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This is to certify that the
dissertation entitled

EVALUATION OF LIGHT-OXIDIZED OFF-FLAVORS IN
REDUCED FAT MILK AND CHEDDAR CHEESE USING
SENSORY EVALUATION AND THE ELECTRONIC NOSE

presented by
HSlN-YEN CHUNG

has been accepted towards fulfillment
of the requirements for the

Doctoral degree in Packaging

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EVALUATION OF LlGHT-OXIDIZED OFF-FLAVORS IN REDUCED FAT MILK
AND CHEDDAR CHEESE USING SENSORY EVALUATION AND THE
ELECTRONIC NOSE
By

Hsin-Yen Chung

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
School of Packaging

2004

ABSTRACT
EVALUATION OF LlGHT—OXIDIZED OFF-FLAVORS IN REDUCED FAT MILK
AND CHEDDAR CHEESE USING SENSORY EVALUATION AND THE
ELECTRONIC NOSE
By

Hsin-Yen Chung

Exposure to light can result in Changes of sensory and nutritional qualities
of foods, including milk and other dairy products. This study was to evaluate the
light-oxidized and/or packaging off-flavors in milk and Cheddar cheese using
sensory evaluation, solid-phase microextraction coupled with gas
chromatography (SPME—GC), and an electronic nose (FOX 3000, AlphaMOS)
equipped with 12 metal oxide semiconductor (MOS) sensors.

Reduced fat (2%) milk in glass bottles exposed to fluorescent light (1000
Ix) at 5°C developed a significant light-oxidized off-flavOr in 8 hours, which was
detected by both consumer and trained panelists. Headspace pentanal and
hexanal increased as the light exposure time increased. Using a 95°C headspace
temperature for the electronic nose analysis provided better model discrimination
than 45°C or 70°C. By defining the harmful light exposure time as a milk
deterioration threshold, the 95°C linear discriminant analysis (LDA) model
correctly recognized 97% of the milk samples exposed to light for 8 hours or
longer. Quantitatively, the 95°C partial least squares (PLS) model provided better
prediction of the sensory scores than the 95°C multilayer perceptrons (MLP)

model.

The different packaging materials (237 ml glass, HDPE, HDPE—TiOz, PET
bottles, and PE-Coated paper cartons) were clearly discriminated and identified
using the electronic nose. However, packaging off-flavors in water and 2% milk
were not clearly defined by both sensory evaluation and electronic nose analysis.
The effect of packaging materials on light-induced oxidation in milk was cloSely
related to their light barrier properties. PE-coated paper cartons reduced light-
oxidation of 2% milk significantly (12 hours of light exposure, 1000 Ix), while
HDPE-Ti02 gave only partial light protection.

Light-induced quality changes (color and flavor) were limited to the top
surface of the vacuum-packaged Cheddar cheese, which was the portion that
was exposed to light (2000 Ix). Discoloration and off-flavors were detected by the
trained panel after exposure to light for 2 weeks. Color measurement showed a
continuous decrease in yellowness contributed the most to the discoloration,
along with a relatively small decrease in redness and increase in lightness. The
discriminant models of sensor responses using 90°C headspace samples had
higher correct identification rates and better discrimination than at 60°C. The
90°C PLS model provided better prediction of the sensory scores than the 90°C
MLP model.

The established discriminant and correlation models have shown the
potential of the electronic nose to be used as a complementary approach to
sensory evaluation to determine light-oxidized off-flavors in packaged milk and

Cheddar cheese.

Copyright by
HSlN-YEN CHUNG
2004

ACKNOWLEDGMENTS

I would like to express my sincere gratitude to Dr. Bruce Harte, the most
wonderful teacher, advisor, and mentor a graduate student can have, for all of his
guidance on this dissertation study and his help, support and trust since the very
first day of my student life in Michigan State University.

I would like to thank Dr. John Partridge, for Showing me the ropes in
judging sensory qualities of milk and dairy products, and having the MSU dairy
judging team involved as the trained panel in this study. I would also like to thank
Dr. Theron Downes, Dr. Susan Selke and Dr. Zeynep Ustunol, for their
invaluable advices and suggestions in the graduate committee.

I am very grateful for the help and friendship from all the graduate
students in the School of Packaging, including their kindness of participating as
consumer panelists. Many thanks to many of them who provided direct
assistance to me: Krittika Tanprasert, Antonis Kanavouras, Ubonratana
Siripatrawan, Pattra Maneesin, Young Suk Lee, Rafael Auras, Li Xiong and
lsinay Yuzay. I also wish to extend my thanks to Robert Hurwitz and Mr. Nate for
their great help in setting up and troubleshooting the hardware for the
experiments.

I would like to thank the Center for Food and Pharrnaoeutical Packaging
Research (CFPPR) for the financial support. I would also like to thank Melody

Farms in Lansing, Michigan for providing paper carton samples with no charge.

TABLE OF CONTENTS

LIST OF TABLES ........................................... I ...................................................... x
LIST OF FIGURES ............................................................................................. xii
CHAPTER 1 INTRODUCTION .......................................................................... 1
CHAPTER 2 REVIEW OF LITERATURE ........................................................... 4
2.1. Light-induced oxidation of milk .................................................................. 4
2.1.1. Photosensitized oxidation ........................................................................ 5
2.1.1.1. Degradation of milk proteins ............................................................. 7
2.1.1.2. Oxidation of milk lipids ...................................................................... 9
2.1.1.3. Vitamin destruction ......................................................................... 10
2.1..2 Wavelength dependent impact of light .................................................. 12
2.1..2 1. Light absorption .............................................................................. 12
21.2 2. Quantum yield ................................................................................. 15
2.1..2 3. Light sources .................................................................................. 16
2.1.3 Occurrences of light-oxidized off-flavors in milk ..................................... 19
2.2. Effects of milk packaging .......................................................................... 23
2.2.1. Off-flavors from milk packaging materials......... ..................................... 23
2.2.1.1 . Polyethylene (PE) ........................................................................... 24
2.2.1.2. Polyethylene terephthalate (PET) ................................................... 26
2.2.1.3. Paper cartons ................................................................................. 27
2.2.2. Light barrier properties of packaging materials ...................................... 29
2.2.3. Other packaging factors affecting milk quality ....................................... 32
2.2.3.1. Size and shape ............................................................................... 32
2.2.3.2. Oxygen permeability ....................................................................... 33
2.3. Light oxidation of cheese .......................................................................... 34
2.3.1. Discoloration of cheese ......................................................................... 34
2.3.2. Light-induced flavor Changes of Cheese ................................................ 37
2.4. Electronic nose .......................................................................................... 41
2.4.1. Sensor arrays ........................................................................................ 45
2.4.1.1. Metal oxide semiconductors (MOS) ................................................ 49
2.4.1.2. Conductive organic polymer (CP) ................................................... 50
2.4.1.3. Quartz crystal microbalance (QCM) and surface acoustic wave
(SAW) .......................................................................................................... 52
2.4.1.4. Metal-oxide semiconductors field-effect transistors (MOSFET) ...... 53
2.4.2. Signal processing .................................................................................. 54

vi

2.4.3. Data processing .................................................................................... 58

2.4.4. Applications of the electronic nose ........................................................ 60
2.4.4.1. Electronic nose in milk applications ................................................ 60
2.4.4.2. Electronic nose in cheese applications ........................................... 63
2.4.4.3. Electronic nose in packaging applications ...................................... 66

2.5 Solid-phase microextraction (SPME) ........................................................ 68
2.6. Multivariate statistical techniques ............................................................ 75

2.6.1. Unsupervised learning techniques ........................................................ 75
2.6.1.1 Hierarchical clustering analysis (HCA) ............................................. 75
2.6.1.2 Principle component analysis (PCA) ................................................ 77

2.6.2. Supervised learning techniques ............................................................ 81
2.6.2.1 Linear/quadratic discriminant function analysis (LDA/QDA) ............. 81
2.6.2.2. k-nearest neighbor (k-NN) .............................................................. 87

2.6.3. Quantitative analyses ............................................................................ 89
2.6.3.1 Partial least square (PLS) regression .............................................. 89

CHAPTER 3 MATERIALS AND METHODS ................................................... 94
3.1. Sample preparation .............................................................................. A ..... 9 4

3.1.1. Light-oxidized off-flavors in 2% milk ...................................................... 94

3.1.2. Packaging off-flavors in 2% milk ............................................................ 96

3.1.3. Light-oxidized and packaging off-flavors in 2% milk .............................. 99

3.1.4. Cheddar cheese samples .................................................................... 100

3.2. Sensory evaluation ................................... 104
3.2.1. Triangle tests performed by a consumer panel ................................... 104
3.2.2. Rating based on ADSA guidelines performed with a trained panel ..... 105
3.2.3. Difference-from-control tests using a trained panel ............................. 108
3.3. Electronic nose analysis ......................................................................... 108
3.4. Solid-Phase Microextraction-Gas Chromatography (SPME-GC) ......... 110

3.4.1. SPME-GC for milk samples ................................................................. 111

3.4.2. SPME-GC for Cheddar cheese samples ............................................. 112
3.5. Light transmission of milk packaging materials ................................... 112
3.6 Color measurement of cheese .............................................................. , ...113
3.7. Multivariate statistical techniques .......................................................... 1 15

3.7.1. Data preprocessing ............................................................................. 115

3.7.2. Unsupervised learning techniques ...................................................... 115

3.7.3. Supervised learning techniques .......................................................... 1 16

3.7.4. Quantitative analyses .......................................................................... 117

vii

CHAPTER 4 RESULTS AND DISCUSSION ................................................. 120

4.1. Light-oxidized off-flavors in 2% milk ...................................................... 120
4.1.1. Sensory evaluation .............................................................................. 120
4.1.2. Headspace analysis using SPME-GC ................................................. 124

4.1.2.1. Optimizing the headspace sampling parameters .......................... 124
4.1.2.2. Quantifying the headspace volatiles of light-oxidized milk ............ 128
4.1.3. Headspace analysis using the electronic nose .................................... 131
4.1.4. Correlation of results from sensory and instrumental analyses ........... 147

4.2. Packaging and light-oxidized off-flavors ............................................... 157
4.2.1. Off-flavors from packaging materials ................................................... 157
4.2.2. Packaging off-flavors in water and 2% milk ......................................... 167

4.2.2.1. Sensory evaluation ....................................................................... 167
4.2.2.2. Headspace analysis using the electronic nose ............................. 171
4.2.3. Packaging and light oxidized off-flavors in 2% milk ............................. 178
4.2.3.1. Sensory evaluation ....................................................................... 180
4.2.3.2. Headspace analysis using the electronic nose ............................. 182

4.3. Light oxidation of Cheddar cheese ........................................................ 186
4.3.1. Discoloration of Cheddar cheese ........................................................ 186
4.3.2. Body and texture of Cheddar cheese .................................................. 191
4.3.3. Light-induced flavor Changes of Cheese .............................................. 194

4.3.3.1. Sensory evaluation ....................................................................... 194
4.3.3.2. Headspace analysis using SPME-GC ........................................... 194
4.3.3.3. Headspace analysis using the electronic nose ............................. 205

CHAPTER 5 CONCLUSIONS ....................................................................... 219

APPENDICES ................................................................................................... 222

A.1. SAS and Matlab programs ...................................................................... 222
A11. Data import in SAS ............................................................................. 222
A12. HCA and PCA using SAS ................................................................... 223
A13. LDA/QDA and k-NN using SAS .......................................................... 225
A14. PLS using SAS and MLP using Matlab ............................................... 229

A.2 Designs and Forms of Sensory Tests .................................................... 233
A21. Triangle tests ...................................................................................... 233
A22. Milk scoring card based on ADSA guidelines ...................................... 238

A.3 Boxplot Interpretation .............................................................................. 242

A.4. Preliminary results .................................................................................. 243
A41. Light-oxidized off-flavors in 2% milk .................................................... 243
A.4.2. Light oxidation of Cheddar cheese ...................................................... 246

viii

BIBLIOGRAPHY .............................................................................................. 247

LIST OF TABLES

Table 1 lntemal and external factors affecting the photosensitivity of milk and

dairy products to visible light. ....................................................................... 19
Table 2 Commercially available electronic nose instruments. ........................... 44
Table 3 Classification of chemical sensors that have been used. ..................... 46

