR-FOP

system in routine analysis over long periods of time and under different operating

conditions.

4.1.2.4. Testing the performance of the chocolate syrup calibration models

The performance of the calibration models were tested using the following criteria:

1. Accuracy, which defmes the closeness of the prediction to the "real values", was

tested by predicting a series of spectra of known concentrations not included in

the training set.

145

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•

2. Repeatability, which is defmed as the closeness of the predicted values when the

same sample is analyzed several times in a row, was tested by recording the IR.

spectrum the same chocolate SYfUP sample 10 consecutive times.

3. Calibration stabiLity testing is aimed at assessing the ability of the calibration to

produce consistent results from the same sample recorded at different times over

a study period. Short-term stability refers to same-day or day-ta-day comparison

and the long-term stability deals with week-to-week and month-to-month

comparison.

Accuracy was evaluated using 5 samples. The accuracy of maltose was the most

important as not ooly it is the sugar present in Least amounts in chocolate SYfUP, but

the calibration model developed for this component yielded the lowest correlation

coefficient (Table 4.9). The SD associated with the analysis ofeach sugar component

by HPLC and KarL Fisher for water is shown in Table 4.12, along with the error

range associated with a ± 1SD error level and ± 2SD. Using the errer ranges

presented in Table 4.12, the FTIR-FOP predictions (Table 4.13) were compared to

the ± 1SO error range and the ± 2S0 error range. Except for the prediction of sucrose

in sample 5, and water and total sugar for sample 2, which feH within the ± 2S0

error Level, alL the predictions were within a ± 1SO error from the reference values.

The graphie representation of the accuracy test is shawn in Figure 4.31.

In this study, repeatability was based on the collection of 10 consecutive spectra

for each of a set of 5 samples. The SO for each sample and each component was

computed and averaged to obtain the experimental precision of the method.

146

•

Table 4.12: Summary of the HPLC error ranges

Average Error by± ISD Error ±2SD ErrorHPLCConcentration

(SD)Level Range Level Range

• Maltose 4.41 ±0.3 4.11-4.74 3.81-5.01

Fructose 8.73 ±0.3 8.43-9.03 8.13-9.33

Glucose 15.28 ±0.5 14.78-15.78 14.28-16.28

Sucrase 25.82 ±0.6 25.22-26.42 24.62-27.02

Total Sugar 54.25 ± 1.0 53.25-55.25 52.25-56.25

Water 32.62 ±0.3 32.32-32.92 32.06-33.22

• 147

•Table 4.13: FfIR-prediction for 5 samples used to evaluate the

accuracy of the method

Sampie Sampie Sample Sample Sample1 2 3 4 5

Maltose 4.36 4.34 4.36 4.47 4.35

Fructose 8.80 8.63 8.60 8.75 8.60

• Glucose 14.99 14.90 15.01 15.15 14.81

Sucrose 25.34 25.14 25.31 25.51 24.97*

Total53.37 53.05* 52.86 53.8 53.45

Sugar

Water 32.94 33.21 * 32.96 32.74 32.66

* These predictionsJal! within the :r 2SD error level.

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•

5.5

5.0

E:: 4.5 r l l0".g --- r'- ~

1.....~E::

1eu(.)

"*- 4.0 1E::0 -u• 3.5 Actual•

• Predicted

3.04.10 4.15 4.20 4.25 4.30 4.35 4.40 4.45

Concentration

(%w/w)

Figure 4.31: Graphie representation of aecuraey testing for Maltose

Graphie representation ofaccuracy testingfor maltose with a:t lSD error

leve1.

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Table 4.14 compares the SD associated with the reference method and the FTIR.-FOP

system for each component. The FTIR-based method demonstrates a higher

precision than HPLC with SD that are consistently lower. For instance, the precision

on maltose is 6 times lower for the FTIR.-based method with values ± 0.05%

compared to ± 0.3% for the HPLC. A significant drop in error although oot as

dramatic was observed for sucrose, where the error decreased from ± 0.6% to ±

0.49%.

The basic assumption in testing for reproducibility is that the sample is stable

over the time period of interest. Chocolate syrups are manufactured to be stable for

several months at room temperature, although sorne evaporation will take place after

opening the sample. The stability of the calibration models was tested using the one­

way ANOVA to compare the means and variance of the different groups of

prediction. The statistics for same-day reproducibility showed no significant

ditference among the two groups of data at both 0.05 and 0.01 significaoce levels

(Table 4.15). The day-to-day reproducibility showed no sigoificant difference

between Day 1, Day 2 and Day 3 at significance levels of0.05 and 0.01 (Table 4.16).

Finally, the week-to-week statistics showed no significant differences for all three

weeks at bath 0.05 and 0.01 significance levels (Table 4.17).

The results indicate that over a four-week period the prediction for water has

remained consistent and the sugar concentrations did not show any increase

suggesting that the chocolate syrup samples did oot undergo any sigoificant change.

Furthermore, the absence of significant differences between the tested sets and the

actual values prove that the calibration has stable prediction efficieocy for up to one

month.

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Table 4.14: Error on precision for chocolate syrup analysis by FTIR-FOP and

HPLC based on predictions of ten repeats

ComponentError by FrIR-FOP

(ISO)

Error by HPLC

(ISD)

• Maltose ±O.O5 ±O.3

Fructose ±O.OS ±O.3

Glucose ±O.29 ±O.S

Sucrase ±O.49 ±O.6

Total Sugar ±O.52 ± 1.0

Water ±O.19 ±O.3

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Table 4.15: Summary of the one-way ANOVA testing for same day

reproducibility

Day l-Run 1 Day l-Run 2

Mean Mean FVariance Variance

(%) (%)

• Maltose 4.40 0.0024 4.43 0.0039 0.638

Fructose 8.40 0.0055 8.55 0.0236 3.770

Glucose 15.01 0.0114 15.10 0.0391 0.839

Sucrase 25.23 0.0349 25.47 0.1 121 1.992

Total Sugar 53.11 0.2869 54.02 0.5471 4.943

Water 33.12 0.1113 32.74 0.1852 2.539

The mean concentrations are expressed in % weight by weight.

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Table 4.16: Summary of the one-way ANOVA testing for day-to-day reproducibility

Day 1 Day 2 Day 3

FMean

VarianceMean

VarianceMean

Variance(0/0) (%) (0/0)

Maltose 4.46 0.0029 4.40 0.0098 4.43 0.0039 0.532

Fructose 8.44 0.0357 8.63 0.0358 8.55 0.0236 1.518

Glucose 14.67 0.0691 15.03 0.0960 15.10 0.0391 0.322

Sucrose 25.26 0.1295 25.51 0.4897 25.47 0.1121 0.374

Total Sugar 53.50 0.797 53.485 0.7083 54.02 0.5471 0.678

Water 32.83 0.1332 33.06 0.0397 32.74 0.1852 1.129

771e mean concentrations are expressed in % weight hy weight.

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Table 4.17: Summary of the one-way ANOVA testing for week-to-week reproducibility

Mean(%)

Week 1

Variance Mean(%)

Week2

VarianceMean(%)

Week3

Variance

F

Maltose 4.46 0.0029 4.42 0.0049 4.40 0.0022 1.108

Fructose 8.44 0.0357 8.32 0.0173 8.56 0.0131 3.210

Glucose 14.97 0.0691 14.83 0.0566 15.09 0.0279 1.571

Sucrase 25.26 0.1295 24.99 0.1322 25.41 0.0787 1.969

TotaI Sugar 53.51 0.1335 33.19 0.1282 32.87 0.0843 1.668

Water 32.83 0.7974 52.85 0.5751 53.82 0.4397 2.004

The mean concentrations are expressed in % weight by weight.

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4.1.2.5. Testing chocolate syrup calibration ruggedness

The ruggedness of a calibration is defmed as its ability to maintain the same

level of performance despite changes in the environmental and/or operating

conditions. The ruggedness of the optimized chocolate syrup calibration models was

tested for the following parameter changes:

1. decreasing the number of co-added scans of both the background and the

sample spectrum

2. decreasing the number of co-added scans of the background spectrum

only

3. increasing the standing time of the sample after its preparation for

analysis

4. working with a non-purged system

1. Effect ofreducing measurement time

The optimized analysis protocol established ln section 4.1.2.2.4

recommended that the sample and the background spectra be recorded using 64 co­

added scans each. AIthough the total acquisition time (background & sample

spectrum) is 4 min, a decrease in the analysis time would be an asset given that it

results in an increase of the number of analyses per hour. Two approaches were

tested to reduce the analysis time.

First the number of co-added spectra were decreased from 64 to 32 co-added

scans for both the background and the sample collection. The PLS predictions from

spectra of the same sample collected under optimized conditions were compared to

the predictions obtained using the spectra collected with decreased number of co-

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added scans employing a one-way ANOVA test. At 0.05 and 0.01 significance

levels, no significant difference was found between the variances and the means of

the two sets (Table 4.18). This conclusion was reached for all6 components, sucrase,

glucose, fructose, maltose, total sugar and water content.

The second approach for decreasing the analysis time was based on reducing

the number of background scans from 64 ta 16 co-added scans. The result in terms of

analysis length was similar ta the previous approach. The one-way ANOVA test

applied ta compare the predictions of the spectra collected under standard and

decreased number of background scans, showed no significant difference for aIl the

components between the two data collection methods at significance levels of 0.05

and 0.01 (Table 4.18).

A comparison of the F values obtained from the two one-way ANOVA tests

showed that the values obtained for the protocol using the 32 co-added scans for both

the background and the sample spectra were lower than the ones calculated using the

data collected with 16 scans for the background spectrum and 64 for the sample. This

difference indicates that collecting 32 seans for each of the background and the

sample speetrum is less different from the original protocol, than the second

approaeh. The difference May be directly related to the slight band broadening that

oecurs when the number of scans is decreased. If the background spectrum and the

sample spectrum are collected with the same number of co-added scans, the ratioing

of the two speetra is then more efficient than a ratioing based on speetra with slight

intensity differences. Despite the possible rationing discrepancy, the performance of

the calibration is not jeopardized and therefor~ either approach would be adequate

for reducing the analysis time to 2 min.

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Table 4.18: One-way ANOVA test results for the decreased analysis time

evaIuation

32 scans for sample and 16 scans for background

background spectra spectrum only

MeanVariance F

MeanVariance F(%) (%)

• Sucrase 25.11 0.025 0.002 25.78 0.321 5.648

Glucose 14.89 0.011 0 15.26 0.099 5.568

Fructose 8.36 0.017 0.1115 8.52 0.320 2.347

Maltose 4.39 0.001 0.096 4.49 0.014 3.769

Total Sugar 53.43 0.676 0.511 54.41 1.158 5.795

Water 32.90 0.038 0.218 33.09 0.150 0.133

The mean concentrations are expressed in % weight by weight.

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Furthermore, observations made in previously published work (Adhihetty et al.,

1991) indic~tes tbat speetra with a higher noise level could be predieted using

calibration models based on spectra with a lower noise level. This observation is

consistent with the results obtained in this work.

2. Evaluation ofthe effect ofsample standing lime

In this work, the standing time has been determined to fall between 2 and 5

min. However, in sorne situations such as batch processing, a sample may stand for a

longer period of time before it is analyzed. To assess the possible effect of the

standing time, 5 samples were prepared and left for 30 min before they were

analyzed. The resulting predictions were compared to the concentrations obtained

from spectra of the same samples collected 3 min after preparation. The one-way

ANaVA test obtained for each component indicated that there was no significant

difference between the two protocols at the significance levels of 0.05 and 0.01

(Table 4.19). These results imply that the observed cocoa particle sedimentation

occurring during the 30 min standing time does not affect the performance of the

calibration.

3. Evaluation ofthe effect ofusing an non-purged ~pectrometer

Purging the spectrometer and the sample compartment require the installation

of purge lines to provide dry air to the system. Purging the spectrometer would not

be a problem since the spectrometer housing cao be sealed and kept dry with a

dessicator. However, purging the sample compartment will require a more

demanding setup. This test is aimed at assessing the potential loss of performance of

the calibration models when predicting sample spectra collected using a system that

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Table 4.19: One-way ANOVA test results from sample standing test

MeanVariance F

(%)

Sucrase 25.48 0.049 5.501

• Glucose 15.11 0.017 5.304

Fructose 8.45 0.003 2.175

Maltose 4.38 0.019 0.484

Total Sugar 53.76 0.188 4.268

Water 33.09 0.150 0.133

The mean concentrations are expressed in % weight by weight.

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has not been purged. Spectra collected with the non-purged system were predicted

and the resulting concentrations were compared to those obtained from the original

data. The one-way ANOVA test showed no significant difference between the meanS

and between the variance of the data sets at 0.05 and 0.01 significance levels (Table

4.20).

The absence ofdeterioration in the performance may be directly related to the

ratioing of each sample spectrum against a background spectrum taken just before

analysis. This approach enables the FTIR-based protocol to compensate for any

changes occurring in the analytical environment between analysis. In effect~ taking a

background before each sample simulates the effect of a purge, which purpose is to

minimize changes in the analytical environment.

4.1.2.6. Performance of calibration models based on diluted samples to

predict undiluted chocolate syrup samples

Having established the potentiai of employing FTIR spectroscopy in

combination with the fiber optic probe sampling accessory for the analysis of diluted

chocolate syrup samples, the potential of using the same calibration models to

predict undiluted samples is investigated. The predictions from spectra of undiluted

chocolate syrup samples were compared to the ones obtained through dilution.

Table 4.21 summarizes the results from the one-way ANOVA tests used to

compare the performance of the calibration model when predicting undiluted

chocolate syrup samples. For ail components, the difference in sample type did not

translate into a significant difference between the prediction sets at a 0.05 and 0.01

significance levels (Table 4.21). However, an examination of the absolute

differences between the predictions of the two methods indicated that ooly two of

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Table 4.20: One-way ANOVA test results for the comparison of purged and

non-purged systems

MeanVariance F

(%)

Sucrase 25.31 0.155 0.867• Glucose 15.05 0.072 1.317

Fructose 8.48 0.035 1.138

Maltose 4.42 0.007 1.760

Total Sugar 53.60 1.007 0.925

Water 32.72 0.061 1.542

The mean concentrations are expressed in % weight by weight.

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Table 4.21: Summary of the one-way ANOVA tests for direct predictions

Dilution method Direct prediction

FMean Mean

Variance Variance(oAJ) (%)

• Maltose 4.44 0.073 4.48 0.0734 0.091

Fructose 8.60 0.0033 8.68 0.3786 0.066

Glucose 15.28 0.0990 15.72 0.0910 0.957

Sucrase 25.87 0.3308 26.43 2.5027 0.267

Total Sugar 54.34 0.1727 56.17 11.3313 1.456

Water 32.65 0.1279 30.51 12.9173 1.757

The mean concentrations are expressed in % weight by weight.

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the 5 undiluted samples yielded predictions within the error level for the FTIR-FOP

method. This result suggests that the extrapolation of the PLS models increases the

error of predictions, a conclusion also supported by the RMSE values. The RMSE

computed for the prediction of diluted samples before correction and after correction

and the predictions for undiluted samples show a steady increase, an indication of the

added error source from one method to the next (Table 4.22). For the diluted sample

prediction, the RMSE values ranged between 0.06 for fructose and 0.66 for water.