Table 4 Comparative properties and performance of the most frequently used

gas sensors in electronic nose instruments. ................................................ 48
Table 5 Recent electronic nose applications in natural products. ...................... 61
Table 6 Types of SPME fibers and their recommended use. ............................ 71

Table 7 Oxygen and water vapor transmission rates of the Cryovac® B-series
bags, given the temperature (°C) and relative humidity (%RH). ................ 100

Table 8 Product attributes and scores of the ADSA guideline for scoring off-
flavors in milk. ............................................................................................ 106

Table 9 Product attributes and scores of the ADSA guideline for scoring flavor
and body/texture defects of Cheddar cheese. ........ . .................................. 107

Table 10 Triangle test results on light-oxidized 2% milk in glass bottles against
control. ....................................................................................................... 121

Table 11 Sensory scores of light-oxidized 2% milk based on ADSA guidelines.
................................................................................................................... 123

Table 12 Coefficient of variation (%CV) for the CARIPDMS fiber used to quantify
volatiles in the standard aqueous solution. ................................................ 126

Table 13 Coefficient of variation (%CV) for the DVB/PDMS fiber used to quantify
volatiles in the standard aqueous solution. ................................................ 126

Table 14 Triangle test results on HPLC grade water in glass, HDPE, PET bottles
and PE-Coated paper cartons at 5°C for 3 days ......................................... 168

Table 15 Triangle test results on 2% milk in glass, HDPE, PET bottles and PE-
coated paper cartons, at 5°C for 3 days ..................................................... 168

Table 16 Sensory scores of 2% milk in glass, HDPE, HDPE-TiOz, PET bottles
and PE-coated paper cartons at 5°C for 3 days ......................................... 170

Table 17 Thickness of the tested packaging materials. ................................... 179

Table 18 A series of triangle tests on 2% milk stored in HDPE, HDPE-TiOz, PET
bottles and PE-coated paper cartons, and exposed to 12 hours of
fluorescent light at 5°C. .............................................................................. 180

Table 19 Sensory scores of 2% milk stored in glass, HDPE, HDPE-TiOz, PET
bottles, and PE-coated paper cartons, and exposed to 12 hours of
fluorescent light at 5°C. .............................................................................. 181

Table 20 Difference-from-control sensory scores in color1 of Cheddar Cheese
samples. .................................................................................................... 187

Table 21 Color measurements (CIE L* a* b*) of the Cheddar cheese samples
exposed to 0, 2, 4 or 6 weeks of the fluorescent light (2000 Ix). ................ 189

Table 22 Hue angle, tan"(b/a), of the Cheddar cheese samples exposed to 0, 2,
4 or 6 weeks of the fluorescent light (2000 Ix) ............................................ 190

Table 23 Difference-from-control sensory scores of body and texture1 of
Cheddar cheese samples. ......................................................................... 192

Table 24 Sensory scores of body and texture of Cheddar Cheese samples,
based on ADSA guidelines. ....................................................................... 193

Table 25 Difference-from-control sensory scores in flavor1 of Cheddar cheese
samples. .................................................................. , .................................. 195

Table 26 Flavor sensory scores of Cheddar cheese samples based on ADSA
guidelines ................................................................................................... 196

Table 27 Volatiles identified1 in the headspace (60°C, 15 minutes) of Cheddar
Cheese. ...................................................................................................... 197

Table 28 Sensory scores of light-oxidized 2% milk based on ADSA guidelines.
Scores were given by an 8-member trained panel ..................................... 243

Table 29 ClE L* a* b* values of Cheddar Cheese samples exposed to
fluorescent light (2000 Ix) for 0 and 2 weeks. ............................................ 246

xi

LIST OF FIGURES

Figure 1 Mechanisms of photosensitized oxidation catalyzed by riboflavin ......... 6
Figure 2 Mechanism of methionine reacting with singlet oxygen (‘02). .............. 8
Figure 3 Lipid oxidation initiated by free radicals or singlet oxygen (‘02), which
is generated in the presence of riboflavin. ..................................................... 9
Figure 4 Absorption spectrum of riboflavin at different wavelength. .................. 13
Figure 5 Absorption spectra of (a) vitamin A and (b) vitamin C. ........................ 14
Figure 6 Emission spectra of (a) sunlight, (b) Philips 83/36 W white, (0) Philips
16l40 W yellow, (d) Philips 33/40 W white. .................................................. 17
Figure 7 Energy output for a typical supermarket white lamp. ......................... .18
Figure 8 Light transmission through various packaging materials. .................... 30

Figure 9 Absorption spectra of the major naturally occurring colorants of cheeses
at concentrations found in milk. ................................................................... 39

Figure 10 Basic diagram showing the analogy between biological and artificial

noses. .......................................................................................................... 42
Figure 1 1 Schematic diagrams of five different kinds of sensors. ..................... 47
Figure 12 Sensor responses of (a) an ideal first order odor sensor responding to

a step odor input (b) MOS sensors in this study (R: resistance). ................. 55
Figure 13 Example of PCA (a) without mean centering and (b) after mean

centering. ..................................................................................................... 57
Figure 14 Classification scheme of the multivariate pattern analysis techniques

applied to the electronic nose data. ............................................................. 59
Figure 15 Solid phase microextraction (SPME) to concentrate headspace

volatiles: (a) extraction; (b) desorption. ........................................................ 69
Figure 16 Effect of time on amount of analyte absorbed. .................................. 70
Figure 17 An example of hierarchical clustering analysis (HCA). ...................... 76
Figure 18 An example of principle component analysis (PCA). ........................ 80

xii

Figure 19 Decision boundaries defined based on the discriminant functions. ...83

Figure 20 Examples of canonical discriminant analysis. ................................... 86
Figure 21 Examples of k-nearest neighbor (k-NN). ........................................... 88
Figure 22 The indirect modeling concept of partial least squares (PLS) approach.
..................................................................................................................... 90
Figure 23 Multiplayer perceptron (MLP) structure. (a) A neuron, the basic neural
unit; (b) a multilayer perceptron. .................................................................. 93
Figure 24 Experimental flowchart to investigate the light-oxidized off-flavors in
2% milk, using sensory evaluation and instrumental analyses. ................... 95
Figure 25 Packaging materials used for milk and/or water ................................ 96

Figure 26 Experimental flowchart to investigate packaging off-flavors in 2% milk,
using sensory evaluation (triangle tests and flavor rating based on ADSA
guidelines) and instrumental analysis (electronic nose) ............................... 97

Figure 27 Experimental flowchart to investigate the light—oxidized and packaging
off-flavors in 2% milk, using sensory evaluation and instrumental analysis
(electronic nose). ......................................................................................... 99

Figure 28 The arrangement of the Cheddar cheese samples exposed to
fluorescent light (2000 Ix) at 5°C for 0, 2, 4, or 6 weeks ............................. 102

Figure 29 Experimental flowchart to investigate the light-induced deterioration of
Cheddar cheese in color, body/texture, and flavor, using sensory evaluation
and instrumental analyses. ........................................................................ 103

Figure 30 The electronic nose system (Fox 3000, Alpha-MOS) ...................... 109

Figure 31 Schematic diagram of (a) 45°l0° geometry of the spectrophotometer
(b) the CIE L* a* b* color space. ................................................................ 114

Figure 32 The feed-forward backpropagation network with 12 input nodes,
which correspond to the mean-centered 12 sensor responses), and one
output node. ............................................................................................... 1 18

Figure 33 Training parameters of the feed-forward backpropagation network.
................................................................................................................... 1 18

Figure 34 Flavor sensory scores based on ADSA guidelines of 2% milk in glass
bottles exposed to 0, 2, 4, 8, 12, 24 or 48 hours of fluorescent light (1000 Ix,
5°C) ............................................................................................................ 123

xiii

Figure 35 Area responses for (a) pentanal, (b) hexanal and (c) dimethyl disulfide
sampling using CARIPDMS, or DVB/PDMS fiber (d, e, f, respectively). at
different sampling temperatures. ............................................................... 125

Figure 36 The amount of (a) pentanal (b) hexanal, and (c) dimethyl disulfide in
2% milk with and without light exposure for 0 to 48 hours. ........................ 130

Figure 37 Sensor responses of the electronic nose to 95°C headspace of 2%
milk (a) without (b) with 48 hours of light exposure at 5°C. ........................ 132

Figure 38 (a) HCA and (b) PCA of the sensor responses for 45°C headspace of
2% milk exposed to 0, 2, 4, 8, 12, 24, 36, or 48 hours of light at 5°C. ....... 133

Figure 39 (a) HCA and (b) PCA of the sensor responses for 70°C headspace of
2% milk exposed to 0, 2, 4, 8, 12, 24, 36, or 48 hours of light at 5°C. ....... 134

Figure 40 (a) HCA and (b) PCA of the sensor responses for 95°C headspace of
2% milk exposed to 0, 2, 4, 8, 12, 24, 36, or 48 hours of light at 5°C. ....... 135

Figure 41 Classification rates for discrimination methods applied to the sensor
responses using a headspace temperature of 45°C for 2% milk exposed to 0,
2, 4, 8, 12, 24, 36, or 48 hours of light, using LDA, QDA, and k-NN for k = 1,
2, 3, and 4. ................................................................................................. 137

Figure 42 Classification rates for discrimination methods applied to the sensor
responses using a headspace temperature of 70°C for 2% milk exposed to 0,
2, 4, 8, 12, 24, 36, or 48 hours of light, using LDA, QDA, and k-NN for k = 1,
2, 3, and 4. ................................................................................................. 138

Figure 43 Classification rates for discrimination methods applied to the sensor
responses using a headspace temperature of 95°C for 2% milk exposed to 0,
2, 4, 8, 12, 24, 36, or 48 hours of light, using LDA, QDA, and k-NN for k = 1,
2, and 3 ...................................................................................................... 138

Figure 44 Canonical discriminant scatter plots of the (a) training (b) test data
sets. Headspace of 2% milk samples exposed to 0, 2, 4, 8, 12, 24, 36, or 48
hours of light was generated at 45°C. ........................................................ 140

Figure 45 Canonical discriminant scatter plots of the (a) training (b) test data
sets. Headspace of 2% milk samples exposed to 0, 2, 4, 8, 12, 24, 36, or 48
hours of light was generated at 70°C. ........................................................ 141

Figure 46 Canonical discriminant scatter plots of the (a) training (b) test data
sets. Headspace of 2% milk samples exposed to 0, 2, 4, 8, 12, 24, 36, or 48
hours of light was generated at 95°C. ........................................................ 142

Figure 47 Classification rates of the defined threshold of harmful light exposure
for discrimination methods applied to the sensor responses using a

xiv

headspace temperature of 45°C using LDA, QDA, and k-NN for k = 1, 2, 3,
and 4 .......................................................................................................... 144

Figure 48 Classification rates of the defined threshold of harmful light exposure
for discrimination methods applied to the sensor responses using a
headspace temperature of 70°C using LDA, QDA, and k-NN for k = 1, 2, 3,
and 4 .......................................................................................................... 145

Figure 49 Classification rates of the defined threshold of harmful light exposure
for discrimination methods applied to the sensor responses using a
headspace temperature of 95°C using LDA, QDA, and k-NN for k = 1, 2, 3.
................................................................................................................... 146

Figure 50 PLS predicted versus actual flavor scores of 2% milk exposed to 0, 2,
4, 8, 12, 24, 36, or 48 hours of light, based on levels of pentanal, hexanal
and dimethyl disulfide quantified using SPME-GC. Models were applied to (a)
training data and (b) test data. ................................................................... 148

Figure 51 MLP predicted versus actual flavor scores of 2% milk exposed to 0, 2,
4, 8, 12, 24, 36, or 48 hours of light, based on levels of pentanal, hexanal
and dimethyl disulfide quantified using SPME-GC ............ . ......................... 1 49