The prediction of undiluted syrup samples yielded the highest error with RMSE of

0.44 for fructose and 4.05 for total sugar. The overalI increase of error may be due to

the extrapolation of the model and the difference between the matrix of the diluted

syrup samples in the training set and the undiluted samples. The addition of

undiluted samples to the calibration in order to model the new components resulted

in low correlation coefficient and a high prediction error. These results indicate that

the difference in matrix between the diluted and the undiluted chocolate syrup

samples may be too significant to obtain a prediction algorithm that models the

undiluted samples without significantly deteriorating the modeis.

4.1.2.7. Transferability

Transferability is a necessary part of the development of an analytical

method. It tests the performance of the calibration models applied to data collected

using an instrument or accessory other than those used for the development of

calibration models. Transferability covers three main types: (1) transferability

between spectrometers of same make, (2) transferability between spectrometers of

diffèrent make, and (3) transferability between two different sampling accessories.

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Table 4.22: Table ofRMSE calculated for aIl three prediction levels

Diluted

concentrations

Corrected diluted

concentrationsDirect prediction

Maltose 0.13 0.25 0.41

• Fructose 0.06 0.12 0.50

Glucose 0.15 0.29 1.03

Sucrase 0.31 0.62 1.72

Total Sugar 0.63 1.25 4.05

Water 0.66 0.34 2.46

'-'

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Once the wavenumber difference from instrument to instrument was

standardized' (Hawkins et al., 1983), calibration transfer research concentrated on

developing methods to insure transfer without loss of predictive ability. Early work

has shown that detector non-linearlty, frequency accuracy, incident angle, and

variations in the purge were important parameters to control to obtain transferable

calibrations (Adhihetty et al., 1991). Calibration transfer can be based on a piecewise

direct standardization (POS) method, which uses a small set of samples to develop a

transformation matrix between the two instruments (Wang and Kowalski, 1992). A

more complex approach involving using artificial neural networks (ANN) bas also

been successfully used in cases where the data was apparently incompatible

(Despagne et al., 1998). Finally, the finite impulse response approach (FIR.) requires

no additional spectra recording and yields results comparable to more conventional

methods such as direct transfer and POS (Blank et al., 1996).

The calibration developed for maltose in section 4.1.2.3.4 is used as the

reference calibration, also referred to as the Master Calibration, because it is the

model with the lowest prediction performance. Briefly, this PLS calibration yielded a

correlation coefficient of 0.970 (Table 4.9) with an error of ± 0.05 on the

repeatability and RMSE on the external validation of 0.27 (Table 4.9). These values

will serve as the basis for comparison for the remainder of the transferability study.

1. Transferahility between same make spectrometers

For our study, the Bornem MB Series spectrometer and the Bornem WorkIR

were used. This choice was based on the possibility of transferring the methodology

developed for the laboratory rvm Series to its industry-oriented version., the WorkIR.

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The spectra of five chocolate syrup samples were recorded using the optimized

protocol described in Table 4.23 employing the WorkIR. spectrometer equipped with

the fiber optic probe accessory. The spectra were then analyzed using the Master

Calibration and the results were statistically compared to the results obtained for the

same samples from the spectra recorded on the ~ spectrometer. A one-way

ANOVA test deteeted no significant differences between the two sets at significance

Levels of 0.05 and 0.01 (TabLe 4.23). Although the RMSE calculated for the

predictions from the WorkIR was 0.19 (Table 4.23), a value four times higher than

the error on maltose using the MB system., the error remains Low. The transferability

testing for the remainder of the components yielded excellent results with the one­

way ANOVA tests showing no difference between the prediction of the~ and the

WorkIR using the Master Calibration at 0.05 and 0.01 significance levels (TabLe

4.23).

The transferability between the Bornem MB and the Bomem WorkIR yieLded

excellent resuLts indicating that the transfer of calibration does not deteriorate the

predictions. These results are consistent with previously pubLished reports (Wang et

al., 1998) in which transferability between different Bruker instruments was tested.

2. Transferability between FTIR-spectrometers from different manufacturers

This part of the study focuses on transferring the master calibration from the

Bornem MB spectrometer to the Nicolet Magna 550. The concems when doing such

a transfer are reLated to the differences in the optics of the two instruments, which

May affect the throughput, bandwidth and band position. The latter concem is greatly

minimized in FTIR spectrometers where the spectrometers are calibrated

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Table 4.23: Summary of performance of the Master Calibration in different transferability scenarios

Fiber Optic - Bomem WorkIR Fiber Optic - Nicolet Magna 550Ge-HATR - Nicolet Magna 550

Different sampling accessory andSpectrometer of same make Spectrometer of difTerent make

different speetrometer make

RMSE F RMSE F RMSE F

Maltose 0.09 11.248 0.15 0.294 0.10 0.497

Fructose 0.12 0.808 0.40 28.860 0.11 5.929

Glucose 0.19 2.285 0.52 0.346 0.34 0.939

Sucrase 0.96 3.9]6 0.9] 0.998 0.65 ].570

Tot al Sugar 0.42 4.608 1.75 0.496 1.28 2.297

Water 0.11 9.034 0.71 1.037 0.42 0.681

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against the frequency of a He-Ne laser. The spectra of a chocolate syrup sample

collected on -the Magna 550 and the Bornem~ both employing the FOP were

compared and 00 significant inteosity difference or band shift was observed.

The prediction of the spectra of the test set by the master calibration did not

show any significant difference with those obtained from the spectra collected on the

MB at 0.05 and 0.01 significance levels. The RMSE caIculated was 0.16 (Table

4.23), an error deemed very good for the transferability on values ranging between

4.3 and 4.5. Interestingly, the RMSE caIculated from the prediction of the Magna

spectra by the Master Calibration (0.16) were comparable to those calculated from

the prediction of the spectra collected on the WorkIR (O. 19). The performance of the

transferability of the other components was comparable to that of maltose. These

results which may well indicate that the technology of FTIR spectrometers has

reached point where a certain degree of standardization exists despite the different

approaches taken by the manufacturer in building their instruments.

3. Transferability between different sampling accessories

Transferability between different sampling accessories is tested here by

investigating the potential of the Master Calibration ta predict spectra collected on a

Ge-HATR mounted on a Nicolet Magna 550. This type oftransferability is the most

complex as the pathlength and depth of penetration at each wavelength is changed.

Calibration transfer between instruments has been discussed earlier and reports show

that a number of approaches can be used ta develop a transfer matrix. Transfer of a

calibration model build on one accessory and to a different accessory has not yet

been reported in the literature. Furthermore, the transfer as reported in the section has

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also not been reported elsewhere. However, a recently published paper studying the

impact of changing the design of the accessory, more specifically a fiber optic

sampling accessory, showed that most variables can be compensated by an

adequately developed transfer matrix (Todd and Masterson, 1999). The change in the

sampling accessory could be paralleled to changing the accessory for a new type.

The examination of the spectra (Figure 4.32) shows a significant intensity difference

between the spectrum of a sample recorded with the Ge-HATR-Magna setup and the

fiber optic-Bomem MB setup. This intensity difference may lie in the depth of

penetration and refraetive index differences between the two sampling accessories.

To correct for the difference in absorbance, 19 samples diluted between 30 and 70%

were predicted from their spectra recorded with the Ge-HATR using the master

calibration. The predictions are plotted against the calculated concentrations of the

diluted samples (Figure 4.33). The graph showed a linear relationship between the

gravimetrically obtained concentrations and the predictions of the fOP-based

calibration. Therefore, an equation of the best-fit linear regression was calculated (y=

-0.09328 + 0.57727x) and used to correct the predictions outputted by the Master

Calibration. This correction may be compensating for the difference in pathlength

and refractive index between the two accessories. Once these predictions were

obtained from the master calibration, the results were corrected using the dilution

factor to enable a comparison between the predictions of the spectra collected on the

fiber optic-Bomem MB spectrometer and those from the Ge-HATR-Magna 550.

Five samples, not included in the set used to develop the correction

algorithm, were used to assess the performance of the transferability algorithm. The

169

•

.""\i \/ \l ,

! \J •. '"J ,. .

1 l1 1

i ~1 ~. .

r, : l1 V 1

/ \[' \J '1 \

W 1 \Î\ I~~ i

i ~ (b) '-.J

-~

0.80

0.70

0.60Q.)(.)

~ 0.50-e0en 0.40.0<

• 0.30

0.20

0.10

1800 1600 1400 1200 1000

Wavenumbers (cm-1)

Figure 4.32: Comparison of scaled spectra of a sarnple collected using (a) a Ge­HATR mounted on a Nicolet Magna 550 and (b) the fiber optic probernounted on a Bornem MD

• 170

•

4.0

3.5

c 3.00.~

'-1:: 2.5Q) ...-0c~0 2.00

-c '$.Q) '-"".....as 1.5]

• asu 1.0

0.5

2 3 4 5 6 7

•

Predicted concentration(%w/w)

Figure 4.33: Transferability algorithm development (Maltose)

Plot of the values predicted by the FOP-based master calibration from

spectra collected on a Ge-HA 1R mounted by the Nicolet Magna 550

against the calculated concentration ofthe maltose in the diluted samples.

Correction algori/hm: y= -0.09328 + O.57727x

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results (Table 4.23) obtained were excellent as the RMSE calculated based on the

prediction hy the Master Calibration of spectra collected using the fiber optic­

Bornem rvm system were substantially lower than those of ail the other

transferability tests conducted in this work. The most likely reason for this

improvement is the quality of the data obtained with Ge-HATR sampling accessory.

Recalling from section 4.1.1.2, the Ge-HATR used with the Nicolet Magna 550 had

the lowest noise level and therefore the highest SNR compared ta the fiber optic

probe used with the Magna 550 and the Bornem MB. For maltose the RMSE was

calculated ta be 0.10 compared to 0.19 when transferring to the WorkIR and 0.15

when transferring to the Nicolet Magna 550 equipped with the fiber optic probe

sampling accessory (Table 4.23). The predictions from the transferability algorithm

were compared to the predictions of the optimized method using a one-way ANOVA

test, there was no significant difference between the two sets at significance levels of

0.05 and 0.01 (Table 4.23). The results obtained for the remaining five components

are comparable to those obtained for maltose (Table 4.23).

The transferability algorithm (Appendix 6) developed to impart the Master

Calibration the ability to predict spectra that were collected using a different make

spectrometer and a different sampling accessory has proved to be successful. The

errors on the predictions are low. From a statistical point of view, the predictive

accuracy of the Master Calibration was not significantly affected during the transfer

step. The results are to be considered excellent given that two correction steps were

applied ta the initial prediction (transfer algorithm and the dilution factor) without a

significant decrease in predictive performance.

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4.1.3. Integrated software for dedicated analyses

Data ,collection was automated using a modified version of the COAT Scanll)

(Thermal-Lube, Montreal, Canada) software developed by Dwight Analytical

(Toronto, Canada). The program is a turnkey, user-friendly interface that automates

data collection and concentration prediction. The final output of the program is a

printable sheet with the predieted concentration for aU 6 components and a set of

charts that enable visual monitoring of the profiles of the analyzed samples.

4.2. SUGAR PROFILE MODELING BY ARTIFICIAL NEURAL NETWORKS

From the previous section, two important observations can be made with

regard to using PLS as a modeling approach. First, PLS cannot deal with samples

that have low variability in the data such as in the case of production line samples.

Second, PLS has demonstrated a limited ability to extract specifie information

related to each sugar in the chocolate syrup matrix and that despite sample dilution to

widen the range and spiking to increase the variability in the concentrations and

break any inherent co-linearity of data. This is most likely due to a matrix effect that

affects the spectrum profile and targets a number of spectra as outliers. An expert

system based on a set of mies was developed to enable the building of a calibration

in a fully automated fashion (Aries et al., 1993). This approach helps the analyst to

deteet outliers in rapid and efficient way.

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Artificial neural networks (ANN) are intricate modeling systems that have the

ability to extract highly complex latent patterns present in the data. ANN have been

~xtensively used for classification and complex recognition processes. ICs ooly

recently that we have seen an increased interest in the quantitative abilities of ANN

(Bos et al., 1993).

In this part of the wodc, ANN is applied to the infrared spectra of chocolate

syrup samples to (1) evaluate its potential at classifying the undiluted chocolate syrup

samples based on their spectral profile in the sugar region, and (2) evaluate its potential

in quantitative anaIysis of chocolate end products. ANN will aIso be applied to sugar

solutions, in order to compare the performance of ANN to PLS in the quantitative

analysis of sugars in aqueous solutions.

4.2.1. ANN terminology

The ANN terminology used in the rest of this document is usedldefmed as

foUows:

1) The terms processing e/ements (PE), neurons and nodes are used

interchangeably.

2) Examples or input vectors or patterns are the samples that constitute the

data set presented to the ANN. Each example is composed of a predefmed

number of inputs or variables.

3) Inputs or variables in the case of an infrared spectrum are the data points.

Therefore, the number of inputs is equaI to the number of data points in the

region of the infrared spectrum that is being used.

4) A learning event is the presentation of an input vector to the ANN during

training.

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5) Epoch is equal to 1 presentation of the complete data set to the ANN during

training.

6) Leaming interval is the number of learning events presented to the training

ANN before recalling the test set to evaluate the model being developed.

4.2.2. Classification of chocolate syrup by ANN

In section 4.1.2.3, it was concluded that chocolate syrup analysis is best served

by formulation specifie calibration models. A step which allows the identification of

the type of formulation being analyzed is therefore required in order to employ the

appropriate set of calibration models.

The ability of ANN to classify data into groups based on subtle changes has

long been recognized (Jansson, 1991). However, chocolate syrup is a highly complex

matrix where a number of variables can be altered with a major impact on the

contribution of minor chocolate syrup constituents to absorbance of the matrix. In arder

to assess the potential of ANN to classify different chocolate formulations, two main

approaches were investigated.

A probabilistic neural network (PNN) where the learning is supervised by

assigning a class number or type for each example was used. The spectra of the

chocolate syrup samples collected using a fiber optic probe (FOP) ATR sampling

accessory mounted on a Bornem MB Series spectrometer were translated ioto their data

points in the region of 1040 to 1500 cm-1 using an in-house Visual Basic macro. The

resulting spreadsheet contained 52 examples covering three types of chocolate

formulation with 115 inputs and was used as the input file for the development on the

ANN. Although PNN is a computationally intensive method, it is a straightforward

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•

design that does not dependent on training but on the size of the training set for its

performance (NeuraIWare, 1996). The optimized PNN required 0.2 smoothing factor.

This factor is applied to decrease the difference between the inputs and the outputs of

, the network by slightly varying the distribution based on the degree of smoothing. The

recalled input fIle was predicted using the optimized network showed that 50 of the 52

examples were correctly classified and 2 were not classified. The two unclassified

input vectors were part of the original formulation series and were rejected because

they were significantly different form the other examples. This was confirmed by

examination of the residual difference between the rejected spectra and a typical

spectrum of an original formulation chocolate sYrUP sample (Figure 4.34). The

prediction based on an external validation composed of 17 spectra taken from all three

formulation types were correetIy classified or rejected as incompatible with the

classification it was trained on. The five unclassified spectra were compared to a

typical spectrum of its supposed group type, and indeed, the residual spectra showed

significant differences. These results prompted an analysis of the threshold at which

ANN rejects an example. For this purpose, the spectrum of an original formulation

chocolate syrup sample was modified by adding noise or changing the intensity of the

absorbance by very small increments towards higher and lower intensities. These tests

showed that a change of more than ± 0.003 Abs units which is equivalent to 0.04

change in total sugar content, will result in sample rejection. This method can therefore

be extended to include a spectrum ouiller rejection function. The advantage of the PNN

approach over a discriminant analysis for instance is that the outlier is not misclassified

but rather flagged as an unknown. In discriminant analysis, on the other, the distance

176

•

Typical spectrum of originalchocolate syrup formulation

Residual spectrum

Unclassified spectrum of originalchocolate syrup fonnulation

0.3

0.2

0.1 lL=--==----========:=:::::=:::::==::.---------,:""".......---­eu 0.4C,)

@ 0.3.e~ 0.2

..0

<t: 0.1-0.02

-0.04

-0.06

-0.08

-0.10~----.-----y--------.-----.----__r_--~--....----~--=---~-

•1400 1200 1000

Wavenumber (cm-1)

Figure 4.34: Residual spectrum from an unclassified spectrum by PNN and atypical spectrum of its actual group type

• 177

•

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•

from a given class \vill be computed, and based on the smallest value, the spectrum is

classified. This will lead to a misclassification of the outlier that requires actual

examination of the data for rejection. In our work, a discriminant analysis model based

on the same training set as the optimized PNN was developed. The spectra rejected by

PNN were classified as high moisture samples instead of an original formulation. The

caIculated distance however was 0.43 between the ouiller spectrum and the high

moisture class, while its distance to the original formulation type class was 0.47. In

either case the spectrum must be rejected.

A self-organizing map (SOM), known as Khonen network, was aIso applied to

the classification of chocolate syrup sample spectra. A SOM is an unsupervised

network, which will group the input vectors based on internaI similarities without

comparing the results of the training to actual output values (CaudilI, 1988b). This

approach was used to establish whether the spectra of the different formulations had

sufficient differences in their characteristics to be classified without prior knowledge of

the specifie sample type of each example. The optimization of the SOM-NN required

adjusting the learning rate and the initial weight of each node. Regardiess of the values

given to either variable, the classification was poor with 60% of training samples being

misclassified. These results suggested that the training set may be too small for a good

generalization or the differences relating to each group of chocolate syrup formulation

are not as significant enough to be distinguished without supervision of the network.

The use of ANN modeling as a classification step prior to quantitative analysis

by an optimized PLS calibration model can be done efficiently through a PNN type

network. This approach not only provided 100% exact classification, but could aiso be

178

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•

used as a outlier detection technique. On the other hand, SOM-NN did not result in an

appropriate 'Classification, which indicates that the spectral changes due to the

difference in formulation may not be the most prominent in the data set. This may be

overcome by increasing the training set size.

4.2.3. Analysis of chocolate syrup by ANN

A feedforward architecture and a backpropagation learning curve was chosen ta

start the optimization step because it is the architecture most suited for quantitative

analysis (Liu et al., 1993). The variables were scaled between 0 and 1 using a linear

function while a sigmoid transfer function, also known as logistic function, was applied

to the data because ail outputs, namely sucrose, glucose, fructose, maltose and total

sugar concentrations, have positive values. The cumulative delta learning role was

based on updating the weight of each neuron at the end of a leaming interval rather

than after each learning event by a delta factor that has been cornputed from each

learning event summed at the end of the learning interval. The cumulative delta role is

aimed at minimizing the difference between the actual output and the predicted output

(NeuraIWare, 1996).

The optimization of a ANN shouid address: the number of hidden layers, the

number of neurons in each layer, the learning rate, the momentum and the learning

interval. The learning rate controis the weight change in arder to reach the minimized

error of the mode!. The use of a large learning rate (close ta 1) leads ta faster learning

process but a wider oscillation in the error which may lead ta a non-convergence where

the model will converge towards a solution that is not optimum (NeuroShell, 1993). To

avoid large oscillation in the weights, the momentum, which determines the proportion

179

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•

of the new weight change with respect to the previous, is used. It is suggested low

momentum values can lead the network into a local minimum, a non-optimum solution

for the network (Matlab, 1998).

First the ANN was tested with respect to its potential sensitivity ta the test set.

A 10 full cross-validation based on 10 non-overlapping randomly selected tests sets

was conducted. Each of the resulting 10 training sets was used in a 2 hidden layer

feedforward backpropagation neural network with 10 processing elements (PE) in the

first hidden layer and 5 in the second. The learning rate and the momentum were set ta

0.1 while the learning interval used was 200. The root mean square error (RMSE)

calculated based on the recalled input spreadsheet showed no significant difference

between the errors and correlations yielded for ail five components. These observations

warranted the use ofa randomly extracted test set for the remainder of the study.

The architecture of the ANN based on the 115 inputs and the 51 input vectors

was then optimized by testing a 1 and 2 hidden layer models, at a fixed learning rate of

0.1, a preset momentum of 0.1 and a learning interval of200. The RMSE and R2 of the

recalled input spreadsheet were used to evaluate the performance of the architecture.

From this point on, the architecture of the ANN will be simply referred to as the

number ofPE in layer 1 : number ofPE in layer; for instance a 15:5 ANN has 15 PE in

the fIfst hidden layer and 5 in the second. For the one hidden layer model, the number

of PE was set to 5, 10, 15, 20, 30 and 40. For the two hidden layers the 5:5, 8:4, 8:8,

10:5, 10: 10, and 15:5 architectures were compared. Figure 4.35 monitors the error of

sucrose, glucose, fructose, maltose and total sugar as the architecture is changed. The

error level in combination with the R2 values was used to determine that a 5:5

180

• 0.55 Maltose 0.5 Fructose

0.50 R2S:S= 0.973 R2

s:S= 0.947

0.45 0.4

UJ t1l 0.3UJ UJ

~ ~0.2

0.20.1

0.20Ci Ci Ci Ci Ci C ~ ~ CIO "'l Ci "'l Ci Ci Ci Ci Ci Ci "'l ~ QQ ~ Ci '"Vi ci Vi .::; .::; .::; Ot:i .::; - ti Vi ~ Yi è::i .::; .::; Vi Ot:i ~

.;-.:"'l QQ

~,... QQ

~N t""I ..,. - N ..,.ANN Architecture ANN Architecture

0.32 0.40Glucose Sucrose

0.30R2s:s= 0.978 0.35 R2

s:s= 0.9980.28

UJ 0.26 UJ 0.30UJ UJ

~ • ~0.24 0.250.22

0.200.20

• 0.18 0.15 •Ci Ci Ci '"

..,. QQ on Ci Ci Ci Ci Ci "'l ..,. QQ '" :2,;.j è::i 0 Yi Oc; Oc; Ci § Vi Vi è::i ti è::i 0 ë Yi cë Oc; ci Yi,... ..,. N,... ..,. ~.

ANN Architecture ANN Architecture

o.

O. Total Sugar

O. R2S:S= 0.998

UJ O.UJ

~ o.

o.o.O.

è::i .::; tiN

ANN Architecture

• Figure 4.35: Error (RMSE) monitoring for chocolate syrup ANN architectureoptimization

181

•

•

•

architecture was the best suited for the quantitative analysis of sugars in chocolate

syrup. This configuration yielded the lowest RMSE for sucrase (0.15), fructose (0.11),

maltose (0.24) and total sugar (0.19). The optimum setup for glucose, however, was

determined to be a 15:0 architecture. A doser examination of the data indicated that the

difference in error and in R2 for glucose were not significant between the 15:0 and 5:5

models and thus the latter was retained for the remainder of the study. The 5:5

feedforward backpropagation ANN model was then optimized for the learning rate,

which was varied between 0 and 1. For aIl components, the minimum error (Figure

4.36) which correlated to the highest R2 values, was obtained with a learning rate of

0.075. The momentum was then varied within the model optimized sa far. A

momentum value of 0.1 yielded the lowest RMSE and the highest correlation

coefficients for all 5 components (Figure 4.37). The optirnization of the learning

interval did not indicate any improvement in the model at values of 25, 50, 75, 100,

150, 200, 250, and 300. It was elected to retain a 200 learning interval because it

decreased the amount of data crunching to be handled by the training network while

producing prediction with low errors.

The optimized network for the analysis of chocolate syrup was determined to

be a 5:5 feedforward backpropagation model with a cumulative delta learning cule, a

0.075 learning rate with a momentum of 0.1, and a learning interval of200. The RMSE

of tbis optimized model were 0.09 for fructose, 0.12 for sucrose, 0.16 for glucose and

total sugar, and 0.17 for maltose. The computed R2 were 0.998 for sucrose, 0.986 for

glucose, 0.966 for fructose, 0.987 for maltose, and 0.996 for total sugar.

182

• 0.7-Maltose •o.

O.R20.07S= 0.987

O. • o.UJ

\------. UJ

~ O.::n

~ 0.2O.

~0.2 0.1

Fructose

R20.07S= 0.9663

.-----.0.1+--r--"'..........---.---r-...---r----r-..---r--.--""T"""""...........,

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Leaming Rate

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Learning Rate0.35 0.40

0.10 i--r--"',......,.----r-"""'T'"----,--~~T__o....-.,

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8Learning Rate

0.15

0.35Sucrase

R2o.07S= 0.9990.30 •

UJ::n 0.25~

------.- 0.20

Glucose

R2o.07S= 0.986

•

0.20

0.30

0.15 +-...--,.----r-...-~__-y-___r_ ......____...__.-..

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8Leaming Rate

~::n~0.25

•0.7

Total SUgar

0.6 •R2

0.07s= 0.0.996\

0.5tlJCZl

~0.4

0.3

0.2

0.10.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Learning Rate

•Figure 4.36: Error (RMSE) monitoring for chocolate syrup ANN learning rate

optimization

183

• 0.45 Maltose

0.40 R2o.1=0.987

0.350.13

t.IJ t.IJCI) CI) 0.12~

0.30~

0.25 0.11

0.20 ../ 0.10

0.15 0.09

0.0 0.2 0.4 0.6 0.8 0.0Momentwn

Fructose

R20.1= 0.966

0.2 0.4 0.6Momentum

0.8

0.195 0.30Sucrase

0.190 ------------- R2o.1= 0.998

0.185 0.25Glucose

t.I.J 0.180R2

o.1= 0.986t.IJCI)

CI)

~0.20

~ 0.175

• 0.170 0.15

0.165 •0.160 0.10

0.0 0.2 0.4 0.6 0.8 0.0 0.2 0.4 0.6 0.8Momentwn Mornentum

0.40Total Sugar

0.35R2

o.1=0.986

t.IJ 0.30CI)

~ 0.25

0.20

••0.15

0.0 0.2 0.4 0.6 0.8Momentum

•Figure 4.37: Error (RMSE) monitoring for chocolate syrup ANN momentum

optimization

184

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•

The optimized ANN was then used to predict 15 spectra of the original

chocolate syrup formulation, the new formulation, the special dark formulation and

high-moisture original formulation samples. The eITors were very low for all five

components (Table 4.24) with a minimum value of 0.27 for fructose and a maximum of

1.02 computed for total sugar. A one-way ANOVA test conducted on the external

validation indicated that there was no significant difference between the reference

HPLC values and those predicted using the ANN at bath a 0.05 and 0.01 significance

level. The predicted values were also evaluated on the basis of their accuracy with

respect to the HPLC values. Based on the graphs in Figure 4.38, it can be observed that

aH the prediction of sucrose fail with ± 1SD of the reference value. The SO refers to the

error associated with the HPLC method (refer to Table 4.11 for the HPLC errors). For

both glucose and maltose 66% of the predicted value feH within ± 1SD while the

remaining were within a ± 2SD error range. For fructose predictions, ooly 2 values fell

outside the ± 1sn range but remained within the ± 2S0. Finally, total sugar showed the

lowest accuracy with most of its prediction falling in the ± 2S0 error range.

AIthough the network described ahove showed a very good performance with

respect to the quantitative analysis of chocolate syrup production line samples based on

their infrared spectra, a logarithmic function and an arctangent transfer functioo applied

at to the output values were separately tested. In both cases, the application of the

function to the concentration values results in the widening of the range of the output

values. Neither approach however improved the performance of the ANN modeL

185

•Figure 4.38: Accuracy of the chocolate syrup ANN model based on ± ISD error

range

(a) Maltose, (b) Fructose, (c) Glucose, (d) Sucrase, and (e) Total Sugar

187

•

•

•

The development of an ANN model that could accurately predict sugar profile

. of chocolate syrup samples indicates that ANN has the ability to extraet information

even from a data set composed of very similar complex matrices. This may be partly

due to the pattern recognition approach of the ANN learning process. ANN bases its

leaming on trends and similarities in the data rather than differences as is the case with

PLS and most regression methods. Based on the latter premise, the values of RMSE

and R2 obtained for the optimized ANN must he examined with caution as the network

may be simply memorizing patterns and rendering a guess as to the concentrations of

each component. A possible indication of this limitation cao be found in the RMSE

graphs reported earlier. Throughout the optimization steps, the errors as weil as the

correlation coefficients did not vary to a large extent indicating that the information

present in the data may not be sufficient ta rninimize the error of the modeling with

ease.

Based on these observations, it is not justified to generalize that ANN can

accurately predict sugar concentrations. In order ta establish the true predictive ability

of ANN with respect to sugar components, a network based on fully randomized

concentrations of sucrose, glucose and fiuctose in solution will he tested in the

following section.

4.2.4. Application of ANN modelling to aqueous solutions of sugar

mixtures

Samples of aqueous solutions of sucrose, glucose, and fructose were prepared

and the concentration distribution was tested for co-linearity (Figure 4.39). The lack of

trending in any of them indicated that the combination of concentrations in each

188

•Figure 4.39: Co-linearity testing for sugar solutions in ANN training set

189

•

•

•

solution is random and the data randomly covers a wide range of variability. This data

distribution .will test the real potential of ANN for quantitative analysis of sucrose,

glucose and fructose in solution. The performance of the ANN is compared to that of a

PLS regression model developed using the same data set.

Preliminary tests using an ANN with a training set composed of 98 examples,

the duplicate spectra of 49 sugar samples, 150 inputs covering the 900-1500 cm-1

region of the infrared sugar fingerprint region, and having 3 outputs (sucrose

concentration, glucose concentration and fructose concentration), were conducted.

Similarly to the previous section, a feedforward backpropagation network with a

cumulative delta learning role and a logistic transfer function was used. Two hidden

layers (10 processing elements in the tirst and 5 in the second) with a momentum and

learning rate set to 0.1 and a 200 events learning interval were used for the architecture

and parameter settings in the preliminary test. The inputs were scaled between 0 and 1

using a linear function. Training of this network indicated that 4 of the 98 examples

were outliers, 2 of which were duplicates of a sample. The outliers were removed and

the resulting network was tested for sensitivity to the test set chosen. A 10 full cross­

validation was conducted using 10 non-overlapping randomly extracted test sets.

Comparison of the eITors and correlations yielded by aIl 10 networks indicated that the

network was not sensitive to the test set. This observation warranted the use of a

randomlyextracted 10% test set for the remainder of the ANN development.

The ANN was tested using 1 and 2 hidden layers. For the one hidden layer

architecture, 5, 10, 15, 20, 30, and 40 PE were evaluated. The 2 layer models tested had

the following architectural setup: 5:5, 8:4, 8:8, 10:5, 10:10, 15:5, 15:10, and 15:15.

190

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•

Figure 4.40 shows the errors obtained for each architecture and each component. From

the RMSE and the R2 values, the optimum number of layer and PE yielded a 8:4

architecture. The optimization of the learning rate (Figure 4.41) and the momentum

(Figure 4.42) showed that a value of 0.1 for both minimized the modeling error for all

three components, sucrose, glucose, and fructose.