Figure 52 PLS predicted versus actual flavor scores of 2% milk exposed to 0, 2,
4, 8, 12, 24, 36, or 48 hours of light, based on the sensor responses using a
headspace temperature of 45°C. ............................................................... 151

Figure 53 PLS predicted versus actual flavor scores of 2% milk exposed to 0, 2,
4, 8, 12, 24, 36, or 48 hours of light, based on the sensor responses using a
headspace temperature of 70°C. ............................................................... 152

Figure 54 PLS predicted versus actual flavor scores of 2% milk exposed to 0, 2,
4, 8, 12, 24, 36, or 48 hours of light, based on the sensor responses using a
headspace temperature of 95°C. ............................................................... 153

Figure 55 MLP predicted versus actual flavor scores of 2% milk exposed to 0, 2,
4, 8, 12, 24, 36, or 48 hours of light, based on the sensor responses using a
headspace temperature of 45°C. ............................................................... 154

Figure 56 MLP predicted versus actual flavor scores of 2% milk exposed to 0, 2,
4, 8, 12, 24, 36, or 48 hours of light, based on the sensor responses using a
headspace temperature of 70°C. ............................................................... 155

Figure 57 MLP predicted versus actual flavor scores of 2% milk exposed to 0, 2,
4, 8, 12, 24, 36, or 48 hours of light, based on the sensor responses using a
headspace temperature of 95°C. ............................................................... 156

CHAPTER 1
INTRODUCTION

Exposure to light can result in changes in sensory and nutritional qualities
of foods. Photosensitized oxidation in the presence of riboflavin is believed to be
mainly responsible for the quality deterioration of milk and other dairy products
due to fluorescent light exposure on dairy shelves in retail stores [1-5].
Depending on the lighting conditions, eg. exposure duration, intensity and
wavelength, and barrier properties of packaging materials, the occurrence of
light-oxidized off-flavors in milk can take place in as soon as 15 minutes [6] to 12
hours [7, 8]. Turnover times of fluid milk average somewhere between 8 hours [6,
9] and 2-3 days [10]. Light-induced discoloration and oxidized off-flavors may
occur at the surface of cheeses, since cheeses are often wrapped in transparent
packaging and displayed in small pieces with large surface areas exposed to
light.

Potential migrants in milk packaging materials, such as oxidative
hydrocarbons from processed HDPE, PET or PE-coated paperboard [11-13],
may cause a defect referred to “packaging off-flavors” in milk. On the other hand,
packaging light and oxygen barriers reduce light oxidation by minimizing the
impact of fluorescent light in retail display and headspace oxygen content inside
the package, respectively.

An electronic nose is an instrument which comprises an array of electronic

chemical sensors with partial specificity and an appropriate pattern recognition

system, capable of recognizing simple or complex odors [14]. An electronic nose
analyzes all substances in gaseous samples as a whole, as an analogy to the
human olfactory system. It is more of a complementary approach than a
substitute for reference methods that use sensory panels [15]. An electronic nose
can be used as an automated discriminatory analysis tool for odors and flavors in
foods, and has the potential for applications such as quality control, process
control, and off-flavor detection [16].

This study was done to evaluate the light-oxidized and/or packaging off-
flavors in milk and Cheddar cheese using sensory evaluation, solid-phase
microextraction coupled with gas chromatography (SPME-GC), and an electronic
nose (FOX 3000, AlphaMOS) equipped with 12 metal oxide semiconductor (MOS)
sensors. In addition to the flavor changes, the light-induced discoloration of
Cheddar cheese was quantified using sensory evaluation and instrumental color

measurement.

Significance

Electronic noses, which can be automated and require less extensive
operating and preparatory measures, have the potential to be a complementary
approach for sensory evaluation. This study used a commercial electronic nose
system as the discriminatory analysis tool for light oxidized off-flavors in milk and
Cheddar cheese, and to determine a correlation between the sensory quality and
the electronic nose responses. The effect of current packaging materials as both

light barriers and sources of potential migrants was also investigated.

Hypothesis
The electronic nose equipped with 12 MOS sensors can perform
effectively in discriminating and predicting the sensory quality of the light-oxidized

and packaging off-flavors in milk, and light-oxidized off-flavors in Cheddar cheese.

Objectives

1. To discriminate light-oxidized off-flavors in milk using sensory evaluation
and instrumental headspace analyses, including the electronic nose and
SPME-GC.

2. To evaluate the quality of milk packaged in different packaging materials
of differing light barrier properties and having different potential packaging
off-flavor problems.

3. To discriminate light-induced discoloration and off-flavors in Cheddar
cheese using sensory evaluation and instrumental analySes, including
color measurements, electronic nose and SPME-GC.

4. To correlate the results of sensory evaluation and instrumental analyses

using multivariate statistical techniques.

CHAPTER 2

REVIEW OF LITERATURE

2.1. light-induced oxidation of milk

Light exposure can have a deteriorative effect on both sensory and
nutritional qualities of foods, including milk and other dairy products. Two types of
light-induced off-flavors in milk have been identified: a “burnt-feather” off-flavor
from protein oxidation usually appears early during light exposure but dissipates
in a few days, and a “cardboardy" off-flavor from lipid oxidation develops with
more prolonged light exposure and does not dissipate. Other nutrients, such as
vitamins, may also decompose in oxidative reactions.

Light absorption can directly initiate the oxidation; however, milk proteins
and lipids absorb very limited amounts of ultraviolet (UV) light and do not absorb
visible light [3]. Lipids such as unsaturated fatty acids are susceptible to
photolytic free radical autooxidation, which involves direct formation of free
radicals when exposed to high energy light such as UV. Most retail stores use
fluorescent light which is designed to generate only a very limited photon flux of
such detrimental wavelengths [5]. Similarly, aromatic amino acids, i.e. tryptophan,
tyrosine and phenylalanine, can absorb UV light below 310 nm, which can result
in direct photochemical changes, but it is not the main contributor responsible for
oxidative byproducts.

A photosensitizer, such as riboflavin, will absorb light energy and cause

photosensitized oxidation and result in the generation of oxidative active

compounds, which can cause further oxidative decomposition of milk proteins,

lipids and vitamins.

2.1 .1 . Photosensitized oxidation

Photosensitized oxidation is a light—induced oxidation which occurs in the
presence of photosensitizers such as chlorophyll and riboflavin, and generates
free radicals or singlet oxygen via Type I or Type II mechanisms, respectively
(Figure 1). The conjugated double bond structure of riboflavin readily absorbs
visible light energy (hv) [17] and forms an excited singlet state, 1Rib*, which by
intersystem crossing forms a triplet state, 3Rib*. Triplet riboflavin subsequently
generates substrate radicals and/or superoxide anions (Type I), or yields singlet
oxygen (Type II). Competition between substrate (Sub), i.e. proteins or lipids, and
oxygen (302) for triplet riboflavin (3Rib*) determines the predominant reaction
path. The type I reaction is predominant under conditions of high reactivity and/or
concentration of substrates, low oxygen concentration in the system, or Short
singlet oxygen lifetime. Milk has low oxygen solubility and, therefore, the type I
path may be predominant, compared to a lipid-based system such as olive oil
containing chlorophyll as the photosensitizer. However, studies using Electron
Spin Resonance Spectroscopy (ESR) have found evidence of singlet oxygen
formation in light-oxidized skim milk [18].

The activation energy of photochemical reactions is often much lower than
that of non-photochemical reactions. The activation energy of oxygen in the

singlet state (‘02) and the triplet state (302) is only 92 and 104 kJ/mol higher than

the ground state, respectively. The energy associated with any region in the
visible spectrum is sufficient (eg. the energy of a photon at 800 nm is 149 kJ/mol)
to produce singlet oxygen in the presence of a photosensitizer such as riboflavin
[4]. The temperature dependency of photosensitized oxidation is usually

negligible in the temperature range of food storage due to its low activation
energy, although temperature does affect the decomposition processes initiated

by singlet oxygen [5].

IN
02" Rib \<

'0.
:\2 Time II
‘Rlb'

’02 ’Rlb' J

Figure 1 Mechanisms of photosensitized oxidation catalyzed by riboflavin, the
photosensitizer. Riboflavin (Rib) absorbs a photon (hv) and forms an excited
singlet state, 1Rib"‘, which by intersystem crossing forms a triplet state, 3Rib*.
Triplet riboflavin subsequently generates substrate radicals and/or superoxide
anions (Type I), or yields Singlet oxygen (Type II) (modified from [3], Figure 3).

2.1.1.1. Degradation of milk proteins

Reaction of singlet oxygen with sulfur-containing amino acids, eg.
methionine, results in the “sunlight flavor” or “activated flavor", which is described
as “burnt feather”. The oxidative degradation of methionine results in the
formation of low molecular weight sulfur-containing volatiles such as methional or
dimethyldisulfide [19, 20] (Figure 2). Methional is relatively unstable and breaks
down into more stable components, including mercaptans, sulfides, and
disulfides [1, 21]. In skim milk an increased level of dimethyldisulfide was found
with an increased level of sensory off-flavor scores [20]. The formation of
dimethyldisulfide requires only Cleavage of the methionine side chain and thus it
is more likely to occur than methional formation, which depends on cleavage of
the protein backbone [3, 5].

Several studies have shown the effects of light exposure on amino acids
and milk proteins, such as photoaggregation of whey proteins [22] and loss of
amino acids, eg. methionine, cysteine and tryptophan [23-25]. However, other
studies designed to evaluate the level of amino acid destruction in commercially
packaged milk under fluorescent light exposure have found insignificant changes
in the aminoacid content. Dimick (1973) [7] exposed homogenized milk in half-
gallon containers (fiberboard, blow-molded plastic, and glass) to 100 ft—c (1076 Ix)
of fluorescent light for 144 hours, which resulted in no significant difference in the
amino acid concentration when compared to the unexposed control. Hedrick and

Glass (1975) [26] drew a similar conclusion by comparing the amino acid content

of 17 amino acid in milk packaged in paperboard and plastic gallon containers

subjected to 150 ft-c (1614 IX) of fluorescent light.

 

0‘

‘ H

(a) 3’, Hacsscui (b)
“3 I
Ir '8“ " °
2 4
H. ll’
O H

Figure 2 Mechanism of methionine reacting with singlet oxygen (‘02): (a) The
formation of methional depends on cleavage of the protein backbone; (b)
dimethyldisulfide is formed by Cleavage of the methionine side chain (modified

from [3], Figure 1).

2.1.1.2. Oxidation of milk lipids

The “cardboardy” or “metallic” off-flavor is caused by oxidation of
unsaturated fatty acids in milk lipids, particularly phospholipids [27], and is
initiated by free radicals or singlet oxygen (Figure 3).

Inltation propagation tormlnafion
0:
R
I.
— L; , non—radical
LH '——"¥_"___7_, L Ll ‘T compounds
OH _ LH L
EM? 4» OH
H203 LOOH M"

Matt-OH-
LO-
.1..
i

Secondary
lipid oxidation
products

Figure 3 Lipid oxidation initiated by free radicals (02" or R' ) or singlet oxygen
( 02), which Is generated In the presence of riboflavin. Autooxidation involving
cleavages of lipid hydroperoxides (LOOH) depends on the presence of catalytic
metal Ions (M2 ) or enzymes such as peroxidases. The sign“= ”indicates
reactions affected by primary antioxidants (from [3], Figure 2).

Secondary lipid oxidation products are formed when lipid hydroperoxides
decompose to alkanes, alkenes, aldehydes, alcohols, ketones, esters, and acids.
Due to their low sensory thresholds, the unsaturated ,aldehydes and ketones are
usually considered the primary sources of oxidized off-flavors. Odor-active
compounds such as hexanal and pentanal have been found to increase in milk
on exposure to fluorescent light, where hexanal is the predominant lipid oxidation

byproduct in light-oxidized milk [28].