Two data pre-treatment approaches were employed and their effect on the

calibration performance was evaluated by comparison to the one optimized thus far.

First, a baseline correction was applied to the spectral data. No other parameter was

changed. The resulting ANN did not perform better than the one with the original

untreated data with eITors on the input vectors of 1.50, 0.85, 0.62 compared to 1.04,

0.68, and 0.62 for sucrose, glucose, and fructose, respectively.

The non-baseline cOITected spectra were analyzed by principal component

analysis (PCA) to reduce the number of input variables. This type of data reduction has

been shown to improve the predictive ability of the ANN in other cases (Ji and Yan,

1999). Principal component analysis was used as a data reduction step to decrease the

number of inputs. The fust 10 principal components of each of the 94 spectra were

calculated. Plotting the eigenvalues against each component showed that 4 PC were

sufficient to account for more that 99% of the spectral variability (Figure 4.43). Based

on the proportion and the cumulative proportion associated with each eigenvalue

(Table 4.25), the frrst 4 PC were used as the input vectors of the ANN being optimized.

The RMSE for both the training and the test sets and for all components was at least

twice as high as the values obtained for the ANN with the identical parameters but

191

• 1.8 Sucrose1.7 R28 ;4 = 0.979

1.6

1.5 •~ V\CI)

~lA

1.3

1.2

1.1

1.0

0 2 4 6 8 10 12 14 16ANN Architecture

1.6

Glucose1.4 • R2g;4 = 0.992

~CI)

1.2~ •

1.0

I~V\;0.8• --.0.6

0 2 4 6 8 10 12 14 16ANN Architecture

1.6

FructoselA • R28;4 = 0.990

~CI) 1.2

~ •

J~V\;·1.0

0.8

0.60 2 4 6 8 10 12 14 16

ANN Architecture

•Figure 4.40: Error (RMSE) monitoring for sugar mixture solution ANN

architecture optimization

192

• 1.8 Sucrase1.7 R2o.1 = 0.9791.6

t.tJ1.5

CZl1.4

~ 1.3

1.2

1.1

1.0

0.0 0.2 0.4 0.6 0.8Leaming Rate

1.6

1.4 GlucoseR2o.1 = 0.992

t.tJ 1.2CI)

~1.0

0.8

• 0.60.0 0.2 0.4 0.6 0.8

Leaming Rate

I.r •ur Fructose

0.9R20. 1 = 0.990

t.tJCI)

~ 0$

0.7

0.6 I~--------- •

0.5

0.0 0.2 0.4 0.6 0.8Leaming Rate

•Figure 4.41: Error (RMSE) monitoring for sugar nlixture solution ANN learning

rate optimization

193

• Sucrase

1.5 R201 = 0.979. ---------

1.4 .--------UJ 1.3CJ)

~1.2

l.1

1.00.0 0.2 0.4 0.6 0.8

Momentum1.3

Glucose1.2 R2

o.1 = 0.9921.1

UJ1.0CJ)

::ECl::: 0.9

/0.8• 0.7

0.60.0 0.2 0.4 0.6 0.8

Momentum0.85

0.80 Fructose0.75 R2

0. 1 = 0.990

t.LJ 0.70CI')

~ 0.65

0.60 •

\0.55

0.50.0 0.2 0.4 0.6 0.8

Momentum

•Figure 4.42: Error (RMSE) monitoring for sugar mixture ANN momentum

optimization

194

•

.-­--.-.--.--.--.--.--.1084 6

PCA Score2

160

140 •120

100(1)

:::s 80~>~ 60(1)

on.-u.:J 40

• 20

0

-200

Figure 4.43: Eigenvalue plot from PCA on th\~ spectra of the ANN sugarsolution training set

• 195

•

Table 4.25: Table of eigenvalues, proportion and cumulative proportions from

PCA on the spectra of the ANN sugar solution training set

PCA Score Eigenvalue Proportion Cumulative Proportion

1 145.02 0.924 0.924

• 2 7.31 0.047 0.970

3 2.97 0.019 0.989

4 1.29 0.008 0.997

5 0.11 0.001 0.998

6 0.10 0.001 0.999

7 0.03 0.001 1.000

8 0.02 0.000 1.000

9 0.01 0.000 1.000

10 0.005 0.000 1.000

• 196

•

•

•

having 150 inputs. The correlation coefficients of the training and testing sets were

below 0.80 for all components, a further indication that ANN was having difficulty

modeling the sugars in the system when the principal components were used as

inputs. Based on these results, it was elected to use the input vectors without

subjecting them to a data pre-treatment step.

The optimized ANN model was a feedforward backpropagation with an 8:4

architecture, a cumulative delta learning mIe, with a learning rate and a momentum

of 0.1 with a 200 events learning interval. The ANN uses the original spectral data

between 1040 and 1500 cm-1. An external validation set composed of 40 examples of

aqueous sugar solutions was predicted using the ANN modeL The RMSE and R2 on

the predictions were 2.48 and 0.933 for sucrase, 1.52 and 0.967 for glucose, and 1.35

and 0.977 for fructose. The predictions of the external validation set were

statistically assessed for deviation from the gravimetric values. A one-way ANGVA

did not deteet a significant difference on the Mean and the variance of both the

prediction and the actual values at significance levels of 0.05 and 0.01.

The PLS calibration developed based on the same training as the ANN model

yielded calibration errors similar to those of the optimized network (Table 4.26) at

values of 1.02 for sucrose, 0.53 for glucose and 0.25 for fructose. The corresponding

correlation coefficients based on the actual vs. predicted plots (Table 4.26, Figure

4.44) were 0.977, 0.994 and 0.998 for sucrose, glucose and fructose respectively.

These R2 values are higher than those obtained for the ANN model (Table 4.26). The

PRESS showed that the error of the system dropped significantly for aIl three

components. Five factors were needed to minimize the modeling error of glucose and

197

• • •

Table 4.26: Table of comparison between ANN and PLS performance for the quantitative analysis of sugar in solution

PLS Calibration Model ANN Model

Calibration Calibration

R1 RMSE

Validation

R1

Validation

RMSE

Calibration Calibration

R2 RMSE

Validation

R2

Validation

RMSE

Sucrose

Glucose

Fructose

0.977

0.994

0.998

1.02

0.53

0.25

0.959

0.991

0.988

1.90

0.89

0.59

0.979

0.992

0.990

1.04

0.66

0.54

0.934

0.967

0.976

2.48

1.53

1.35

198

• 30Sucrose

~ 2500.p

~ 20cc:.l_

g~8~ 15oo~0-t) 10;.ae~ 5

05 10 15 20 25 30

Aetual Concentration30 (OAJw/w)

c 25 Glucose0.~

20~0_g ~0- 15u~

00*

• 0-t) 10:.a~c.. 5

05 10 15 20 25 30

25 Actual Concentration(%w/w)

= 20 Fructose00=

~~

8- 15= :::0-u~

'"0* 10~-....u

:.a~ 5c..

00 5 10 15 20

Actual Concent:ration(%w/w)

Figure 4.44: Actual vs Predicted for PLS model of sugars in solution

• 199

•

•

•

fructose and 6 were needed for sucrose (Figure 4.45). The RMSE calculated based

on the predictions of the external validation set yielded values of 1.90 for sucrose,

0.89 for glucose and 0.59 for fructose. These corresponded to correlation coefficients

of 0.959, 0.991, and 0.988, respectively. Comparison between the errors and

correlation coefficients of the PLS calibration models and the optimized ANN

indicated that the modeling error for both approaches are comparable. However, the

prediction of the external validation set suggest that ANN has a lower predictive

power with an external validation RMSE of2.48 compared to 1.90 for sucrose, 1.53

compared to 0.89 for glucose and1.35 compared to 0.59 for fructose of the PLS

calibration. A critical look must he carried on these values and the meaning of this

difference in prediction must be analyzed with caution. Legitimately, one can

wonder if the values predicted by the ANN model are indeed based on having

extracted patterns present within the training set, or is the model predicting the

values by associating the profile to a "previously seen" profile?

A doser comparison of the ANN predictions on the external validation set

shows that the samples with similar concentrations in the validation and the training

set were not predicted with values close to one another. If ANN was indeed using a

pattern recognition approach to quantify the components, the vaIues of the two

samples should be very close. Similar results were obtained for the three validation

samples, which had homologues in the training or testing set. On another level,

samples that were not close in profile to any particular sample in the training set did

not show any bias, indicating that the absence of very close similarity between the

validation samples and the training samples is not at the ongin of the lower ANN

performance.

200

• 4000

Sucrase

3000

CI)

~ 2000 •c.. \1000

---...---0 ._--.-.-.-.--

0 2 4 6 8 10Factor

4000~

Glucose3000

CI)CI)

~ 2000~

1000

• 0 -----------------.0 2 4 6 8 10

2500 Factor

2000 Fructose

CI) 1500CI)

~1000~

500

0~----tl__________

0 2 4 6 8 10Factor

•Figure 4.45: PRESS for PLS model of sugar solutions

201

•

•

•

Using a simple and linear system such as sugar solutions may not he a prime

example for .developing an ANN modeI. However, the straightforward relationship in

the system serves as a hasis to evaluate the true ability of the ANN modeI. Many

concems have been voiced as to the ability of ANN to give precise values (Caudill,

1988a) as outputs due to the nature of the delta mIe and the backpropagation

network. The results reported from the application of ANN to the quantitative

anaIysis of sugars in aqueous solutions indicate that ANN has the potential ability of

predicting with acceptable accuracy the individual sugar components. Therefore

depending on the error level accepted by the analyst, an ANN model for quantitative

analysis may or may not faIl within acceptance limits. A possible approach to

improving quantitative analysis would he increasing the number of input vectors.

The relationship between absorbance and increase in sugar concentration in aqueous

solutions being a linear one, this system does not benefit from the advantages offered

by ANN and that have been recognized for non-linear systems (Fang et al., 1998; Ji

and Yan, 1999) but demonstrate the true potential of an ANN model.

4.3. INVESTIGATION OF THE SUGAR-SUAGR AND SUGAR-WATER

INTERACTIONS IN CARNOHYDRATE SOLUTIONS

Carbohydrates are characterized, among other things, by their ability to forro

an extensive hydrogen bonding network owing to the hydroxy! groups present on the

periphery oftheir carbon ring (Figure 2.1). Water is also prone to hydrogen bonding

with other water molecules as well as with solvated molecules (Max et ai., 1998a).

202

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•

•

Sugar solutions can therefore be described as a hydrogen-bonded matri~ where

water-water,. water-sugar and sugar-sugar interactions are at the center of the

chemical behavior of the deferent species.

Infrared spectroscopy has been extensively employed for the study of

structural changes at the molecular level. It is therefore possible to investigate

changes that may be occurring in single sugar solutions as the concentration of the

carbohydrate increases. In addition, infrared spectroscopy may be employed to probe

the potential interactions between two or three dissolved sugars with respect to

changes in concentration of at least one component. Although extensive band

assignment in the fmgerprint region (800-1200 cm-1) of common sugars (sucrose,

glucose and fiuctose) is found in the literature, it is still acknowledged that much

remains to be done in this field due to the complex nature of this chemical species

even in its simplest form: monosaccharides (Mathlouthi et al., 1996b). Due to the co­

existence of different carbohydrate structures (cr enantiomer, J3 enantiomer, pyranose

ring, furanose ring and open chain form), it must be understood that any

interpretation of an infrared spectrum of sugars assumes that only one chemical

species exists.

Based on these preliminary remarks, this section of the reported work is an

investigation into the potential of 2D IR spectroscopy correlation to uncover the

behavior of sugars in solution with an emphasis on the potential hydrogen bonding

effects on the infrared spectra of the sugars.

203

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•

4.3.. 1.. Effect of increasing the concentration of a single sugar in

solu.tion on its infrared spectrum

Figure 4.46 shows the plots of the sucrose, glucose, fructose, and maltose

c,?ncentration (5% w/w to 65% w/w) versus the correeted area under the peak (900­

1200 cm- I) of single sugar solution spectra, indicates a polynomial relationship,

although it has been shown that the relationship between sugar concentration and

infrared absorbance is linear. The plot obtained for sucrose shows that the area under

the peak decreases after 60% w/w. These observations may indicate that sucrose

micro-particles are not completely dissolved in solution although no precipitate

could be observed.

The deviation from linearity over the studied concentration range was

investigated with respect to the increase in refractive index (RI) of sugar solutions as

the concentration is increased. The change in RI May be an important factor as the

samples were scanned using an ATR sampling accessory in which the depth of

penetration of the beam depends on the difference in refractive index between the

crystal and the sample (Urban, 1996). The RI for a single sugar solution increases

forro 1.35 at 5% w/w to 1.45 at 80% w/w, regardless of the sugar under study

(Snyder and Hattenburg, 1963). The plot of the concentration vs. the RI and the

corrected area vs the RI were aIso fit using a polynomial regression with sirnilar

regression parameters indicating that the relationship of all three variables, namely

concentration, absorbance and refractive index is affected by an extemal factor that

has not been taken into account.

The depth of penetration of the IR beam is not only dependent on the

difference of RI but on the wavelength being measured. It was caIculated that a 40%

204

•100 140

C':SSucrase ~ 120 Glucose

e 80 eu

< ~ 100"0 60 "0eu euÜ Ü 80ê 40

ê0 060

U u

20

]40

200 1 1 • • 1 i

0 10 20 30 40 50 60 0 10 20 30 40 50 60Concentration Concentration

(%w/w) (%w/w)

120Fructose 100 Maltose

C':S ~

e 100 ~< « 80"0

80"0

eu eu..... Ü• <.J 60ê 60 ê0 0u U 40

40

20 20

0 10 20 30 40 50 60 0 10 20 30 40 50Concentration Concentration

(%w/w) (%w/w)

Figure 4.46: Plots of the concentration (5% to 60% w/w) versus the areabetween 900 and 1500 cm-1 for sucrase, glucose, fructose and maltosesolutions

• 205

•

•

•

decrease in the depth of penetration was oceurring between 900 and 1500 cm-l,

which represents the complete sugar-absorbing region. On the other hand the

decrease of the depth of penetration between a 5% and a 65% w/w sugar solution is

approximately 6%. The area was corrected for each concentration based on the

change of the depth of penetration relative to the 5% w/w depth of penetration. The

new area values were plotted against the concentration. Once again a linear fit could

not be used to adequately model the relationship. Based on the results obtained, it

was ruled out that the polynomial relationship existing between the sugar

concentration in solution and the IR. absorbance was due to the sampling accessory

used. This conclusion was confrrmed by recording the spectrum of sugar solutions

with concentrations ranging frOID 5% ta 65% w/w using a transmission ceU and

plotting the concentration of the sugar against the corrected area under the peak. The

plots obtained were similar to those presented in Figure 4.46.

A doser examination of the spectra of the different concentrations of sucrose,

glucose and fructose solutions demonstrated band shifts. For glucose for instance, the

band at 1033 cm-l shifts from 1033.9 cm-l to 1030.8 cm-l as the concentration

increases frOID 5 to 60% w/w (Figure 4.47, Table 4.27). This shift occurs to different

extents and directions for ail samples at 990.7 cm-\ 1080 cm-l, 1106 cm-l, and

1150.9 cm-l (Figure 4.47, Table 4.27). Similar band shifts are also observed for

sucrase, fructose and maltose (Appendix 8) at their characteristic bands.