2.1 .1 .3. Vitamin destruction

Riboflavin (0.41 mg riboflavin in 8 oz. of milk, [29]) is consumed rapidly
when exposed to light, and the two main photosensitized oxidative pathways are
cyclic (Figure 1). The degradation is probably due to attack by some of the
activated oxygen species generated by deactivation of the excited states of
riboflavin, including singlet oxygen, superoxide, peroxides, and-the hydroxyl
radical [3]. Photolysis of riboflavin in aqueous solutions is proportional to time of
light exposure and light intensity [30]. The decrease in riboflavin content has
been used to indicate the extent of riboflavin-sensitized light oxidation in milk [31-
33] and other dairy products [34-36], and the effectiveness of the package used
to protect the milk [37, 38] or other dairy products [39, 40] from light oxidation.

Besides riboflavin (vitamin 32), other nutrients such as vitamin A and its
precursor carotenoids, other B vitamins, vitamin C (ascorbic acid), and vitamin D
are also light sensitive. The oxidative reactions affecting these nutrients can be
affected by the presence of riboflavin [1, 3], especially by the singlet oxygen

generated in photosensitized reactions (Figure 1).

10

Naturally occurring vitamin A and its precursors (retinol and retinyl esters)
in milk (about 10-60 ugl100mL) were found to be relatively more stable to light
exposure than riboflavin [1]. However, a significant reduction in vitamin A (retinyl
palmitate) was caused by light exposure in supplemented low fat and nonfat
milks containing vitamin A fortification levels between 2000 and 3000 IU per
quart (Federal Regulations 21 CFR 131.135 and 21 CFR 131.143 for low fat and
nonfat milk, respectively) [41]. It was reported that 50% loss of total vitamin A
occurred in milk packed in HDPE containers exposed to 200 ft-c (2152 Ix)
fluorescent light for 3 hours [42]. A more recent study showed that even a brief,
moderate light exposure (2000 lx for 2 hours) can significantly reduce the vitamin
A content in reduced fat (2%) and skim milk in HDPE gallon containers [41]. The
photostability of fortified vitamin A is affected by the carrier used and the
chemical form of the vitamin [43, 44].

The vitamin C (ascorbic acid) content is about 2 to 5 mgi100g in fresh milk
[45]. Its degradation was found to be proportional to the amount of light
transmitted into the container in the presence of riboflavin. The stability of
ascorbic acid is maintained in the absence of riboflavin [46, 47]. Ascorbic acid,
acting as a singlet oxygen quencher, has been reported to effectively reduce the
level of dimethyl disulfide and other oxidative volatiles generated in
photosensitized oxidation in the presence of riboflavin [20, 48-50].

Light exposure is essential to change provitamin D into vitamin D and its
related compounds [51]. However, vitamin D can be also be destroyed by

riboflavin-photosensitized oxidation. Light accelerates the loss of vitamin D in

11

skim milk containing riboflavin, but that decomposition rate was not affected by
light in a model system in the absence of riboflavin [52]. Similar results were
observed in two other studies [51, 53], though oxidation of vitamin D was not
observed in the systems without riboflavin under light exposure, nor in those with

riboflavin but stored in the dark.

2.1.2. Wavelength dependent impact of light

Light absorption and light stability (i.e. quantum yield) of photosensitizers
and other light sensitive components are wavelength dependent. Light sources
with distinct spectral distributions can have very different impacts. Yellow colored
lighting, which filters out the light below 500 nm, is suggested for dairy retail
displays [54, 55] to reduce the photosensitized oxidation in the presence of

riboflavin.

2.1 .2.1. Light absorption

Only absorbed light can initiate Chemical reactions [3]. Riboflavin as the
photosensitizer in milk and dairy products has three absorption bands as shown
in Figure 4. The third band in the visible region (blue to green, broad maximum at
430-460 nm) is the main band responsible for the photosensitized oxidation in

milk and dairy products [4].

12

10

 

 

 

 

1st absor- 2nd absor- 3rd visible
ption band ption band absorption band
8 — (UV 8) (UV A) (blue-green)
H .
c A
.9 ‘-
0 '—
'- O
s . -
° N
I:
.3 5
9‘ I» 4 T
O o
5 ‘—
2 ._
0 l l l I I
230 300 400 500
Wavelength (nm)

Figure 4 Absorption spectrum of riboflavin at different wavelength (from [4],
Figure 3).

Other photo-sensitive components in milk such as vitamin A (retinyl
palmitate) and vitamin C (ascorbic acid) have strong absorption bands at
maximums of 325 and 270 nm, respectively (Figure 5). Protection from light at
these wavelengths (UV range < 380 nm) may protect vitamin A and C from
primary photooxidation; however, photosensitized oxidation in the presence of
riboflavin can still cause losses of vitamin A and C in most of the visible light

region [4], especially the light below 500 nm [54, 55].

13

 

 

 

 

 

 

 

 

 

 

 

 

 

100
90 - I a
90 ' (a)V'ItarninA(Retinyl Palmitate) in .
c 70 _ Dioxane vs.'Dioxane ]
,9 (Concentration 1.8 x 10'2 M)
.3 so - -
5
a 50 ~ ~
:2 40 t ~
30 l. J
20 - -
10 » ' ~
0 1 1 l l I
200 300 400 500 600 700 800
Wavelength (nm) .
100
90 . I _
30 _\ (b) Vltamin C (Ascorbic Acid) in ..
Water vs. Water _2 _
g 70 - (Concentration 1.85x10 gll) a
.8 so _ .
E
a 50 L .
3'2 40 . .
30 "’ «I
20 - U .
10 L .,
0 t . . . .
200 300 400 500 600 700 800
Wavelength (nm)

Figure 5 Absorption spectra of (a) vitamin A and (b) vitamin C (modified from
[37], Figure 3).

14

2.1.2.2. Quantum yield

The quantum yield can be explained as the light stability (or sensitivity) of
molecules at varying wavelengths. This parameter has been suggested as a
replacement of rate constants for light-induced changes in foods [3]. It is defined
as the number of molecules converted by one photon, as shown below:

(b = molecules reacted = AC,-
photons absorbed by reacting compound Q,-

 

where AC,- is the change in concentration of the specific compound during a
period of time resulting from the number of photons absorbed by the compound,
0;. during the sample period. Quantum yield ranges between zero and one, with

the exception of chain reactions where 49 may be larger than one. Combining the
absorption Spectra with light intensities at different wavelengths allows the ‘
calculation of the number of photons absorbed [3].

The apparent quantum yield is based on total light absorption and may be
used for comparison with photo-induced processes in comparable systems, as
shown below:

(p = moles reacted = ACINA’"
app photons absorbed by food matrices Qtotal

 

where AC; is the change in concentration of the reacting compound determined
by a specific Chemical analysis such as gas chromatography, N A is Avogadro’s
number, m is the mass of the illuminated sample of food matrix, and Qtotal is the

total number of photons absorbed by the system. Assuming no light transmission

and minimum reflection from the food matrix, the apparent quantum yields were

15

calculated for the secondary oxidation volatiles generated in light-induced

oxidation of Havarti cheese [36].

2.1.2.3. Light sources

Different light sources, eg. sunlight or fluorescent light, can have very
different emitting spectra (Figure 6), and therefore can have very different
impacts on light sensitive food ingredients. The light sources with high light
emission at the critical wavelength ranges (considering both the light absorption
and quantum yields) are expected to cause more product damage. Since light
oxidized off-flavors in milk and dairy products are mainly generated by riboflavin
photosensitized oxidation, light sources with lower emission below 500 nm are
believed to be less harmful, since the absorption band of riboflavin at 430-460
nm is believed to be the main band responsible for photosensitized oxidation [4].
For instance, warm white fluorescent lights generally have less impact than cool
white lights.

Alternatively, filtering out the light in the critical wavelength ranges can
reduce the impact of light. Use of pigmented packaging materials or putting a
colored filter directly on the light source can reduce the light transmission in
specified wavelength ranges. To protect milk and other dairy products from
photosensitized oxidation, it is critical to filter out the visible light below 500 nm,
particularly at the harmful blue-violet visible range of 400-500 nm, by colored or

opaque light barriers (Figure 7, [56]). IDF (lntemational Dairy Federation)

16

recommends that the maximum permissible light transmission through packaging

material should be 8% at 500 nm and 2% at 400 nm [57, 58].

 

nm")

. (a)

100-

 

 

 

Normalized lmdiance (pW ~cm

I i
500
Wavelength (nm)

l
700

 

 

100
(b)

 

 

 

 

 

Normalized Irradiance (uW - cm'2 ~nm")

 

Wavelength (nm)

Normalized Irradiance (uW-crn'z-nm'1)

-1

Normalized lrradiance (pW . cm'2 ~nm

 

 

 

 

 

 

 

 

100
(G)
A
50 ~
0 I I A I I
300 500 700
Wavelength (nm)
100
(d)
.J
50 ~
0 I L I l I I
300 500 700
Wavelength (nm)

Figure 6 Emission spectra of (a) sunlight. (b) Philips 83/36 W white, (c) Philips
16/40 W yellow, (d) Philips 33/40 W white (modified from [2], Figure 3).

17

 

 

A light source or packaging material with reduced light transmission below
500 nm has a yellow appearance, since relatively more yellow-orange-red light
(i.e. above 500 nm) is perceived by human eyes (Figure 7). Hansen et al. (1975)
[55] studied the effects of colored lamps and lamp filters on milk packaged in
HDPE containers, and found that yellow lamps or yellow/green filters protected
milk from light-induced oxidation, where the off-flavor development was delayed

from 5-7 hours (cool-white lamp) to 30-40 hours (yellow lamp).

%Relative Energy Output
100% ' '

 
  
    

80%

Non-harmful range

I
I
60%:
I

40%
20%

 

 

380420 460500540 580620 660 700

Violet Blue Green Orange-red

Light Light Light Light
Wavelength (nm)

Figure 7 Energy output for a typical supermarket white lamp (modified from [56],
Figure 1).

2.1.3 Occurrences of light-oxidized off-flavors in milk

Light-oxidized off-flavors in milk have been widely studied [2-4, 27, 59].
Since the 1960’s, many studies have been conducted using various light
exposure conditions, eg. light sources with different spectral emissions, light
intensities (distance-dependent), exposure times, and storage temperatures. Milk
composition (e.g. fat or prooxidant/antioxidant contents) and packaging
size/material also significantly affect the development of light-induced off-flavors.
In addition, sensory scores and measurement of headspace volatiles are often
used to define the occurrence of light-oxidized off-flavors. Table 1 lists the
internal and external factors affecting the photosensitivity of milk and dairy

products to visible light.

Table 1 lntemal and external factors affecting the photosensitivity of milk and
dairy products to visible light (from [4], Table 2).

 

Factors Photopreventive action Photosensitizing action

 

Intrinsic factors (generally properties of the product itself)

Structure, texture compact, scattering translucent
Own coloration dark colorless, bright
Content of reducing substancesl (pro)oxidants
antioxidants
- vitamins C (ascorbic acid) 32 (riboflavin)
- unsaturated fatty acids - peroxide formation
- fat photodiffusion degree of unsaturation
- heavy metals - catalysis (e.g. CU”)
- dissolved Oz reducing microflora synergy with light
- free amino acids - formation of methional
(proteolysis)

 

19

Table 1 (continued)

 

 

Factors Photopreventive action Photosensitizing action
Heat treatments: harsh gentle
pasteurization, UHT formation of -SH formation of -S-S-
treatment, etc. (= reducing) (= oxidants)
Mechanical treatment:
homogenization ? ?

Extrinsic factors (to be considered)

Source of light

spectrum UV (A < 350 nm)

source: day light

source: artificial light

light (= intensity)

duration of the exposure
Geometry

distance from the source

presentation, disposition
of products
possible auxiliary means

Packaging

light transmission of
packaging

- with coloring

- with pigmentation
material thickness
oxygen permeability

Storage

effect of temperature on:
- 02 solubility
- activation energy
shelf life

“cut-off” (material)
“white warm”
weak
shon

great
closely packed

framed crate, baskets

weak/zero

brown-red
turbid/light scattering
thick

weak/zero

room temperature
low
shod

high energy available
“white cold”

strong

long

shon
dispersed

high

blue-green
translucent
(ultra)thin
high

low
room temperature
long

 

20

According to the Plastic Bottle Institute, approximately one half of the fluid
milk products in plastic containers remain in the dairy case (and therefore maybe
exposed to light) for at least 8 hours [6, 9]. A New Zealand study revealed that
the fluid milk turnover time may be as much as 2-3 days, resulting in severe light
exposure [10]. Light intensity depends on the light source as well as distance
from the light source, with ranges reported from 80 to 12000 Ix in dairy retail
display cabinets [5]. Measurements took place in dairy cases bearing products in
5 supermarkets and 3 convenience stores in the area around Ithaca, New York,
with light intensities ranging from 750 to 6460 Ix [6]. Different packaging materials
have different light barrier properties, and different container sizes have different
surface—to-volume ratios, which results in various light exposure conditions for
the fluid milk.

Dunkley et al. (1963) [54] reported that the minimum light exposure
conditions to develop a detectable “light flavor” for milk in clear quart bottles (946
ml), were one half inch from a cool white lamp, for 20 minutes. Satter and
deMan (1973) [60] exposed homogenized whole milk in returnable plastic 3 quart
jugs (2.84 L) to 100 ft-C (1076 IX) and 200 ft-c (2152 Ix) of 40W cool-white
fluorescent light, which resulted in significant off-flavors in 12 and 3 hours,
respectively, as determined by 12-18 trained panelists using a duo-trio test.
Dimick (1973) [7] and Coleman et al. (1976) [8] tested homogenized milk in half-
gallon (1.89 L) plastic containers and reported that the milk had a sharply
decreased flavor score after 12 hours of light exposure (100 ft-c, i.e. 1076 Ix) at a

refrigerated temperature. Hansen et al. (1975) [55] reported light oxidized off-

21

flavors in milk stored in HDPE containers and exposed to 200 ft-c (2152 Ix) of
cool-white fluorescent light at 33°C. The light oxidized off-flavor was first
perceived by a trained panel at 2-4 hours, and a more intense off-flavor was
developed at 5—7 hours, which was expected to be detectable by average
consumers.

Chapman et al. (2002) [6] exposed 2% milk in HDPE gallon (3.785 L)
containers to 2000 Ix of fluorescent light at 6°C, which resulted in a significantly
detectable quality change for a 10-member trained panel in 30 minutes, and for a
94-member consumer panel in 2 hours. The minimum light exposure times in
order for the quality change to be noticed by 50% of the panelists, determined by
interpolation, were 15 minutes and 54 minutes for trained and consumer panels,
respectively. The tests were performed using a semi-ascending paired difference
method [61] with an 11-point intensity line scale of difference from control. In
another study [62], it was shown that half of the teen and adult consumers could
detect an off-flavor in less than 2 hours of light exposure. The light-oxidized milk

was objectionable to teen consumers.

22

2.2. Effects of milk packaging

Storage of milk in different packaging materials may affect milk quality by
impacting the development of packaging and light-oxidized off-flavors. Packaging
off-flavors from high density polyethylene (HDPE), polyethylene terephthalate
(PET), and PE-coated paper cartons has been reported as “plastic”, “bitter”,
“oxidative”, or “lacks freshness”. On the other hand, packaging materials with
good light barrier properties minimize the impact of fluorescent lighting in retail

display, and reduce light-oxidized off-flavors.

2.2.1. Off-flavors from milk packaging materials

Potential migrants in milk packaging materials may cause a defect
referred to as “packaging off-flavors” in beverages such as milk. Migrants may
include oxidative hydrocarbons from processed HDPE, PET and plastic coated
paper cartons, or solvents used during lamination, coating or printing processes.
Shelf-life needed and storage temperatures of various milk products are also
critical factors. For instance, fresh milk is expected to be stored at refrigerated
temperature for less than two weeks, while UHT aseptic milk may have a shelf-
Iife greater than six months at room temperature.

Studies using gas chromatography (GC) have been widely conducted to
identify the problematic compounds. An “indicator“ compound, eg. hexanal, is
used to “measure” the extent of oxidative degradation and is often correlated to
the off-flavor intensity. In order to precisely reveal the actual consumer impact of

the packaging off-flavor, sensory evaluation is essential. It has been suggested

23

that sensory evaluation is the most reliable way to determine off-flavors

originating from packaging [63].

2.2.1 .1 . Polyethylene (PE)

Polyethylene, especially high-density polyethylene (HDPE), dominates the
milk packaging business because it is low-cost, durable and lightweight [64]. Off-
flavors in milk packaged in polyethylene containers have been described as
“plastic”, “off-flavor", “bitter", and “oxidative”. It was found that the off-flavor
compounds were already present in the granular PE [11, 65, 66], and the
converting and extrusion processes may further increase the amount of off-flavor
volatiles [67]. Oxidative hydrocarbons generated at high temperature and/or
substantial shear stress during processing, may be present at the surface of
polyethylene pouches or bottles [11, 65]. Those low molecular weight byproducts
from oxidation or thermal degradation may migrate to food syStems, such as
water or milk, and result in off-flavors. Addition of antioxidants, e.g. lrganox 1010,
butylated hydroxytoluene (BHT) and d-tocopherol, are often used to reduce the
oxidation and thermal degradation of polyethylene [68-70]. Other potential
migrants (but which may not be significant) [67] from PE containers include
antioxidants, plasticizers, solvents, and other additives used in making or
converting polyethylene.

Although alkanes and alkenes make up a significant portion of the thermal
degradation fragments of polyethylene [71], aldehydes and ketones are of

particular concern, due to their low odor thresholds and high polarity, which make

24

them more likely to desorb from a non-polar polymer like polyethylene [71, 72].
Using a gas chromatography/olfactometry technique (GCO), Bravo (1992) [11]
found that 14 odor-active compounds were present, but only eight of them were
identified, i.e. hexanal, l-hepten-3-one, 1-octen-3-one, octanal, 1-nonen-3-one,
nonanal, trans-2-nonenal, and diacetyl. A “wax-like” descriptor was assigned to
the overall odor, while the individual components were fruity, herbaceous, rancid,
metallic, waxy, pungent, or orangy. The study confirmed that o-unsaturated
aldehydes and ketones are responsible for much of the off-odor associated with
thermally oxidized polyethylene.

Polyethylene used for milk packaging is formed as mainly blow-molded
high-density polyethylene (HDPE) jugs, low-density polyethylene (LDPE)
pouches, and the inner LDPE seal layer of paper cartons. Ho et al. (1995) [71]
evaluated the packaging off-flavors in drinking water contained in blow molded
HDPE bottles containing various antioxidants (vitamin E, lrganox 1010, and BHT),
using sensory evaluation and purge-and-trap GC-MS. Forty-seven volatiles,
including n-alkanes, 1-alkenes, aldehydes, ketones, phenolics, olefins, and
paraffins from C6 to C18, were identified. The acceptability of the water was
correlated inversely with intensity of odor and taste sensory scores, as well as
the concentrations of aldehydes and ketones present. Maneesin (2001) [73]
reported that storing water in HDPE containers (processed at 204°C with addition
of lrganox 1010) for three months imparted significant off-flavors, using nonanal
content in water as the off-flavor indicator, along with different-from—control

sensory tests and headspace analysis using an electronic nose.

25

Compared to HDPE, LDPE is in general processed under more vigorous
conditions, i.e. higher processing pressures and temperatures [73], and therefore
the off-flavors from LDPE pouches and coatings may be more of a concern [72,
74]. Srivastava and Rawat (1978) [75] reported that
paper/aluminum/polyethylene pouches tolerated heat (75-90°C in boiling water)
but imparted “plastic flavor" to milk, in terms of their consumer acceptability.

Many studies have been conducted to investigate the use of polyethylene
as a milk packaging material; however, due to its transparency, quite often light-

induced off-flavors are more pronounced than packaging off-flavors.

2.2.1.2. Polyethylene terephthalate (PET)

“Portability improves profitability” [76] and reflects the growing market for
single-serve milk packages. Polyethylene terephthalate, an attractive packaging
material with high clarity, low oxygen transmission rate, considerable mechanical
strength, light weight and versatility [64], is becoming an increasingly popular
packaging material for milk. However, an odorous degradation product from PET
processing (acetaldehyde) is a concern in PET packaged water and milk.

Van Aardt (2001) investigated the origination and sensory impact of
acetaldehyde in milk [12, 77]. Since PET is a clear, transparent material, and
acetaldehyde is a contributor to light-oxidized off-flavors, acetaldehyde in milk
may be present due to both light-induced oxidation and migration from PET.
Sensory detection group thresholds of acetaldehyde in milk containing 3.25%,

2%, and 0.5% fat were determined to be 3939, 4020 and 4040 ppb, respectively,

26

with no statistically significant difference among differing fat levels. Headspace
analysis (SPME-GC) showed that fluorescent light exposure (1 100-1300 lx, 4°C,
18 days) resulted in an increase of acetaldehyde in milk. However, a trained
panel using a nine-point sensory scale was not able to detect acetaldehyde in
either light-exposed or light-protected samples. It was found that acetaldehyde
levels in milk stored in various packaging materials (glass, HDPE, Clear PET,
Clear PET with UV absorber and amber PET) were all below the human flavor

threshold.

2.2.1.3. Paper cartons

Paper cartons, such as gabletops or Tetra-Pak (Brick-Paks), are widely
used for pasteurized milk including ultra-high temperature sterilized (UHT) milk.
One or more polymer layers are combined with paperboard, to provide good
barrier properties, heat resistance and printability [64]. An aluminum layer may
also be added to increase the light and oxygen barrier properties of paper
cartons. Mehta and Bassette (1978, 1980) [78, 79] reported that UHT milk in
aluminum foil-lined cartons, as judged by a 5-member trained panel and a 24-
member untrained consumer panel, was not as good as freshly pasteurized milk,
but superior to milk in plain polyethylene-lined cartons. A positive correlation was
found between the off-flavor intensity and the n-pentanal concentration.

Off-flavors recognized as “lacks freshness” [80] or “stale” [7 8] in paper

carton packaged milk were found to significantly impair the milk acceptance [13,

27

81]. Often the off-flavors are the result of oxidative hydrocarbons migrating from
the LDPE inner coatings [67].

Leong et al. (1992) [13] investigated the packaging off-flavor in water and
in homogenized milk with fat contents of 3.25%, 2% or 0.05%, packaged in PE-
coated paperboard cartons for one, three or six days at 22°C. Paired
comparison tests were performed by a 10-member screened panel. It was found
that packaging off-flavor in water and milk was recognized within one day of
storage, with no significant increase in the off-flavor intensity in the following
three days of storage. Smaller size cartons had a more intense packaging off-
flavor, since the surface area to volume ratio is larger for smaller containers.
Heat sealing did not contribute significantly to the packaging off-flavor.

Gandhi (1996) [81] had elementary school children (2"d through 5th grades)
taste reduced fat (2%) milk in half-pint (237 ml) PE-coated paperboard cartons
(stored for three days at 22°C), and evaluated the results using a two-Sided
paired preference test and a nine point hedonic scale. The results indicated that
the elementary sch00l children had a Significantly higher preference and
acceptability rating for the control, milk stored in glass containers, than for milk in
cartons. The same samples were evaluated by an 11-member trained panel
using a paired comparison test, and the flavor difference between milk in glass

and cartons was also recognized.

28

2.2.2. Light barrier properties of packaging materials

To protect milk and other dairy products from photosensitized oxidation, it
is critical to filter out the visible light below 500 nm, particularly in the harmful
blue-violet visible range of 400-500 nm, using colored or opaque light barriers
.[56]. Figure 8a shows the light transmission at various wavelengths for different
milk packaging materials. Glass allows the highest light transmission through the
UV and visible ranges of the spectrum. Compared to the natural (unpigmented)
HDPE, the addition of UV absorber eliminates the light transmission below
400 nm but not between 400-500 nm. Yellow pigments significantly reduce the
light transmission below 500 nm, and the bright yellow pigment is more effeCtive
than the pale yellow pigment. White pigment (titanium oxide, TiOz) reduces the
light transmission but will still allow partial light transmission at 400-500 nm.
Paper cartons (Figure 8b) have the least light transmission and offer the best
light protection to milk. The light transmission through PE cartOns is below the
recommendations from IDF (lntemational Dairy Federation) for pasteurized milk
[58]. IDF recommends that the maximum permissible light transmission through

packaging material should be 8% at 500 nm and 2% at 400 nm [57].