The band intensity at 1639 cm-l decreases and a band shift to higher

wavenumbers (Figure 4.47, Table 4.27) is aIso observed. The changes in band

position can be an indication of the interaction between the sugar molecules and the

206

•

5% 60%

~ ~

0.90

Cl.) 0.86uc::~

.D.... 0.820v.I

.D

• «0.78

0.74

0.70

10S0 1040 1030 1020

Wavenumbers (cm-1)

Figure 4.47: Band shift in the spectra of samples with increasing glucoseconcentration at 1033 cm-1

• 207

e e e

Table 4.27: Observed concentration dependent band shift in glucose solution spectra

Concentration Band at

990 cm-t

Band at

1033 cm- l

Band at

1050 cm"'

Band at

1106 cm-t

Band at

1108 cm- l

Band at

1639 cm- l

5% 990.76 1033.77 1150.91 1106.02 1080.05 1639.36

10% 991.97 1033.90 1150.50 1106.12 1079.92 1639.26

15% 992.41 1033.43 1150.42 1105.47 1079.20 1640.41

20% 992.43 1033.82 1150.81 1105.73 1079.46 1639.88

25% 992.16 1034.07 1150.68 1105.95 1079.59 1639.74

30% 992.30 1033.07 1150.15 1105.52 1079.14 1640.26

350/0 992.30 1032.76 1149.85 1105.21 1078.67 1640.12

40% 992.30 1032.47 1149.64 1104.77 1078.48 1640.64

45% 992.32 1031.94 1149.50 1104.51 1078.14 1640.65

50% 992.28 1031.71 1149.17 1104.30 1077.89 1641.11

55% 992.15 1031.20 1148.90 1104.05 1077.55 1641.04

60% 991.17 1030.75 1148.42 1103.84 1077.09 1641.64

The concentrations are expressed in %weight by weight.

208

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•

water Molecules. It is also possible that a simultaneous change in the interactions

between the sugar Molecules May be taking place.

As the concentration of water decreases, a lesser degree of hydrogen bonding

between water Molecules and between sugar and water Molecules occurs and thus

the sugar-sugar interaction predominates. As the water hydrogen bonds to the sugar,

a reorientation of the molecules occurs, preventing them from vibrating to their full

extent. The result ofthis network may be the shift of the water band. Furthermore, as

the concentration of sugar increases, the water Molecules available ta hydrate the

sugars are not sufficient to accommodate the rising number of sugar Molecule. The

"fluid" hydrogen bonding network which is characteristic of the dilute solutions, is

therefore replaced by a more rigid network where sugar-sugar interaction takes

place. Aiso as the number of water Molecules decreases in the solution, the water­

water interaction is decreasing in favor of the sugar-water interactions.

4.3.2. Potential application of 2D IR correlation spectroscopy for the

study of the change in spectral features of single sugar

solutions with increasing sugar concentration

2D correlation spectroscopy can correlate changes in an IR spectrum, such as

intensity variation, band shift and changing in directional absorption, to the

perturbation applied to a system. In our study, 2D IR correlation spectroscopy will be

used to investigate the cross-correlation of the band behavior between different

regions as a function of increasing sugar concentration.

The synchronous function, aIso known as coincidaI function, gives

information regarding the coupling of different functional groups. Mathematical

209

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•

caiculations of the synchronous function produce a set of data translating the degree

of coherence between two signaIs measured simuitaneousIy. The graphicai

representation can be three-dimensional (3D) or collapsed into a 20 graph known as

a synchronous map. Two types of peaks can be isolated from a synchronous map:

autopeaks, which are found on the diagonal of the graph, and cross-peaks. Autopeaks

forrn the power spectrum, which indicates what groups present in the system will be

susceptible to the extemai perturbation. Cross-peaks provide information pertaining

ta the different functional groups and whether they reorient simuitaneously. A

positive peak indicates that the groups reorient simultaneously and in the same

direction, while a negative peak indicates a reorientation in opposite directions.

Groups that do not reorient simultaneously do not produce peaks in 20 IR

correlation spectroscopy. The analysis of the information provided by a 20

synchronous map allows to establish a network of inter- and intramolecular

interactions within the system.

The asynchronous function, aIso known as quadrature function, measures the

relative speed between the groups that reorient simultaneously. The resulting

calculation using the asynchronous function yields a set of data that can be graphed

iota a 3D or a 2D image termed asynchronous map. The sign of the peaks produced

gives an indication of the relative sequence of events thus providing information as

to the mechanisms of the change undergone by the system in the presence of the

perturbation. The asynchronous map produced from data of a kinetic study carries a

great wealth of information to understand the molecular mechanism behind the

kinetics (Ozaki et al., 1997).

210

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•

4.3.2.1. 2D IR synchronous mapping of the effect of sugar concentration on

the spectral profile

When 2D IR correlation spectroscopy is applied to spectra of sugar solutions

with concentrations ranging between 5% and 60% w/w. the synchronous map

(Figure 4.48) shows as expected only positive cross-peaks in the region of900-1500

cm- l• indicating that the absorbance in the sugar region increased with respect to all

the other wavenumbers as the concentration increases occurred. Another positive

cross-peak correlation could he seen at 1000 cm-l vs. 2928 cm-1 and 2887 cm-l. This

correlation is confirmed by the absorbance spectra and can he explained by the fact

that as the concentration increases the number of CH bonds present in solution

increase and thus absorb more. Finally, a negative correlation between the sugar

bands and the water bands at both the 1700 and 3000 cm- I implied that the water and

the sugar regions were moving in opposite direction from each other as expected

since the absorbance of the water decreases as the sugar concentration increases.

4.3.2.2. 2D IR asynchronous mapping of the effect of sugar concentration on

the spectral profile

1. Analysis ofthe asynchronous map offructose in solution

Examination of the asynchronous map of the fructose solutions uncovered an

intricate network of cross correlation within the sugar region between 30 different

peaks. all of which cao be paralleled to peaks present in the second derivative of the

absorbance spectrum of fructose solution. The interpretation of the asynchronous

211

• • •SYllcllrolloliS 2D Correlation Map

Dashed /ine: negative peak

Plain line: positive peak

81!:IC1

t i

~

0-s(;

..~'~.

~

//

/~

/~

//

/~

//~

/~

//

//

//

//

/~

//

//

//

///

1000

4500

4000

2000

,-......1 250080'-'MCl)

.D8 3000::s=Cl)

~~ 3500

3500 3000 2500 2000 1500 1000

Wavenumber (cm-1)

Figure 4.48: 2D mcorrelation synchronous map (Sucrose)

212

•

•

•

map requires referral to the synchronous 2D map and the original absorbance

spectra. The. basic interpretation rules can be stated as fol1ows:

1. If a peak is positive in the synchronous map and the same peak is positive

in the asynchronous map, then a change in the band at the wavenumber

on the x-axis occurs before that on the y-axis.

2. If a peak is positive in the synchronous map and the same peak is

negative in the asynchronous map, then a change in the band at the

wavenumber on the x-axis occurs after that on the y-axis.

3. If the peak in the synchronous map is negative then rules 1 and 2 are

inverted.

4. The absence of bands in the asynchronous spectrum, but present in the

synchronous spectrum indicates that the events are occurring

simultaneously.

The sign of the different peaks seen in the 2D asynchronous map (Figure 4.49)

calculated for the fructose solutions can be found in Appendix 8. Based on the

interpretation cules and the table in Appendix 8, the following observations can be

made from the asynchronous map of fructose:

1. The band at 1086 cm- l increases before the bands at 1049, 1030, 1014,

975.8 and 964.2 cm- I.

2. The band at 1077 cm- l increases before the bands at 1049, 1030, 1014,

975.8 and 964.2 cm- I but after 1088 and 1068 cm- I.

3. The band at 1065 cm- l increases before the bands at 1146, 1099, 1076,

1046, 1030, 1014, 975.8, and 964.2 cm- l.

4. The band at 1049 cm-l increases before the band at 1026 cm-I but after the

bands at 1458, 1419, 1346, 1265, 1254, 1188, 1161, 1103, 1084, and

1065 cm- l.

213

e e e

Asyncllronous 2D Correlation Map

Plain line: positive peak

Dashed line: negative peak

':~

l}

~

{)

()

~oc=:- ~~

,[J .~ "."

~ .............

•) ..~".'-~.lP"?c:=::......... .r..~J:"J '1f

......... • ,~t'1J/' l )..... .1

..........tI'••

~.........'".,

tI'....

..............

........,......'........

....'...'..',,"............

..............,

......."/ .....

....

1100

lIDO

1200~-1

8(,) 1300

'-'"...(1)

..c 1400S:sc(1)

~ 1500~

1600

1700

1800

1800 ]700 1600 1500 1400 1300 1200 1100 1000

Wavenumber (cm-1)

Figure 4.49: 2D IR correlation asynchronous map for fructose solution spectra the concentration increases

214

•

•

•

5. The band at 1035 cm-l increases after the bands at 1161, 1103, 1084, and

1065 cm-l.

6. The band at 1007 cm-l increases after the bands at 1103, 1084, and 1065

cm-l.

7. The band at 980cm-1 increases before the peak at 1053 cm-1.

8. The band at 976 cm-l increases before the band at 1049 cm-l, but after the

ones at 1084 and 1065 cm-l.

9. The band at 965 cm-1 increases after the peaks at 1106, 1084, and 1065

cm- l .

10. The band at 930 cm- l increases before the band at 1053 cm-Jo

A tentative sequence of event regarding the band increase in a fiuctose

solution as the concentration increases has been rationalized based on the 10

observations stated. The sign ">" signifies that the listed bands to the left increase

before the bands to the right while the bands separated by a cornmon may he

increasing simultaneously.

1. 1086, 1068, and 1065 cm-J > 1077, 1146, 1099, and 977 cm-1>1049,

1030, 1014, 975.8, and 964.2 cm-1> 1026 cm-l

2. 1458, 1419, 1346, 1265, 1254, 1188, 1161, 1103, and 1084 cm-1 > 1035,

1007, 965 cm-1

Although sorne of the bands in the fructose spectrum are assigned to specifie

functional groups on the molecule (Appendix 1), the information is still limited. It is

therefore difficult to speculate as to the meaning and impact of this sequence of

events. However, it cao be proposed that the difference in timing of the increase in

215

•

•

•

the absorbance may indicate: (1) that band shifts are occurring throughout the

process at increasing sugar concentration, and therefore the 2D map is a compounded

profile of ail the band position changes in the solution, (2) that an interaction

between the different molecules may be promoting the vibration of one functional

group over the other peaks at a given concentration.

2. Analysis of2D IR asynchronous maps ofglucose in solution

Using the same approach as described above, the conclusions of the 2D IR

correlation study for glucose in solution are the following (Figure 4.50, Appendix 9).

1. The band at 991 increases after the bands at 1435, 1365, 1153, 1111, 1080,

1038, and 991.2 cm-l.

2. The peak at 1015 increases before the bands at 1007 and 983.5 cm-l, and after

the bands at 1439, 1369, 1157, III l, 1080, 1038, and 944.9 cm-l .

3. The peak at 1019 cm-l increases after the bands at 1431, 1365, 1257, 1153,

1111, 1080, 1038, and 991.2 cm-l.

4. The band at 1049 cm-l increases before the bands at 1007 and 983.5 cm-t, and

after the bands at 1435, 1365, 1157, 1111, 1080, and 1038 cm-l.

5. The band at 1060 cm-l increases before the bands at 1007 and 983.5 cm-l, and

after the bands at 1435, 1365, 1157, 1111, 1080, 1061, and 1038 cm-l.

6. The band at 1068 cm-l increases before the bands at 1007 and 983.5 cm-l, and

after the bands at 1435, 1365, 1157, 1111, 1080, 1061, and 1038 cm-l.

7. The band at 1096 cm-l increases before the band at 1007 cm-l, and after the

band at 1365, 1153,11 11,1080,1061, and 1038 cm-l.

8. The band at 1145 cm- l increases after the peaks at 1111, 1080, and 1038 cm-l .

216

e e eAsyncllronolls 2D Correlation Map

1000 . ,::...,.......'')1:' ("..'.,....... ...... ~

1100

r-...... 12001

8<)'-"M 1300Q)

.CJ

1400 ~....-'.... - 0-S 1:)

~ "..'Qs:l .......

al .'.'~

.'"

~ 1500

1600 ~,.-

....-'Dashed line: negative peak/ .....

",,'

\J Plain fine: positive peak1 l

1700

18001 1 1

1800 1700 1600 1500 1400 1300 1200 1100 1000

Wavenumber (cm-)

Figure 4.50: 2D mcorrelation asynchronous map for spectra of glucose solutions as a function of increasing concentration

217

•

•

•

9. The tentative sequence of events can be summarized as: 1435, 1365,

1257,1153, 1111, 1080, 1035, and 991.2 cm- l > 1145 cm- l > 1096 cm- l >

944.9, 1015, 1049, 1060, and 1068 cm- l > 1019 cm- l > 991, 1007, and 983.5

cm- l.

The examination of the glucose sequence of events suggests that the

information present in the maps combines the band shift to the intensity information

of the deteeted peaks. An example of the band shift effect can be seen in the 1019

cm- l band, which occurs before the 1015 cm- l peak. This is confirmed by examining

the 2nd derivative of the absorbance spectra of glucose. It is aIso interesting to

observe that the band at 1019 cm- l occurs mer the bands at 1365 and 1153 cm- l

while the band at 1015 cm- l occurs before the bands at 1365 and 1157 cm- l. These

observations indicate that the two latter bands may have resulted from a band shift of

the two former ones. Once again, it is difficult to assess the significance of this

information on the molecular level due to the limited understanding we have of sugar

solutions.

3. Analysis of2D IR asynchronous maps ofsucrose solutions

Based on the synchronous (Figure 4.48) and the asynchronous (Figure 4.51)

maps for sucrose and the table of cross-peak signs (Appendix la), the following

observations and sequence ofevents were reached for sucrose.

1. The band at 929 cm- l increases before the peak at 1061 cm- l but after the

one at 983.5 cm- l.

2. The band at 987.85 cm- l increases before the 983.5 cm- l and after 1454,

1435, 1161, 1142, 1119, 1061, 1081, and 1003 cm- l.

218

• • •Asyncltronolls 2D Correlation Map

Dashed line: negative peak

Plain line: positive peak

~

(~J

aQ

() 0'.'

~ ~ -u~ ...:::a-

c:>

1800 -1-- , , i , , , , , '

1000

1100

,-....1200....

1

S0

"-" 1300-(1)

.D8 1400::s~(1)

~ 1500~

1600

1700

1800 1700 1600 1500 1400 1300 1200 1100 1000

Wavenumber (cm-1)

Figure 4.51: 2D IR correlation asynchronous map for sucrose spectra as a function of concentration

219

•

•

•

3. The band at 987.4 cm-l increases before the bands at 987.4 and 921.8 cm­

1 but afterthe bands at 1165,1142,1080,1061,1041, and 1014cm-l.

4. The band at 1059 cm- l increases before the peaks at 1126, 1099, 1076,

104 1, 987.4, and 925.4 cm- I.

5. Sequence of events: 1451, 1435, 1165, 1161, 1142, 1126, 1119, 1099,

1080/1,1076, 1061, 1041, 1014, and 1003 cm- l > 1038 cm- l > 987.8,929,

925.4, and 921.8 cm- l > 983.5 cm- l.

Understanding the chemical effect of this sequence is difficult because of the

limited specifie band assignment (no assignment have been made for the frequencies

in group 2, 3 and 4 of the sequence) and due ta the inability to resolve the bands

present in the fust group ofbands in the sequence ofevents.

4, Analysis of2D IR asynchronous Inaps ofmaltose solutions

Finally, the application of2D IR spectroscopy (Figure 4.52, Appendix Il) to

the maltose solutions with increasing sugar concentration led to the following

sequence of events.

1. The band at 917 cm- l increases after the bands at 1149, 1115, 10557, and

1041 cm- I.

2. The band at 1017 cm-l increases before the bands at 1007 and 987.4 cm-l

but after the bands at 1435, 1365, 1261, 1153, 1115, and 1080 cm-l.

3. The band at 1042 cm-l increases before the bands at 1146, 1011 and 987.4

cm-l but after the bands at 1115 and 1080 cm,l.

4. The band at 1051 cm-l increases before the bands at 1146, 1099, 1049,

1018, and 987.4 cm- I.

220

e e e

Async/rrollolls 2D Correlation Map

Dashed fine: negative peakPfainline: positive peak

1»

~~ 0

-~~

0 0

c::::::::::> <==--

A ~

c::::::>

1800 1r 1 1 1 1 1 1 1 11800 1700 1600 1500 1400 1300 1200 1100 1000

Wavenumber (cm~l)

1000

1100

~ 1200-1e(,) 1300~

~d)

.D 1400S::s1::d) 1500~~ 1600

1700

Figure 4.52: 2D masynchronous map from spectra of maltose solutions as a function of increasing concentration

221

•

•

•

5. The band at 1077 cm- l increases before the bands at 1011 and 987.4 cm- l

but after the bands at 1115, 1080 and 1041 cm- l.

6. The band at 1100 cm- l increases before the bands at 1011 and 987.4 cm- l

but after the band at 1080 cm- l.

7. The sequence ofevents for maltose can be described as follows: 1051 cm­

1 > 1115 and 1080 cm-l > 1149, 1076, 1057, 1041, 1435, 1365, 1261, and

1153 cm- l > 1077 cm- l > 1017, 1146, and 1011 cm- l > 1007.4, 987.4, and

917 cm- l.

4.3.2.3. General interpretation of data and hypothesis

The study of solutions with different sugar concentration solutions by 2D IR.

correlation spectroscopy has demonstrated the complexity of the commonly used

dilution process. In the case of the sugar solutions, changes related to an increase in

band intensity as weIl as a shift of the band position is strongly indicative of

interactions occurring within the matrix. Due to the nature of the chemical species in

the solution, one cao safely speculate that these interactions are essentially of the

hydrogen bonding type, which can be very strong in cases where the network is

closely packed. This possibility must be considered in the current study as the

concentration reaches 60% w/w, a value beyond which glucose will precipitate at

foom temperature and maltose will not go into solution at room temperature. It is

possible to speculate that the increase in sugar concentration which correlated to a

decrease in free water, will cause reorientation of the chemical structures under the

growing force of the hydrogen bonds, that are due to water in diluted solutions, but

can be attributed to the sugar-sugar interaction at higher concentrations.Furthermore,

it can be suggested that the cross-peaks seen in the asynchronous graph cao be

222

•

•

•

associated to specifie concentration ranges. If so, 2D IR correlation spectroscopy

may have provided the tool to understanding the good performance of the chocolate

syrup PLS calibration models developed in section 4.1. Based on the hypothesis that

each sugar concentration range is characterized by a series of discrete peaks. varying

the concentration of a chocolate syrup sample, wbere 55% w/w of the sample is

sugar and 33% is water, causes a change in the water-sugar interaction. These

changes may be leading to concentration-dependent variations in the vibration mode

of the sugar as there are less tightly heId ioto position by an extensive sugar-sugar

hydrogen bonded network. If so, tben PLS may he basing its modeling on a

concentration dependent band shift.

Fina1ly, due to a limited numher of specifie band assignments for each sugar

spectrum, it is difficult to do an in-depth interpretation of the data at the molecular

level. However. 2D IR correlation spectroscopy bas the potential of being a powerful

tool in understanding the dynamics of complex systems provided specifie band

assignment is available.

223

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•

Chapter 5: Conclusion and contribution ta knowledge

An investigation was undertaken to study the potential of a mid-infrared

chalcogenide fiber optic probe (FOP) as a sampling accessory for the analysis of

sucrase, glucose, fructose, maltose, total sugar and water content in solution and in

chocolate syrup using FTIR spectroscopy.

The use of the FOP accessory was standardized through a series of

experiments addressing the stabiIity of the fiber optic cables, the signal to noise, and

the cut-offpoint, in order to obtain repeatable data. The performance of the FOP was

then compared to that of the well-established germanium horizontal ATR (Ge­

HATR) sampling accessory using sugar solution mixture models. Overa1l, the data

predicted from the fiber optic probe based calibration models indicated that the FOP­

based calibration models are adequate for quantitative analysis of sugars in solutions.

Having established the capabilities of the FOP accessory, this new sampling method

was employed for the quantitative analysis of chocolate syrup.

An evaluation of the chocolate SYfUP spectra recorded using the FOP

indicated that there was not sufficient extractable information to build PLS

calibration models for the quantitative analysis of sucrase, glucose, fructose,

maltose, total sugar and water content from production line samples. Based on a

comparison of the pure component and statistical spectra from chocolate syrup and

aqueous sugar solutions, it was deterrnined that this limitation was attributable to the

tightness of the concentration ranges of the different sugars, and the restricted

variability in the ratios of the sugars to one another. The conventional approaches for

preparing training standard samples for the development of a quantitative FTIR

224

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•

•

based analytical methodology requires that the analyzed components be sufficiently

varied by standard addition, for instance. Calibration models based on chocolate

samples spiked with sugar solutions resulted in high errors for ail calibration models.

This was attributed to the complexity of the chocolate syrup matrix where underlying

absorption may be contributing in the sugar region. Furthermore, the disruption of

the chocolate syrup gel matrix during dilution and addition of sugar solution may

have altered the chocolate syrup matrix sufficiently to deteriorate the calibration

model. AIso, the combination of dilution and spiking of the chocolate syrup samples

may be uncovering or creating a matrix effect that may be interfering in achieving a

stable calibration model. To overcome these difficulties an alternative experimental

design, where the concentration of each sugar was associated to the total sugar

absorbance was employed. This approach was possible because of the tightness of

the concentration range and the ratio of the sugars to one another in the original

chocolate syrup samples. The PLS calibration models based on the unconventional

experimental design yielded high correlation coefficients and low error on both

calibration and extemal validation sets. Ho\vever, these models performed poorly

when predicting spectra of sugar spiked chocolate syrup samples or chocolate syrup

samples of a different formulation. Despite this limitation, the results obtained from

the calibration models developed for individual sugars, total sugar and water content

of chocolate syrup demonstrated that the fiber optic probe can be used as a sampling

accessory for the quantitative analysis of chocolate.

Accuracy, repeatability, long-term stability and ruggedness were tested and

the results demonstrated that the calibration models were robust and had a better

repeatability than the reference HPLC method.

225

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Having developed a formulation specifie PLS regression model, a

preclassification approach was required to ensure that the model is used with the

appropriate sample type. ANN was used as a classification approach. Although a

SOM-NN could not group the different formulation in an unsupervised fashion, a

supervised PNN model yielded excellent classification results as weIl as a build-in

ability to rejected outliers. The ANN modeling approach was also used to predict the

chocolate syrup samples without any prior sample preparation. Using a

backpropagation and a cumulative delta mIe, ANN was able to predict aIl sugar

components with high accuracy. A model system using three sugars with randomized

concentrations was used to test the ability of ANN towards quantitative analysis. The

results of a PLS regression method and the ANN model were compared. AIthough

the calibration results were similar, the external validation error of the ANN model

was higher than that obtained with PLS. No indication could be found with regards

to ANN employing a pattern recognition approach to solving quantitative analysis

problems.

Finally, an investigation into the complex interactions between sugar

molecules and water molecules was undertaken using 2D IR correlation. This study

indicated that dilution of a sample results in effect in a complex pattern of band cross

correlation, compounded to band position change and intensity increase.

Unfortunately, the limited specifie-band assignment for each sugar lead to an

inability to relate the observations to aetual structural and chemical changes.

226

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Appendix 1: IR band assignment for sugars

Band Assignment Reference

(cm-I)

424 C3-C4 a-glucose (Zhbankov et al., 1997)

424 C5-C6 a-glucose (Zhbankovetal.,1997)

427 C1-01 J3-g1ucose (Zhbankov et al., 1997)

427 C4-04 J3-glucose (Zhbankov et al., 1997)

427 C l-C2 J3-glucose (Zhbankov et al., 1997)

427 C3-C4 J3-glucose (Zhbankov et al., 1997)

428 bending C-C-C pyranose ln D- (Mathlouthi and Vinh, 1980)fructose

452 CI-OS a-galactose (Zhbankov et al., 1997)

452 C6-06 a-galactose (Zhbankov et al., 1997)

452 C2-C3 a-galactose (Zhbankov et al., 1997)

452 CS-C6 a-galactose (Zhbankov et al., 1997)

460 bending C-C-C furanose ln D- (Mathlouthi and Vinh, 1980)fructose

492 C2-02 a-galactose (Zhbankov et al., 1997)

492 C2-C3 a-galactose (Zhbankov et al., 1997)

492 CS-C6 a-galactose (Zhbankov et al., 1997)

493 C2-02 J3-glucose (Zhbankov et al., 1997)

493 C l-C2 J3-g1ucose (Zhbankov et al., 1997)

493 C5-C6 J3-g1ucose (Zhbankovetal.,1997)

494 CS-OS a-glucose (Zhbankov et al., 1997)

494 CI-OS a-glucose (Zhbankov et al., 1997)

494 C4-CS a-glucose (Zhbankov et al., 1997)

S19- InternaI rotation modes about C- (Hineno, 1977)

800 OH (side bond)

227

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•

530 bending C-C-O pyranose in 0- (Mathlouthi and Vinh, 1980)fructose

530 C4-04 a-glucose (Zhbankov et al., 1997)

530 C2-C3 a-glucose (Zhbankov et al., 1997)

538 C4-04 f3-g1ucose (Zhbankov et al., 1997)

538 C4-C5 P-glucose (Zhbankov et al., 1997)

556 C5-05 P-glucose (Zhbankov et al., 1997)

556 C l-C2 f3-glucose (Zhbankov et al., 1997)

556 C4-C5 f3-glucose (Zhbankov et al., 1997)

557 C 1-01 a-galactose (Zhbankov et al., 1997)

557 C2-02 a-galactose (Zhbankov et al., 1997)

557 CS-05 a-galactose (Zhbankovet al., 1997)

557 C l-C2 a-galactose (Zhbankov et al., 1997)

587 C3-03 a-glucose (Zhbankov et al., 1997)

587 C3 -C4 a-glucose (Zhbankov et aL, 1997)

587 C4-C5 a-glucose (Zhbankov et al., 1997)

587 C5-C6 a-glucose (Zhbankov et al., 1997)

597 C2-02 a-glucose (Zhbankov et al., 1997)

597 C l-C2 a-glucose (Zhbankovet~.,1997)

597 C5-C6 a-glucose (Zhbankov et aL, 1997)

600- Torsional vibrations of J3-D- (Hineno, 1977)700 glucopyranose (crystal)600 C 1-01 J3-glucose (Zhbankov et al., 1997)

600 C l-C2 p-glucose (Zhbankov et al., 1997)

600 C2-C3 p-glucose (Zhbankovet al., 1997)

625 C4-04 a-g~actose (Zhbankov et al., 1997)

625 C3-C4 a-g~actose (Zhbankov et al., 1997)

625 C4-C5 a-galactose (Zhbankov et al., 1997)

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•

•

636 bending C-C-O exocyclic in D- (MatWouthi and Vinh, 1980)fructose

646 C l-C2 a-galactose (Zhbankov et al., 1997)

646 C2-C3 a-galactose (Zhbankov et al., 1997)

646 C3-C4 a-galactose (Zhbankov et al., 1997)

6S3 C6-06 J3-g1ucose (Zhbankovet al., 1997)

653 CS-C6 f3-g1ucose (Zhbankov et al., 1997)

673 C 1-01 a-glucose (Zhbankov et al., 1997)

673 CS-OS a-glucose (Zhbankov et al., 1997)

673 CI-OS a-glucose (Zhbankov et al., 1997)

673 C l-C2 a-glucose (Zhbankov et al., 1997)

694 CS-OS a-galactose (Zhbankov et aL, 1997)

694 CI-OS a-galactose (Zhbankov et aL, 1997)

694 C4-C5 a-galactose (Zhbankovet al., 1997)

712 bending C-C-O furanose in D- (Mathlouthi and Vinh, 1980)fructose

721 C-C, C-O stretching of a-D-glucose (Vasko et al., 1972)(crystal)

767±8 f3-D-glucose, type 3 (Mathlouthi and Koenig, 1986)

774±9 a-D-glucose, type 3 (Mathlouthi and Koenig, 1986)

781±S Pyranose ring (Mathlouthi and Koenig, 1986)

790 amorphous amylose (Mathlouthi and Koenig. 1986)

Be/ow Skeletal deformation and internai (Hineno, 1977)800 rotation modes826 bending C-C pyranose in D-fiuctose (Mathlouthi and Vinh, 1980)

836 P-D-glucose (Back et al., 1984)

836 C5-C6 a-galactose (Zhbankov et al., 1997)

844 Characteristic of the a-glucose (Back et al., 1984)(solution)

844 a-D-glucose (Back and Polavarapu, 1983)846 C6-06 a-glucose (Zhbankov et al., 1997)

229

•

•

•

846 CS-C6 a-glucose (Zhbankov et al., 1997)

8S0±6 Fl;U"anose ring (Mathlouthi and Koenig, 1986)

874±6 p-D-glucose, type 2a (Mathlouthi and Koenig, 1986)

874 C-C bending furanose in D-fiuctose (Mathlouthi and Vinh, 1980)

874 C6-06 P-glucose (Zhbankov et al., 1997)

874 CS-C6p-glucose (Zhbankov et al., 1997)

890±5 0.- D -glucose, type 2a (Mathlouthi and Koenig, 1986)

891 Characteristic of the ~-glucose (Back et al., 1984)(solution)

891 p-o-glucose (Back and Polavarapu, 1983)

893 changes in conformation due to (Mathlouthi and Koenig, 1986)rotation about the interglycosidicbond

898 C6-06 a-galactose (Zhbankov et al., 1997)

898 CS-C6 a-galactose (Zhbankov et al., 1997)

904- C-O & C-C stretching (Hineno, 1977)1153905 a.-D-glucose (Back et a1., 1984)

915±5 13- D -glucose, type 1 (Mathlouthi and Koenig, 1986)

919 C l-C2 a-glucose (Zhbankov et al., 1997)

919 C2-C3 a-glucose (Zhbankov et al., 1997)

921±4 0.- 0 -glucose, type 1 (Mathlouthi and Koenig, 1986)

922 C-C-H bending furanose ID D- (Mathlouthi and Vinh, 1980)fructose

923 stretching CC in sucrose (Kacurakova and Mathlouthi, 1996)