29

 

 

 

 

 

 

 

 

 

 

  
  

 

 

90 (a) Glass (200 mils)
Natural HDPE
80
C
.3 7° Natural HDPE
“(2 60 with 0.1% UVAbsorber
E
g 50
p 40 2% Pale Yellow HDPE
32
30
20 2% TiOz HDPE
10 % Bright Yellow HDPE
Paper Carton ]
0
300 400 500 600 700 800
Wavelength (nm)
12 (b) — PE coated paper carton
------- PElbarrier paper carton
:10 —- Aluminum-foil carton .
.§ 8 ..........................................................
E e
E
I'- 4
ES
2 ........................... , ,,,,,,
o , 5*. .r T CW - .. e . . s ..-.--zi'ri“.'f.":': r . - . - w—s- s.
300 350 400 450 500 550 600
Wavelength (nm)

Figure 8 Light transmission through various packaging materials. (a) Glass,
unpigmented HDPE and HDPE with yellow or white pigments (20-22 mils), and
paper carton (modified from [56], Figure 3); (b) PE, non-foil barrier and
aluminum-foil cartons. The dotted lines indicate IDF recommendation limits
(modified from [58], Figure 6).

30

Since the 1960’s, the effect of packaging material on light induced quality
deterioration of milk has been widely studied [7, 8, 32, 33, 55, 60, 77, 82-86].
Satter and deMan (1973) [60] reported that opaque pouches protected milk from
light induced quality changes (24 hours of cool-white fluorescent light, 1076 lx or
2152 Ix); paper cartons protected milk up to 12 hours but not longer; HDPE jugs
offered less light protection (3 hours at 2152 IX and 12 hours at 1076 IX), and
Clear pouches provided the least light protection (3 hours at 2152 IX and 6 hours
at 1076 Ix). Dimick (1973) [7] reported that paperboard containers protected milk
from light-oxidized flavor changes up to 48 hours, whereas milk in plastic and
glass containers developed the off-flavor following only 12 hours of exposure
(1076 lx). Coleman et al. (1976) [8] evaluated milk stored in HDPE jugs and
unpigmented and colored (yellow, red, blue and black) paper cartons and
exposed to cool-white fluorescent (1076 lx). It was found that all paper cartons
offered greater protection to light induced flavor changes thandid HDPE jugs,
and there were no significantly different protection effects for the paper cartons
printed in different colors. Pouches of milk packaged in different PE overwraps of
varying light transmittances (printed white/yellow, aluminum ink, or coextruded
black/white pigments) [83] and aluminum foil shields on the bottom and the top of
the HDPE jugs [33] were proposed to provide additional light protection. Van .
Aardt et al. (2001) [77] reported better milk quality retention in amber PET and

UV compounded PET than in clear PET bottles.

31

2.2.3. Other packaging factors affecting milk quality
2.2.3.1. Size and shape

Size and shape directly affect the diffusion rates of the migrants and have
an effect on the packaging off-flavors in milk. For example, Leong et al. (1992)
[13] reported a significantly stronger packaging off-flavor in milk packaged in half-
pint (237 ml) paper cartons than milk in quart (946 ml) and half-gallon (1893 ml)
containers, after 6 days of storage at 22°C. The ratio of surface area to volume
can be used as a measurement of the size/shape factor. The higher the ratio, the
relatively larger surface area is available for migration per unit volume, and
therefore the packaging off-flavor is likely to be more significant.

Light penetration also depends on the ratio of surface area to volume, or
more precisely (but also more difficult to define), the ratio of surface area that is
exposed to light, eg. the top surface of a package, to the package volume.
Dunkley et al. (1963) [54] mentioned in a comparison of glasspint (473 ml), quart
(946 ml) and half-gallon (1893 ml) bottles, the intensity of light oxidized off-flavor
produced by one hour exposure was inversely related to the container size.

Packaging size also affects the microbial stability of milk. Erickson (1997)
[87] compared milk in 1 liter pouches, 2 liter PE-coated paper cartons, and one
gallon (3.785 L) HDPE jugs. Slightly higher microbial populations (total and
psychrotrophic counts) were found in milk packaged in the larger containers, and
in milk whose package had been opened and dispensed. It was suggested that a
unit gallon of milk may remain fresher when packaged in multi-unit packages

than if packaged in one single container.

32

2.2.3.2. Oxygen permeability

Good oxygen barrier properties are more critical for long shelf life milk
products such as UHT milk. Rysstad et al. (1998) [58] evaluated the sensory and
chemical shelf life of UHT milk stored in aluminum-foil, non-foil barrier (X-board),
and PE cartons, with oxygen transmission rates of ~0, 10-20 and >1000

ml
m2 .24 h-atm

 

, respectively. The light transmission properties of these three

paperboards are shown in Figure 8b. At 6°C with no light exposure, milk in PE
cartons had significant off-flavor after 24 weeks, while in the other two materials
(which were better oxygen barriers) there was no off-flavor detected in 26 weeks.
When exposed to light, off-flavors developed in PE cartons after 2 weeks, in X-
board after 8 weeks, and were not detected in milk stored in aluminum-foil
cartons. .

For fresh pasteurized milk, the oxygen in the packaging headspace, in
addition to the oxygen dissolved in the milk, is usually sufficient to induce light
oxidation, which usually can be initiated in a relative short period of time (from 15

minutes to 12 hours).

33

2.3. Light oxidation of cheese

Light-induced oxidation may take place if cheese is exposed to sunlight or
fluorescent light, and can lead to quality changes in color and flavor, as well as
loss of nutrients or formation of potentially toxic compounds [3]. Cheeses are
susceptible to light-induced quality changes, since they are often wrapped in
clear transparent packaging materials, in small pieces under fluorescent light in
the dairy cases for longer times than milk [34]. Oxidative quality changes are
mostly located at the cheese surface [3]. Discoloration and off-flavors caused by

light oxidation may lower product quality and marketability [5].

2.3.1. Discoloration of Cheese

Color is an important attribute affecting the appearance acceptability of
Cheddar cheese. To reinforce natural color and provide appearance to match
market preference, annatto extract and B-carotene are the two colorants often
used [88]. Annatto is a water-based colorant and more readily dissolves in milk
than B-carotene. Discoloration of Cheddar cheese is often associated with
annatto, which is susceptible to oxidation and sensitive to processing parameters
such as pH and temperature [89].

Hong et al. (1995) [90] described the factors affecting light-induced
discoloration of annatto-colored Cheese. Under light exposure, the surface of
Cheddar cheese may experience a slow decrease in redness along with a rapid
decrease in yellowness, resulting in a decrease of hue angles (0° for red and 90°

for yellow), where the hue angle is defined as tan"(bla) for a (redness) > 0 and b

(yellowness) > 0. Exposure to cool white fluorescent light (vs. warm white), high
light intensity, longer light exposure, higher storage temperature (i.e. 8°C vs. 2°C)
lower pH (i.e. 4.8 vs. 5.4) reduces the color stabilityof annatto and results in
more severe discoloration. Hong et al. (1995) [91] conducted a study to
investigate the effects of packaging and lighting. He found that exposure of
Cheddar cheese in modified atmosphere packaging (flushed with 99% carbon
dioxide prior to sealing) to cool-white fluorescent light (3500 Ix) at 8°C for 14 days,
resulted in significant decreases in yellowness and hue angles, and the effects
were more pronounced when using a packaging film with a higher oxygen
transmission rate. Using the same lighting condition, he found that Cheddar
cheese in vacuum packaging had slightly lower yellowness values and an
increase in redness. Incorporating a UV absorber into the cheese packaging did
not improve the color stability, while enclosing it in aluminum foil provided a
significantly great protection. I

Petersen et al. (1999) [88] compared the light sensitivity of annatto and [3-
carotene, two commercial colorants, to monochromatic light at 313, 366 or 436
nm, in a model system as well as in an industrial scale production. In general,
exposure to UV light (313 or 366 nm) caused more color bleaching than
exposure to visible light (436 nm). Annatto had higher apparent quantum yields,
i.e. more sensitivity to light, than B-carotene, irrespective of wavelength. The light
sensitivity of both colorants depended significantly on the combination of pH (5.2
and 5.4) and light wavelength. When exposed to light at 316 nm, annatto was

much more unstable at low pH than B-carotene. Lowering the pH increased the

35

discoloration of Cheddar cheese during light exposure, which may be due to

acid-base equilibrium, eg. between superoxide radical (05‘) and a more

reactive hydroperoxyl radical (H05) with pKa as 4.8 ' [5].

“Pink discoloration” is observed with annatto-colored cheese, has been
known and studied since the 19305. Unlike the light-induced discoloration [90],
the pink discoloration is not limited to the surface, but can occur throughout the
cheese loaf [92]. Possible causes of this color defect include mold enzymes and
acids [93, 94], sulfhydryl compounds [95, 96], precipitation of caratenoids due to
hydrogen sulfide [97], fluorescent light exposure [90, 98], and pH [90]. A recent
study by Shumaker and Wendorff (1998) [92] reported that cooking temperatures
and types of emulsifying salts used had a significant effect on the occurrence of
pink color. High cooking temperature resulted in slight decreases in Hunter a and
b values, and overall decreases in hue angles. High sodium citrate to disodium
phosphate ratio (emulsifying salts) resulted in decreased hue angles. Low
cheese pH was expected to accelerate pink discoloration but it did not cause

significant color changes in this study.

36

2.3.2. Light-induced flavor changes of cheese

Oxidation of lipids and proteins contributes to light-induced flavor changes
in Cheeses. Under fluorescent light exposure, lipids and proteins may experience
photosensitized oxidation in the presence of riboflavin, which presents in cheese
at levels of 0.30-0.60 mgl100 g [5, 99]. Free radicals and/or singlet oxygen are
involved in these oxidative reactions with low activation energies, which can
cause further decomposition of lipids and proteins.

Mortensen et al. (2004) [5] comprehensively reviewed light induced
changes in packaged Cheeses, and summarized their work on this topic [36, 100-
103] as well as the related work of other researchers [34, 35, 39, 88, 90, 91, 104-
107]. Studies related to different cheese products were summarized in terms of
various packaging characteristics, light exposure times and intensities,
parameters investigated (i.e. instrumental or sensory measurements), and major
results [5]. Factors affecting the occurrence of light-induced qUality changes,
including cheese products, process parameters, packaging materials, and light
exposure were reviewed.

Cheese compositional characteristics which affect its photosensitized
oxidation include types and amounts of fatty acids, amino acids, prooxidants, and
antioxidants. High fat cheeses are more susceptible to oxidative discoloration
since more oxidizable substrate is available. High unsaturated fatty acid content
cheeses (due to the addition of vegetable oil), are more prone to oxidation than
normal saturated fatty acid cheeses. Oxidative decomposition of proteins mainly

involves the amino acids and not the peptide backbone, and therefore the

37

amount of “free” amino acids is more important than the total protein content.
Prooxidants include metals, such as copper [108] and iron [109] from processing
equipment or nutrient fortification, and sensitizers, such as riboflavin and
chlorophyll (e.g. herbs in cream cheese), can accelerate oxidative reactions.
Antioxidants found in cheese include carotenoids and tocopherols (mainly in the
lipid phase), and small amounts of ascorbic acid in the aqueous phase [110, 111].
B-carotene is the second most light absorbing constituent of cheeses (Figure 9).
It may protect riboflavin by absorbing a Significant fraction of the harmful light
energy in synergy with y-tocopherol; however, under certain conditions it may
function as a prooxidant instead of an antioxidant [112].

Low pH cheeses are more susceptible to discoloration. The physical
properties of the cheese matrix, in terms of light penetration and oxygen
absorption, directly affect the area and depth of the oxidizable region.
Homogenization of milk reduces the particle diameter and increases the number
of particles, which has the net result of increasing light reflection. Solid-sample
fluorescence spectroscopy [106] has been used to visualize the intensity and
propagation of light-induced oxidation in dairy products including Jarlsberg

cheese, and 5-6 mm of light penetration was reported.