936 C4-C5 P-glucose (Zhbankov et al., 1997)

936 C5-C6 p-glucose (Zhbankov et a1., 1997)

945 C6-06 a-galactose (Zhbankov et al., 1997)

986 C-C-H bending pYfanose in D- (Mathlouthi and Vinh, 1980)fructose

993 Disaccharide link (Cadet et al., 1995)

993 bending COH; stretching CO, (Kacurakova and Mathlouth~ 1996)glycosidic linkage in glucose

230

•

•

•

998 bending COlL stretching CO (Kacurakova and Mathlouthi, 1996)glycosidic linkage in sucrose

998 C5-05 a-glucose 'Zhbankov et al., 1997)

998 C6-06 a-glucose (Zhbankov et al., 1997)

998 C5-C6 a-glucose (Zhbankov et al., 1997)

1000 stretching CO, stretching CC, (Kacurakova and Mathlouthi, 1996)bending COR in lactose

1000 C 1-C2 l3-glucose (Zhbankov et aL, 1997)

1000 C2-C3 l3-g1ucose (Zhbankov et al., 1997)

1015 stretching CO, stretching CC, (Kacurakova and Mathlouth~ 1996)bending COR in sucrose

1019 C3-03 l3-g1ucose (Zhbankov et al., 1997)

1019 C3-C4 J3-glucose (Zhbankov et al., 1997)

1020 C3-C4 a-glucose (Zhbankov et al., 1997)

1020 C4-C5 a-glucose (Zhbankov et al., 1997)

1020 C-Q-H D-glucose (Koenig, 1979)

1022 D-maltose (Koenig, 1979)

1027 C-O-H deformation of a-n-glucose (Vasko et al., 1972)(crystal)

1027 stretching CO, stretching CC, (Kacurakova and Mathlouthi, 1996)bending COR (C40) in maltose

1032 stretching CO, stretching CC, (Kacurakova and Mathlouth~ 1996)bending COR C40 in glucose

1044 C 1-05 a-galactose (Zhbankov et al., 1997)

1044 C5-C6 a-galactose (Zhbankov et aL, 1997)

1045 CI -01 a-glucose (Zhbankov et al., 1997)

1045 C2-C3 a-glucose (Zhbankov et al., 1997)

1046 C2-C3 l3-glucose (Zhbankov et al., 1997)

1047 C I-H deformation of a-n-glucose (Vasko et aL, 1972)(crystal)

1055 stretching CO, stretching CC, ring, (Kacurakova and Mathlouthi, 1996)(C 10) in sucrose

1055 a-D-glucose (Back and Polavarapu, 1983)

231

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•

1058 stretching CO, stretching CC, ring (Kacurakova and Mathlouthi, 1996)CIO, in galactose

1063 stretching CO, stretching CC, ring (Kacurakova and Mathloutbi, 1996)CIO in Fructose

1067 C6-06 J3-glucose (Zhbankov et al., 1997)

1067 C4-C5 tl-glucose (Zhbankov et al., 1997)

1068 C-O stretching exocyclic ln D- (Mathlouthi and Vinh, 1980)fructose

1070 D-maltose (Koenig, 1979)

1072 stretching CO, stretching CC, (Kacurakova and Mathlouthi, 1996)bending COH (CIH) in maltose

1074 stretching CO, stretching CC, (Kacurakova and Mathlouthi, 1996)bending COH (CIH) in lactose

1074 C 1-01 a-galactose (Zhbankov et al., 1997)

1074 C4-04 a-galactose (Zhbankov et al., 1997)

1074 CS-OS a-galactose (Zhbankov et al., 1997)

1076 C I-H & C-O-H defonnation of a,- (Vasko et al., 1972)D-glucose (crystal)

1076 stretching CC, stretching CO, (Kacurakova and Mathlouthi, 1996)bending COH (CIH) in galactose

1078 stretching CO, stretching CC, (Kacurakova and Mathlouthi, 1996)bending COH (CIH) in glucose

1079 D-maltose (Koenig, 1979)

1080 stretching CO, stretching CC, (Kacurakova and Mathlouthi, 1996)bending COH (CIH) in fructose

1081 J3-o-g1ucose (Back and Polavarapu, 1983)

1081 Cl-OS a-glucose (Zhbankov et al., 1997)

1085 C-O stretching of anomeric COR (Back et al., 1984)group of a-D-glucopyranose

1085 CS-OS l3-glucose (Zhbankov et al., 1997)

1085 C6-06 f3-g1ucose (Zhbankov et al., 1997)

1086 COH bending in D-fructose (Mathlouthi and Vinh, 1980)

1087 CI-OS J3-glucose (Zhbankov et al., 1997)

1087 C l-C2 J3-g1ucose (Zhbankov et al., 1997)

1098 C6-06 a-galactose (Zhbankov et al., 1997)

1099 C6-06 a-glucose (Zhbankov et al., 1997)

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1099 C4-CS a-glucose (Zhbankov et al., 1997)

1104 stJ;etching CO ring, C40, C60 in (Kacurakova and Mathlouthi. 1996)fructose

1104 stretching CO, ring (C40, C60) in (Kacurakova and Mathlouthi, 1996)sucrose

1107 stretching C-O ring (C40, C60) in (Kacurakova and Mathlouthi, 1996)Glucose

1109 stretching CO, ring (C40, C60) in (Kacurakova and Mathlouthi, 1996)maltose

1116 galactose in lactose (Kacurakova and Mathlouthi, 1996)

1118 C5-C6 ct-galactose (Zhbankov et al., 1997)

1120 C4-CS ct-glucose (Zhbankov et al., 1997)

1121 C2-02 f3-glucose (Zhbankov et al., 1997)

1121 C4-04 f3-glucose (Zhbankov et al., 1997)

1126 C 1-01 a-glucose (Zhbankov et al., 1997)

1127 C6-06 ct-galactose (Zhbankov et al., 1997)

1127 CS-C6 a-galactose (Zhbankov et al., 1997)

1132 C3-03 f3-glucose (Zhbankov et al., 1997)

1132 C2-02 a-galactose (Zhbankov et al., 1997)

1132 C3-03 a-galactose (Zhbankov et al., 1997)

1133 stretching CO, glycosidic linkage in (Kacurakova and Mathlouthi, 1996)sucrose

1136 C-Q-H bending (Cadet et al., 1995)

1138 C2-02 Il-glucose (Zhbankov et al., 1997)

1138 C4-04 Il-glucose (Zhbankov et al., 1997)

1139 C4-04 a-glucose (Zhbankov et al., 1997)

1142 C-O & C-C stretching with C-H, C- (Vasko et al.) 1972)O-H deformation for a-D-glucose(crystal)

1143 glucose mutarotation (Mathlouthi and Koenig, 1986)

1143 C40H in galactose (Kacurakova and Mathlouthi, 1996)

1147 C2-02 a-glucose (Zhbankovet al., 1997)

1147 CS-C6 a-galactose (Zhbankov et al.) 1997)

1148 stretching CO, glycosidic linkage in (Kacurakova and Mathlouthi, 1996)

233

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•

maltose1148 C 1-01 J3-g1ucose (Zhbankov et al., 1997)

1148 CI-OS J3-g1ucose (Zhbankov et al., 1997)

1149 bending CO fructose (Kacurakova and Mathlouthi, 1996)

1149 stretching CO glycosidic linkage in (Kacurakova and Mathlouthi, 1996)galactose

1150 CO stretching pyranose in 0- (Mathlouthi and Vinh, 1980)fiuctose

Il S4 stretching CO in fructose (Kacurakova and Mathlouthi, 1996)

I1S6 stretching CO, glycosidic linkage in (Kacurakova and Mathlouthi, 1996)lactose

1157 C6-06 J3-glucose (Zhbankov et al., 1997)

1157 C5-C6 J3-glucose (Zhbankov et al., 1997)

1157 C6-06 a-glucose (Zhbankov et al., 1997)

1157 C5-C6 a-glucose (Zbbankov et al., 1997)

1160 C4-04 a-galactose (Zhbankov et al., 1997)

1163 mannose mutarotation (Mathlouthi and Koenig, 1986)

1170 CS-OS J3-glucose (Zhbankov et al., 1997)

1170 CS-C6 J3-glucose (Zhbankov et al., 1997)

1181 stretching CO, in fiuctose (Kacurakova and Mathlouthi~ 1996)

1186 CO stretching furanose fi D- (Mathlouthi and Vinh, 1980)fiuctose

1186 CS-OS a-glucose (Zhbankov et al., 1997)

1186 CS-C6 a-glucose (Zhbankov et al., 1997)

1186 CS-OS a-galactose (Zhbankov et al., 1997)

1186 Cl-OS a-galactose (Zhbankov et al., 1997)

1189 C-O & C-C stretching a-o-glucose (Vasko et al., 1972)(crystal)

1199- O-C-fL C-C-H and C-O-H bending (Hineno, 1977)1474 vibration for glucose (solution)1200- Richest in structural information (Mathlouthi and Koenig, 1986)15001219 CH2 for a-o-glucose (crystal) (Vasko et al., 1972)

1231 C 1-01 a-glucose (Zhbankovet al., 1997)

234

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•

,1237 C 1-01 a-galactose (Zhbankov et al., 1997)

1237 C l-C2 a.-galactose (Zhbankov et al., 1997)

1250 CI-H defonnation a-D-glucose (Vaskaetal., 1972)(crystal)

1250 C 1-01 P-glucose (Zhbankov et aL, 1997)

1250 C l-C2 f3-glucose (Zhbankov et al., 1997)

1250 C3-C4 a-galactose (Zhbankov et aL, 1997)

1254 C2-02 a-glucose (Zhbankov et al., 1997)

1255 bending CH (C1H) in fructose (Kacurakova and Mathlouthi, 1996)

1256 V-complex of amylose (Mathlouthi and Koenig, 1986)

1258 C3-C4 f3-glucose (Zhbankov et al., 1997)

1260 bending CH CIH in sucrase (Kacurakova and Mathlouthi, 1996)

1262 bending CH (C1H) in lactose (Kacurakova and Mathlouthi, 1996)

1266 twisting CH2 in D-fructose (Mathlouthi and Vinh, 1980)

1267 C2-C3 f3-glucose (Zhbankov et al., 1997)

1268 C6-06 f3-glucose (Zhbankov et aL, 1997)

1268 C5-C6 f3-glucose (Zhbankov et al., 1997)

1270 C6-0-H &/or CI-0-H related (Vasko et al., 1972)vibration for a-D-glucose (crystal)

1270 C4-C5 a-galactose (Zhbankov et al., 1997)

1272 C3-03 a-glucose (Zhbankov et al., 1997)

1272 C2-C3 a-glucose (Zhbankov et al., 1997)

1278 C6-06 a-glucose (Zhbankov et al., 1997)

1278 C6-06 a-galactose (Zhbankov et al., 1997)

1279 C3-C4 a-glucose (Zhbankov et al., 1997)

1286 C6-06 f3-glucose (Zhbankov et al., 1997)

1286 C4-C5 f3-glucose (Zhbankov et al., 1997)

1313 C4-04 a-glucose (Zhbankov et aL, 1997)

1313 C6-06 a-glucose (Zhbankov et al., 1997)

1314 C5-05 f3-glucose (Zhbankov et al., 1997)

235

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1314 C3-C4 fJ-glucose (Zhbankovet al., 1997)

1314 C4-C5 fJ-glucose (Zhbankov et al., 1997)

1320 anomeric CH defonnation of fl-D- (Back et al., 1984)J!:lucose (solution)

1320 f3-D-glucose (Back and Polavarapu, 1983)

1320 C2-02 f3-glucose (Zhbankov et aL, 1997)

1320 C l-C2 f3-gIucose (Zhbankov et al., 1997)

1322 C 1-01 a-glucose (Zhbankov et al., 1997)

1322 C l-C2 a-glucose (Zhbankov et aL, 1997)

1322 C2-C3 a-glucose (Zhbankov et al., 1997)

1328 C4-C5 a-galactose (Zhbankov et al., 1997)

1328 C5-C6 a-galactose (Zhbankov et al., 1997)

1332 C3-03 f3-glucose (Zhbankov et al., 1997)

1332 C3-03 a-glucose (Zhbankov et al., 1997)

1332 C3-C4 a-glucose (Zhbankov et al., 1997)

1339 anomeric C-H deformation of a.-D- (Back et al., 1984)J!:lucose (sin)

1339 a.-o-glucose (Back and Polavarapu, 1983)

1342 C4-C5 a-glucose (Zhbankov et al., 1997)

1342 C5-C6 a-glucose (Zhbankov et al., 1997)

1344 wagging CH, bending OH (C4) in (Kacurakova and Mathlouthi, 1996)fiuctose

1349 C-O-H a-D-glucose (Koenig, 1979)

1357 C 1-01 f3-g1ucose (Zhbankov et al., 1997)

1357 C2-02 f3-glucose (Zhbankov et al., 1997)

1357 C 1-05 f3-glucose (Zhbankov et al.) 1997)

1358 CS-05 ex-galactose (Zhbankov et al.) 1997)

1361 bending OH, bending CH in (Kacurakova and Mathlouthi, 1996)galactose

1362 bending OH, bending CH ln (Kacurakova and Mathlouthi, 1996)J!:lucose

1373 C4-04 a-galactose (Zhbankov et al., 1997)

236

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1373 C3-C4 a-galactose (Zhbankov et al., 1997)

1373 C~-C6 a-galactose (Zhbankov et al., 1997)

1374 C2-02 a-glucose (Zhbankov et al., 1997)

1374 Cl-OS a-glucose (Zhbankov et al., 1997)

1375 CS-OS a-glucose (Zhbankov et al., 1997)

1376 wagging CH2 in D -fructose (Mathlouthi and Vinh, 1980)

1378 C2-02 a-galactose (Zhbankov et al., 1997)

1384 difference in the extent of coupling (Back et al., 1984)of anomeric C-H in a.-D-glucose(solution)

1384 C2-02 a-galactose (Zhbankov et al., 1997)

1385 CS-OS f3-glucose (Zhbankov et al., 1997)

1385 CS-C6 f3-glucose (Zhbankov et al., 1997)

1414 difference in the extent of coupling (Back et al., 1984)of anomeric C-H in a.-D-glucose(solution)

1418 bending CH, bending OH in (Kacurakova and Mathlouthi, 1996)glucose

1418 bending CH, bending OH in (Kacurakova and Mathlouthi, 1996)galactose

1418 bending CH, bending OH in (Kacurakova and Mathlouthi, 1996)fructose

1418 bending CH, OH in lactose (Kacurakova and Mathlouthi, 1996)

1420 C2-02 a-galactose (Zhbankov et al., 1997)

1428 C4-04 a-galactose (Zhbankov et al., 1997)

1429 environment of the C6 group (Mathlouthi and Koenig, 1986)

1429 C 1-01 a-glucose (Zhbankov et al., 1997)

1429 C2-02 a-glucose (Zhbankovet al., 1997)

1435 bending CH (ClaR) in glucose (Kacurakova and Mathlouthi, 1996)

1435 bending CH (ClaR) in galactose (Kacurakova and Mathlouthi, 1996)

1435 bending CH (ClaR) in fructose (Kacurakova and Mathlouthi, 1996)

1438 C 1-0 1 J3-g1ucose (Zhbankov et al., 1997)

1438 C3-03 J,3-glucose (Zhbankov et al., 1997)

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1442 C 1-01 a-galactose (Zhbankov et al., 1997)

1444 C3-03 a-glucose (Zhbankov et al., 1997)

1444 C4-04 a-glucose (Zhbankov et al., 1997)

1447 C 1-01 P-glucose (Zhbankov et al., 1997)

1447 C4-04 P-glucose (Zhbankov et al., 1997)

1450 C6-06 a-glucose (Zhbankov et al., 1997)

1451 C6-06 P-glucose (Zhbankov et al., 1997)

1452 C6-06 a-galactose (Zhbankov et al., 1997)

1453 bending CH2 in maltose (Kacurakova and Mathlouthi, 1996)

1456 bending CH2 in glucose (Kacurakova and Mathlouthi, 1996)

1456 bending CH2 in galactose (Kacurakova and Mathlouth~ 1996)

1456 bending CH2 in fructose (Kacurakova and Mathlouthi, 1996)

1456 bending CH2 in lactose (Kacurakova and Mathlouth~ 1996)

1460 bending CH2 in D-fructose (Mathlouthi and Vinh, 1980)

1461 C3-03 a-galactose (Zhbankov et al., 1997)

1462 C4-04 P-glucose (Zhbankov et al., 1997)

1462 C3-03 ex-glucose (Zhbankov et al., 1997)

1462 C4-04 ex-glucose (Zhbankov et al., 1997)

1465 CH2 scissoring vibration (Hineno, 1977)

1465 C2-02 J3-glucose (Zhbankov et al., 1997)

1468 C2-02 ex-glucose (Zhbankovet al., 1997)

1640 bending H20 (Mathlouthi and V~ 1980)

1728 Open chain form of fructose (Yaylayan and Ismail, 1992)

2820- C-H stretching (Hineno, 1977)29802890 CH stretching of p-D- (Back et al., 1984)

glucopyranose2935 CH stretching of ex-D- (Back et al., 1984)

glucopyranose3216 correlated to O... 0 distance (Mathlouthi and Koenig, 1986)

3250 OH stretching (Hineno, 1977)

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•

3279- free O-H stretching (Mathlouthi and Koenig, 1986)34723442 correlated to 0 ... 0 distance (Mathlouthi and Koenig, 1986)

3470 correlated to 0 ... 0 distance (Mathlouthi and Koenig, 1986)

3485 hydrogen bonded Q-H (Mathlouthi and Koenig, 1986)

3530 correlated to 0 ... 0 distance (Mathlouthi and Koenig, 1986)

239

•Appendix 2: Concentration distribution of "sugar-spiked"

chocolate syrup samples

Sample Maltose Fructose Glucose Sucrose Total Sugar

# (%) (0/0) (%) (%) (0/0)

2.78 11.91 7.23 19.72 42.27

2 3.77 4.44 12.77 Il.04 32.01

3 5.18 3.04 7.68 20.90 36.81

4 1.84 12.26 10.94 15.60 40.64

5 3.16 11.71 6.71 14.17 35.75

6 5.41 8.90 14.61 Il.28 40.21

7 4.38 3.27 14.55 18.23 40.44

8 5.95 4.82 10.11 8.75 29.62• 9 4.06 7.07 11.12 8.68 30.93

10 5.31 6.57 14.51 13.90 40.30

Il 1.94 9.05 6.94 7.30 25.23

12 3.09 6.43 14.38 18.89 42.79

13 2.66 6.12 13.44 20.49 42.70

14 3.03 7.11 6.34 6.88 23.36

15 5.19 3.5 12.08 6.96 27.75

16 1.14 12.31 13.59 10.54 37.59

17 2.61 3.21 6.21 9.54 21.58

18 2.34 2.16 8.64 13.26 26.40

19 2.09 5.12 6.45 17.26 30.92

20 4.25 6.54 8.22 16.71 35.71

21 5.14 9.37 10.23 16.07 40.81

The concentrations are expressed in % weight hy weight.

• 240

• Appendix 3: Summary of sucrose, glucose and fructose

mixture solutions prepared for ANN study

Sample Sucrose Glucose Fructose Total Sugars Water(%) (%) (%) (0A.) (0/0)

1 22.15 23.72 15.84 61.71 38.29

2 10.47 8.93 19.73 39.13 60.87

3 5.04 20.98 8.54 34.56 65.44

4 23.14 15.92 16.40 55.46 44.54

5 14.97 29.00 10.03 54.00 46.00

6 20.65 23.68 5.63 49.96 50.04

7 14.88 26.88 13.74 55.50 44.50

8 13.73 8.97 5.71 28.41 71.59

9 28.10 8.98 3.59 40.67 59.33

• 10 6.69 5.95 4.92 17.56 82.44

Il 3.01 Il.04 7.24 21.29 78.71

12 13.82 25.20 20.71 59.73 40.27

13 23.47 18.76 3.56 45.79 54.21

14 18.71 12.26 15.55 46.52 53.48

15 22.78 Il.02 12.35 46.15 53.85

16 14.74 9.20 12.49 36.43 63.57

17 12.85 7.43 16.59 36.87 63.13

18 12.99 11.97 12.89 37.85 62.15

19 6.98 4.93 14.66 26.57 73.43

20 18.86 15.13 20.71 54.70 45.30

21 20.86 9.17 19.94 49.97 50.03

22 11.94 19.94 9.95 41.83 58.17

23 4.91 16.91 5.46 27.28 72.72

• The concentrations are expressed in % weight by weight.

241

Table 3.6, conlinued• Sample Sucrose Glucose Fructose Total Sugars Water(%) (%) (%) (%) (0/0)

49 15.88 15.92 12.78 44.58 55.42

50 17.37 26.75 14.79 58.91 41.09

51 13.01 4.96 5.06 23.03 76.97

52 9.07 27.74 20.65 57.46 42.54

53 4.93 8.33 17.75 31.01 68.99

54 19.61 13.91 5.14 38.66 61.34

55 6.34 10.90 12.83 30.07 69.93

56 24.88 17.95 12.13 54.96 45.04

57 30.00 5.09 12.09 47.18 52.82

58 4.93 25.50 9.73 40.16 59.84

59 28.02 22.58 10.02 60.62 39.38

60 16.11 21.05 17.72 54.88 45.12

• 61 7.95 18.55 5.21 31.71 68.29

62 17.20 16.01 19.09 52.30 47.70

63 29.73 5.18 21.30 56.21 43.79

64 23.81 27.12 5.15 56.08 43.92

65 12.07 14.15 14.90 41.12 58.88

66 17.64 9.82 16.42 43.88 56.12

67 29.78 15.81 6.98 52.57 47.43

68 10.78 5.66 8.92 25.36 74.64

69 23.54 4.81 5.53 33.88 66.12

The concentrations are expressed in % weight by weight.

• 243

• Appendix 4: Table of best fit linear regression equations for

the predicted vs. actual of aU calibrations

1. Calibrations based on full sugar region (Section 4.1.1.3.)

Maltose

Fructose

Glucose

Sucrase

Total Sugar

Water

y= 0.015 + 0.996x

y= 0.013 + 0.998x

y= -0.016 + 1.00lx

y= 0.142 + O.991x

y= 0.121 + 0.998x

y= -0.085 + 1.000x

2. Calibrations based on restricted sugar regions (Section 4.1.1.3.)

• Maltose y= 0.011 + 0.997x

Fructose y= 0.038 + 0.995x

Glucose y= 0.008 + 0.999x

Sucrase y= -0.097 + 1.004x

Total Sugar y= 0.024 + 0.999x

•

3. Calibrations based on sugar solutions recorded using the fiber optic probe

(Section 4.1.1.4.)

Maltose y= -0.002 + 1.001x

Fructose y= 0.020 + 0.997x

Glucose y= -0.026 + 1.002x

Sucrase y= 0.142 + 0.991x

Total Sugar y= -0.003 + 1.000x

Water y= 0.222 + 0.996x

244

4. Calibrations for chocolate syrup analysis (Section 4.1.2.3.4.)•

•

•

Maltose

Fructose

Glucose

Sucrase

Total Sugar

Water

y= 0.067 + 0.970x

y= 0.034 + O.992x

y= 0.041 + 0.995x

y= 0.069 + 0.995x

y= 0.141 + O.995x

y= 0.147 + 0.998x

245

•Appendix 5: PRESS for ail the calibrations developed

l. Calibrations based on full sugar region (Section 4.1.1.3.)

50 200a•

40 Maltose FructoseISO

en 300CZlen

~ 200

CZl 100

~c..500

100

0

__________8_

0 ---------a 2 4 6 8 10 12 0 2 4 6 8 10 12

Factor700 Factor

500 •• 600

• 400 Glucose 500 SucraseCI')

300 00 400CI') CZl

~ ~ 300a... 200

200100

100 -0 ---------- ~_____-._-a0

0 2 4 6 8 la 12 0 2 4 6 8 la 12Factor Factor

•800 •

600 Total Sugar Wateren CZlen CZl

~ 400 ~ 200

Q.. Q.,

200

\_-------0 ""------a--- 0

0 2 4 6 8 10 12 0 2 4 6 8 10 12

• Factor Factor

246

•2. Calibrations based on restricted sugar regions (Section 4.1.1.3.)

Fructose

_ __e-a -.

•

o

500

Maltose

o

o 2 4 6 8

Factor10 12 o 2 4 6 8

Factor10 12

sooo •Glucose Sucrase

4000

CI) 3000 enen en

~ ~• Q,., 2000 c..

1000

0 ----------------------. 0-...____~__e-a____a____

0 2 4 6 8 10 12 0 2 4 6 8 10 12

Factor Factor

­"----------

Total Sugar

•

8000 •\

6000

enen~ 4000

Q,.,

2000

0

0 2 4 6

Factor8 10 12

247

• 3. Calibrations based 00 sugar solutions recorded usiog the fiber optic probe

(Section.4.1.1.4.)

•

._------.-._--12la86

Factor

Fructose

4

---._--._--2o

a

1210

Maltose

6 8

Factor42

400 •

300

CZlCZl~ 200Q..

100

0

a

• 4. Calibrations for chocolate syrup analysis (Section 4.1.2.3.4)

30

5 •• 25

4 \ Maltose Fructose20

3CI) 00 15

~ 2 ~~ Q.c 10

5-------0 ---- 0 -.-----.---0 2 4 6 8 10 12

0 2 4 6 8 10 12Factor Factor

100

• Glucose250

Sucrase80

200

60150

CI') CI)

• ~ 40 ~~ Q.c 100

20 50

0 -----------11-11------- 0 ----tI-----------a 2 4 6 g 10 12 0 2 4 6 8 la 12

Factor Factor

1200100 ••

1000Total Sugar Water800

800600

60000

CI')

~~ 400~~ 400

200200 ____..-----a----- 0 ----------0

0 2 4 6 8 la 12 0 2 4 6 8 la

Factor Factor

•249

•

•

Maltose

Fructose

Glucose

Sucrase

Total Sugar

Water

y= -0.093 + 0.577x

y= -0.657 + 0.525x

y= 0.104 + 0.538x

y= 0.043 + 0.547x

y= -0.044 + 0.553x

y= 41.803 + 0.555x

250

• Appendix 7: Concentration dependent band shift in

sucrose, fructose, and maltose

Sucrose

Concentration Band at

996 cm-1

Band at

1054 cm-1

Band at

1137 cm-1

Band at

1639 cm-1

5% 996.21 1054.98 1137.10 1639.23

10% 997.96 1055.35 1137.26 1636.15

15% 998.07 1055.43 1137.11 1636.64

20% 997.89 1054.85 1137.09 1639.60

• 25% 997.62 1054.66 1136.80 1640.04

30% 997.33 1054.42 1136.56 1640.28

35% 997.20 1054.07 1136.11 1640.73

40% 996.95 1053.86 1136.11 1640.81

45% 996.63 1053.31 1136.09 1641.15

50% 996.14 1052.97 1135.59 1641.39

55% 995.90 1052.66 1135.41 1641.52

60% 995.62 1051.92 1135.30 1642.52

65%) 995.60 1052.26 1135.01 1642.27

•251

•Fructose

ConcentrationBand at

1063 cm-1

Band at

1156 cm-1

Band at

1639 cm-1

5% 1063.36 1156.10 1639.41

10% 1063.39 1156.06 1639.56

15% 1062.51 1153.58 1640.45

20% 1062.92 1154.95 1640.38

25% 1063.22 1154.68 1639.73

30% 1062.08 1153.30 1640.34• 35% 1061.98 1153.30 1640.91

40% 1061.50 1152.51 1641.20

45% 1061.00 1152.52 1641.39

50% 1060.77 1152.18 1641.93

55% 1060.24 1151.55 1641.92

60% 1059.64 1151.24 1642.37

65% 1058.97 1150.25 1642.45

• 252

•

Maltose

ConcentrationBand at

1037 cm-I

Band at

1074 cm-I

Band at

1110 cm-I

Band at

1148 cm-l

Band at

1639 cm-I

5% 1037.91 1074.59 1110.11 1148.80 1639.45

10% 1037.69 1075.91 1110.90 1149.43 1639.84

15% 1038.70 1076.92 1109.42 1149.45 1641.00

• 20% 1038.58 1076.89 1109.96 1149.57 1642.26

25% 1037.79 1076.75 1107.96 1149.32 1641.11

30% 1037.62 1076.86 1109.40 1149.28 1642.08

35% 1036.48 1076.65 1109.60 1149.18 1641.24

40% 1036.38 1076.53 1109.28 1148.94 1642.13

45% 1035.47 1076.53 1108.50 1148.89 1642.63

50% 1032.25 1076.40 1107.96 1148.69 1642.87

• 253

• Appendix 8: 2D IR asynchronous correlation peaks for

fructose

Band Position on Y-axis Band Position on X-axis Peak Sign

980 1057

930 1053

965 1103 +1084 +1065 +

977 1084 +1065 +1049

1007 1103 +1084 +

• 1065 +

1035 1161 +1103 +084 +1065 +

1049 1458 +1419 +1346 +1265 +1254 +1188 +1161 +1103 +1084 +1065 +10491026

• 254

Band Position on Y-axis Band Position on X-axis Peak Sign• 1066 114610991076104910301014975.8964.2

1077 1088 +1068 +104910301014975.8964.2

1086 104910301014

• 975.8964.2

• 255

• Appendix 9: 2D IR asynchronous correlation peaks for

glucose

Band Position on Y-axis Band Position on X-axis PeakSign

1435 +991

1365 +1153 +1111 +1080 +1038 +1439 +

10151369 +1157 +1111 +1080 +1038 +1007

• 983.5944.9 +

1019 1431 +1365 +1257 +1153 +1111 +1080 +1038 +991.2 +1435 +

1049 1365 +1157 .+1111 +1080 +1038 +1007983.5

• 256

Band Position on Y-axis Band Position on X-axis Peak Sign• 1060 1435 +1365 +1157 +1111 +1080 +1061 +1038 +1007983.5 +

1068 1435 +1365 +1157 +1111 +1080 +1038 +1007983.5

1096 1365 +

• 1153 +IIlI +1080 +1038 +1007

1145 III 1 +1080 +1038 +

• 257

• Appendix 10: 2D IR asynchronous correlation peaks for

sucrose

Band Position on Y-axis Band Position on X-axis Peak Sign

929 1061 +983.5

987.85 1454 +1435 +1161 +1142 +1119 +1061 +1018 +1003 +983.5

1038 1165 +• 1142 +1080 +1061 +1041 +1014987.4921.8

1059 1126109910761041987.4925.7

• 258

•

•

•

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