38

 

 

 

    
 

0'14 _’ I] W * Visible 1
0.12 - ; , y .
’ Violet Blue Green Yellow-Red
0.10 °’°"°° -
g 0.08 3 .
.9 . .
o 0‘ e
3 0,05, Riboflavm .
<1: .
0.04 L '
. B-carotene
0.02

 

 

 

0.00

 

300 400 500 600 700
Wavelength (nm)

Figure 9 Absorption spectra of the major naturally occurring colorants of cheeses
at concentrations found in milk (4.52 mm riboflavin in phosphate-citrate buffer,
pH 6.6, and 0.37 mm B-carotene in carbon tetrachloride); from [5], Figure 2.

39

Packaging materials affect light-induced oxidation because of their light
and/or oxygen barrier. Aluminum foil is an excellent barrier to light, and
metallization of plastic materials is widely used to improve the oxygen, moisture
and light barrier properties. The effect of wall thickness, pigments and titanium

oxide has been reviewed previously.

Along with oxygen transmission rate, initial gas composition and product
to headspace ratio will affect oxygen availability. Vacuum packaging reduces
light-induced oxidation by reducing the oxygen content in the package
headspace [91]. Oxidation can still take place, however, because of the oxygen
dissolved in the product. Extremely low oxygen levels (e.g. 0.01%) can be
achieved using oxygen absorbers [102, 103], which can reduce the amount of
secondary oxidative volatiles.

Cool-white fluorescent light can be as harmful to cheeses as it can be to
milk. The turnover times are expected to be considerably longer for Cheeses than
fluid milk, which may be from 8 hours [6, 9] to 2-3 days [10]. In addition, cheeses
are often wrapped in transparent packaging and displayed in small pieces with
large surface areas exposed to light. Significant quality changes in Harvarti
cheese were observed after 12 hours of light exposure, regardless of storage
temperature (3°C and 10°C), light source (yellow light and standard fluorescent

light), or light intensity (600 lx or 1200 Ix).

40

2.4. Electronic nose

An electronic nose is an instrument, which comprises a headspace
sampling system, an array of gas sensors with partial specificity, i.e. differing
selectivity, and a computer to perform signal processing and data processing (i.e.
pattern recognition), to analyze gaseous samples qualitatively and/or
quantitatively, and is often used as an analog of human olfactory responses
(Figure 10). A generally accepted definition of an electronic nose from Gardner
and Bartlett (1994) [14, 113] is, “an electronic nose is an instrument which
comprises an array of electronic chemical sensors with partial specificity and an
appropriate pattern recognition system, capable of recognizing Simple or complex
odors.” It is different from conventional substance-specific chemical sensor
systems, because an electronic nose is designed to quantify and characterize all
substances present in gaseous samples as a whole, without separating and
identifying the individual compounds like a gas chromatograph. The sensor
arrays respond to the mixture of substances simultaneously and create unique
sensor response profiles. After extracting certain response features at the signal
processing step, the extracted features are collected for data processing using
multivariate analysis. Differentiation and/or recognition are then performed based
on the multivariate models, which may be then used to identify the unknown
samples. More of a complementary approach instead of a substitute for
reference methods that use sensory panels [15], an electronic nose can be an

automated discriminatory analysis tool for odors and flavors in foods, and has the

41

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42

potential for applications such as quality control, process control, and off-flavor
detection [16].

Using various combinations of metal electrodes, electrolytes, and applied
potentials, Hartman (1954) [115] assembled the very first experimental array of
amperometric electrochemical gas sensors to analyze odorous samples,
although there was no serous attempt to process the data patterns [113]. Several
studies developed sensory arrays based on temperature-sensitive resistors,
modulation of conductivity, modulation of the contact potential, etc. However, the
concept of using chemical gas array coupled with computer-based odor
classification was not established until Persaud and Dodd (1982) [116] in the UK.
and lkegami et al. (1985) [117] in Japan built their systems [113]. After nearly 20
years of development since then, many commercial electronic nose systems are
currently available on the market (Table 2). Most are desktop analytical
instruments, which operate sensors at high temperature (e.g.-Metal Oxide
Semiconductors, MOS) or because of the bulk of the sensors (e.g. Mass
Spectrometry, MS). Several handheld units based on SAW (Surface Acoustic
Wave) or CF (Conducting Polymer) mechanisms are also available, eg. the
Cyranose 320TM from Cyrano Sciences, which uses a 32-channel carbon black

polymer composite chemiresistor array.

43

Table 2 Commercially available electronic nose instruments (modified from [118],
[119] Table 4.7 and [120] Table 7.1)

 

Number of

 

Manufacturer Chemosensor Type‘ Sensors Pattern Recognltlon
Agilent Technology MS ..2 I ANN, PCA, PLS
Alpha M05 M08, CP, SAW, QCM, 6-24 ANN, DFA, PCA
MS
AppliedSensor MOSFET, MOS, QCM - —
AromaScan CP 32 PCA, ANN
Bloodhound Sensors CP 14 ANN, CA, PCA
Cyrano Sciences CP (composite) 32 PCA
EEV Ltd. Chemical Sensor CP, MOS, QCM, SAW 8-28 ANN,DFA, PCA
System
Electronic Sensor GC, SAW 1 SPR
Technology
Element MOS — -
Environics Industries IMCELL — -
HKR Sensorsysteme QCM 6 ANN, CA, DFA, PCA
Lennartz Electronic MOS, QCM 16-40 ANN, PCA, PCR
Marconi Applied CP, MOS, QCM - -
Technologies
Microsensor Systems SAW - -
Neotronics Science CP 12 -
Osmetech CP 48 -
RST Rostock MOS, QCM 6-10 ANN, PCA
Sensobi Sensoren (microsensor) 8-16 -
Shimadzu MOS 6 PCA
SMart Nose MS - PCA, DFA
Technobiochip QCM 8 —
Technologies AB MOSFET, QCM - -
WMA Airsense MOS 10 ANN, DC, PCA, SPR

 

1Chemosensor type; Mass Spectrometry- MS; Metal Oxide Semiconductor — MOS, Organic
Conducting Polymer — CP, Quartz Resonator Microbalance — QCM, Surface Acoustic Wave -
SAW, Gas Chromatography - GC, Quadrupole Mass Spectrometry - QMS, Infrared - IR and
Metal-oxide-semiconductor Field Effect Transistor — MOSFET. Pattern recognition; Artificial
Neural Network - ANN, Distance Classifiers — DC, Principal Component Analysis - PCA,
Statistical Pattern Recognition - SPR, Discriminant Function Analysis - DFA, Cluster Analysis -
CA and Principal Components Regression — PCR.

2 lnforrnation not available.

2.4.1. Sensor arrays

A chemical gas sensor is a device that is capable of converting a chemical
quantity into an electrical Signal which corresponds to the concentration of
specific particles such as atoms, molecules, or ions in gases [119]. It is a
reversible process: a dynamic equilibrium develops as volatile compounds are
constantly being adsorbed and desorbed at the sensor surface [121]. The types
of Chemical sensors that are used in the electronic nose applications need to, but
may not only, respond to odorous volatiles. Gas sensors respond to a range of
organic molecules, depending on the sensor construction, but they also respond
to water vapor, which is odorless to humans. In general, CP sensors are the
more sensitive to humidity changes than MOS sensors.

Chemical gas sensors that are ideal to be used in electronic noses Should
have high sensitivity (comparable to the human nose which can smell to 10‘12
g/ml) and partial specificities, i.e. each sensor responds to a different range of
compounds present in the gaseous sample. It is also ideal to have the sensOrs
insensitive to humidity and temperature, with high stability and reproducibility,
short reaction and recovery time, easy calibration, durable and inexpensive [121].

The advantages/disadvantages of chemical sensors based on
conductometric, capacitive, potentiometric, calorimetric, gravimetric, optical, and
amperometric principles are listed in Table 3. The schematic diagrams of the five

commercially available sensor mechanisms are shown in Figure 11.

45

 

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Figure 1 1 Schematic diagrams of five different kinds of sensors. (a) metal oxide
semiconductor (MOS); (b) conductive polymer (OF); (C) quartz crystal
microbalance (QCM); (d) surface acoustic wave (SAW); (0) MOS field effect
transistor (MOSFET). Modified from [121], Figure 1.

47

Based on the operating temperatures, these sensors can be categorized

as hot sensors (e.g. MOS, MOSFET) and cold sensors (e.g. CP, SAW, QCM).

Operating at higher temperatures may make the sensors less sensitive to

moisture with less carry over from one measurement to another, and therefore

they may have less sensor drift and longer lifetime [15]. On the other hand, the

high operating temperature consumes more enerQY. as well as limits their use as

sensors for handheld/portable devices. Comparison of the performance of the

MOS, MOSFET, CP, OMB and SAW sensors is shown in Table 4.

Table 4 Comparative properties and performance of the most frequently used
gas sensors in electronic nose instruments (from [122], Table 1).

 

 

Performance MOS MOSFET CP QMB SAW
Selectivity Poor Moderate Moderate High High
Sensitivity > 0.1 ppm > 0.1 ppm 0.01 ppm > 0.1 ppm ppb
Reproducibility Poor Good Good Moderate Moderate
Temperature Low Low High Moderate High
dependence
Carrier gas Synthetic Synthetic Inertl lnertl Inert!

air (02) air (02) Synthetic Synthetic Synthetic

air (02) air (02) air (02)

Humidity Low Moderate High Low Low
dependence
Operating 300-400 100-200 Ambient Ambient Ambient
temperature (°C)
Response time (0.5 - 5) (0.5 - 5) (20 - 50) (20 - 50) (20 - 50)
(sec.)
Recovery time Fast Fast Slow Slow Slow
Lifetime (years) 3 - 5 1 - 4 1 - 2 < 2 < 2

 

48

2.4.1.1. Metal oxide semiconductors (MOS)

The n-type metal oxide semiconductors, e.g. SnOz, ZnO, and Fe203,
respond to reducible gases such as H2, CH4, C2H5 or H28 with increased
conductivity at temperatures of ZOO-500°C [119]. At equilibrium (i.e. baseline),
oxygen in the air is adsorbed on the surface [O(s)‘] by trapping free electrons [e]

from n-type semiconductors, which consequently produces a high resistive layer.

e + ‘A 02 —+ O(s)'
When the reducible gases [R(g)] are introduced to the system and react with the
sensors, they consume the oxygen atoms adsorbed on the surface [RO(g)] and
increase free electrons [e], which thus increases their conductivity.

R(g) + 0(8)‘ —+ R0(9) + 6
Similarly, p-type semiconductors (e.g. CuO, MO and CoO) respond to oxidizable
gases (e.g. 02, N02, Clz) which decreases their conductivity during the reaction.
Note that these instruments usually measure the resistance (R) instead of the

conductivity (0), based on the relationship:

L 1 L _ . ..
R = _ = - _ , where p. resustnvnty,
F’(A) 0(A)

L: length,

A: cross section area.

The reaction between gases and surface oxygen varies depending on the
operating temperature of the sensors and the activity of semiconductor materials,
including the doped impurities and catalytic metal additives such as palladium

(Pd) or platinum (Ft) [119]. The impurities act as additional electron donors (or

49

acceptors). Controlling the doped amount of impurities can change the
conductivity of the sensors, i.e. sensitivity. The catalytic metals doped to the
sensors or coated as a thin layer on the sensor surface control the selectivity of
the sensors.

MOS sensors have been widely marketed and used in many commercial
electronic nose systems, due to their high sensitivity and stability, compared to
sensors based on other mechanisms, eg. CP, QCM, SAW, although MOS
sensors have relatively poorer selectivity [123], i.e. are less substance-specific
and responding to a wider range of compounds. Redundant information obtained
as a result of poor selectivity may be reduced using multivariate methods such as
principle component analysis (PCA) or discriminant function analysis (DFA) at
the data processing step. Other limitations of MOS sensors include: their high
sensitivity to ethanol, CO2, and moisture, which masks desired responses to
aroma compounds, i.e. “blinds” the sensors to other analytes' of interest, the
baseline recovery may be slow for some high molecular weight compounds, and
certain reactive gases such as sulfur compounds or weak acids may have

irreversible binding reactions and can permanently “poison” MOS sensors [15].

2.4.1.2. Conductive organic polymer (CP)

CP sensors rely on a change in conductivity when exposed to reducible or
oxidizable gases. Reversible changes in conductivity take place when chemical
substances (e.g. methanol, ethanol, and ethyl acetate) adsorb and desorb from

the polymer [119]. The sensors (Figure 11b) are composed of a substrate (e.g.

5O

fiberglass or silicon), a pair of gold-plated electrodes, and a conducting organic
polymer such as polypyrrole, polyaniline, polythiophene, or polyacetylene as a
sensing element [15, 123]. The common feature of these electronically
conducting materials is a one-dimensional polymer backbone with conjugated
double bonds, i.e. alternating single and double bonds, which enables a super-
orbital to be formed for electronic conduction [113, 119]. Polypyrrole can be
either fully oxidized or doped by different amounts of counter-anions, which
balances the charge on the polypyrrole backbone and changes the polymer
conductivity. The properties of the resulting conducting polymers depend on the
choice of monomer and counter-anions and the polymerization conditions, eg.
solvent used and concentration of the monomer and counter-anions [113].

GP sensors have the following advantages [15, 113, 119]: (1) a wide
choice of materials can be synthesized; (2) relatively low cost materials; (3)
respond to a broad range of organic vapors; (4) operate at room temperature; (5)
allow extreme miniaturization, i.e. small sizes, since the sensor responses are
independent of the polymer length. The disadvantages of CP sensors include [15,
123]: (1 ) sensitive to moisture; (2) relative long response time; (3) short life-time;
(4) poor batch-to-batch reproducibility; (5) pronounced sensor drift overtime or

with changes in temperature.

51

2.4.1.3. Quartz crystal microbalance (QCM) and surface acoustic wave (SAW)

QCM and SAW sensors are gravimetric odor sensors, using acoustic
wave devices that operate by detecting the effect of sorbed molecules on the
propagation of acoustic waves [119]. A basic device consists of a piezoelectric
substrate, such as quartz, LiNbO3 or ZnO, with a chemically and thermally stable
sorbent coating. Sorption of vapor molecules into the sorbent coating is then
detected by the change in resonance frequency and amplitude of oscillation of
the propagation of acoustic waves on the piezoelectric materials [15, 113]. The
sensitivity and selectivity of a sensor depends on the thickness of the quartz
crystal and the choice of sorbent coating materials, which must take into
consideration the solubility parameter of the sorbent coating and detecting gases
[113]. QCM sensors (Figure 110) propagate the acoustic waves inside the crystal
(e.g. quartz). SAW sensors (Figure 11d) comprise a relatively thick plate of
piezoelectric material with electrodes, usually of gold, to excite the oscillation of
the surface, i.e. the acoustic waves transmitted at the surface of the crystal [15,
113].

Both QCM and SAW sensors can be operated at room temperature, and
can be modified for a higher degree of specificity by choosing different sorbent
coatings. The main problems with QCM and SAW sensors are their relatively
poor long-term stability and high sensitivity to moisture, poor batch-to-batch
reproducibility, and pronounced sensor drift over time or with changes in

temperature.

52

2.4.1.4. Metal-oxide semiconductors field-effect transistors (MOSFET)

MOSFET sensors rely on a change of electrostatic potential to respond to
gases [123]. A MOSFET sensor consists of a silicon semiconductor, a silicon
oxide insulator and a catalytic metal “gate”, e.g. palladium, platinum, iridium or
rhodium (Figure 11a). In the case of the palladium (Pd) MOSF ET transistor, the
applied voltage on the metal gate (i.e. palladium) and the drain (where the
current flows out) contact creates an electric field, which influences the

conductivity of the transistor. When the metal gate reacts to the substances

which can generate hydrogen atoms (H' ), such as hydrogen, ethanol, and
hydrogen sulfide [113], the electric field and thus the current flowing through the
sensor are modified. The changes in voltage necessary to keep a constant
current flow are then recorded as the sensor responses [123].

The selectivity and sensitivity of MOSFET sensors are influenced by the
operating temperature (SO-200°C), the composition of the metal gate, and the
microstructure of the catalytic metal [123]. MOSFET sensors have similar
advantages and disadvantages as those reported for MOS sensors, such as high
sensitivity and robustness. To achieve good quality and reproducibility the

MOSFET sensors may require higher levels of manufacturing expertise [123].

53

2.4.2. Signal processing

Prior to entering the data processing step for pattern recognition, signal
processing is required to extract the feature(s) of the sensor responses. Each
sensor in the sensor array gives a dynamic response over time while reacting to
the injected gaseous samples. An ideal first order sensor response to a step odor
input, i.e. an instantaneous step change in odor concentration at t = 0 over a
specified period of time (until the end of region III), is shown in Figure 12a. The
steady-state region III is the region that is commonly used to estimate the sensor
parameter because it eliminates any variability in the flow delivery system, eg.
dead-time and flow rate [113]. The MOS sensors in this study used the sensor

R-Ro

O

 

parameter in the steady-state region (Figure 12b), max( ), the rescaled

maximum value of the resistance R to the baseline resistance R0. Since the
injected volatile concentrations decreased gradually instead cf the ideal step
input, the steady-state region lll may not end as a sharp slope as shown in
Figure 12a.

If the response time of the sensors to odors is relatively long, the initial
slope (i.e. the slope of the linear portion in region I) may be used as a predictor of
the final steady-state value [113]. If there is no significant variability in the
dynamic behavior of the sampling system, eg. using an automated sampling
system instead of manual sampling, parameters in the dynamic regions II and IV

may be useful in data processing.

 

..m ,

m I IV

Output signal, g(t)

 

 

Q

 

 

Time, t

(h) f

 

 

 

0 Time, r

Figure 12 Sensor responses of (a) an ideal first order odor sensor responding to
a step odor input (b) MOS sensors in this study (R: resistance). Figures modified
from [113], Figure 6.13.

55

To remove or reduce the irrelevant sources of variation (either random or
systematic), a data preprocessing step is often applied before the data, i.e. the
extracted feature(s) described above, are entered into the multivariate analysis.
Preprocessing changes the data which will either positively or negatively
influence the results [124]. Several common preprocessing techniques are
described below.

Normalization of a sample vector is accomplished by dividing each
variable by a constant [124]. Amine at al. (1998) [125] compared the
performance of three types of normalization applied to MOS sensor responses
(FOX 4000 system, AlphaMOS), and concluded that the sum normalization

nvars
(normalizing to unit area by dividing Z lle, where the xj is the jth sensor
j=1
response of sample x in the experimental set) gave the best differentiation in
principle component analysis (PCA) and discriminant function analysis (DFA).

Mean centering is a common preprocessing tool that is applied to account
for an intercept in the data. Variable scaling is achieved by dividing each element
in a variable vector by the standard deviation of the variable. Autoscaling is the
application of both mean centering and variable scaling [124]. These are
common tools for variable preprocessing. For example, mean centering is always
used in PCA. As shown in Figure 13, without mean centering, the first principle
component (PC1) describes the direction from the origin to the cloud of data,

instead of the variance of the data (Figure 13b).

Variable 2

 

  
 

 

Variable l

 

(b)

Variable 2

 

 

 

 

 

Figure 13 Example of PCA (a) without mean centering and (b) after mean
centering.

57

2.4.3. Data processing

Pattern recognition (PARC) is the second most critical element of an
electronic nose, after the design of sensor arrays. A more generalized definition
of electronic nose was used for techniques using multivariate analysis techniques
to analyze any multiple instrumental measurements, such as the area responses
on a chromatogram [126] or peaks on a mass spectrum (e.g. HP 4440A from
Agilent Technologies and SMart NoseTM from SMart Nose).

Figure 14 shows the multivariate analysis techniques applied to electronic
nose data, including statistical chemometric methods and biologically inspired
methods [114]. “Unsupervised” Ieaming routinely separates the different classes
from the response vectors, by discriminating between unknown odor vectors
without being presented with the corresponding descriptors. On the other hand,
in “supervised” Ieaming a known set of odors is systematically introduced to build
a model, which is then evaluated by testing/predicting the class membership of
an unknown odor. Based on the data distribution assumption (e.g. normal
distribution), the classical statistical methods can be categorized as parametric
(e.g. discriminant function analysis, DFA) and non-parametric (e.g. nearest
neighbor, NN) methods.

The biologically inspired methods are more capable of handling non-linear
data, and have further advantages such as Ieaming capabilities, self-organizing,
generalization and noise tolerance. Since electronic noses are designed to
simulate human noses, it may be more desirable to apply biologically motivated

algorithms that imitate the human brain by learning from patterns.

58

More detailed description of the multivariate analysis techniques used in

this study are discussed in Chapter 2.6.

Quantitative r Supemsedi MLR

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

PLS
Statistical
, Unsupervised PCA
chemometrics { NN
Pattern CA '{ War d’s
anal sis T
y Supervised E DFA —O— LDA
. PCR
Multivariate 4» Unsupemsed4_ SOM
analysw _ ”m + MLP
Supervised PNN
RBF
LVQ
. . , FIS
Biologically L Supemsed l FNN
inspired Fuzzy FCM
methods Self-supervised ART
A Fuzzy ARTMAP
Self-supervised
Others + GA
Supervised NFS
Wavelets

Figure 14 Classification scheme of the multivariate pattern analysis techniques
applied to the electronic nose data, including multiple linear regression (MLR),
partial least squares (PLS); principle component analysis (PCA), cluster analysis
(CA) including nearest neighbor (NN), discriminant function analysis (DFA)
including linear discriminant analysis (LDA), principle component regression
(PCR), artificial neural networks (ANN) including self-organizing map (SOM),
multi-layer perceptron (MLP), probabilistic neural networks (PNN), radial basis
function (RBF), Ieaming vector quantization (LVQ), fuzzy inference systems (FlS),
fuzzy neural networks (FNN), fuzzy c-means clustering (FCM), adaptive
resonance theory (ART), fuzzy ARTMAP, genetic algorithms (GA), neuro-fuzzy
systems (NFS) and wavelets (from [114], Figure 6.3).

59

2.4.4. Applications of the electronic nose
2.4.4.1. Electronic nose in milk applications

Electronic nose systems have been widely applied in many different areas,
such as environmental monitoring, medical diagnostics and health monitoring,
natural product identification, process monitoring, food and beverage quality
assurance, automotive and aerospace applications, detection of explosives,
cosmetics and fragrances [127]. Table 5 shows some examples of electronic
nose applications in natural products including foods, and the sensors and
pattern recognition methods that have been applied. Of main interest in this study
are the cases involving dairy products and their packaging. These are reviewed
below.

Heating time and temperature control of the pasteurization process affect
milk quality. Cooked off-flavor in pasteurized milk was the first flavor defect in
milk that was analyzed using an electronic nose. Sulfur containing volatiles such
as hydrogen sulfide, which is generated during pasteurization, give a “cooked or
sulfurous taint” in milk. Further overheating may produce carbonyl compounds,
nonenzymatic browning or Maillard reaction byproducts [128]. Sberveglieri et al.
(1998) [129] showed promising results using a lab-made electronic nose, with an
array of four MOS sensors, to discriminate (using principle component analysis,
PCA, and cluster analysis, CA) the heat treatments of whole milk, i.e.
pasteurization, direct ultra high temperature (UHT) method and “in bottle”
sterilization. The result was then verified by the headspace volatile contents

quantified using DH (dynamic headspace using absorbent Tenax).

60

Table 5 Recent electronic nose applications In natural products (modified from

[130], Table 19.1).

 

 

Application Sensor Data Analysis

Toasting level of oak wood 6 MOS PCA, DFA, NN

barrels .

Fermentation- Bioprocess eNOSE 4000 (Neotronics)- DA

monitoring 12 CP

Freshness of soybean curd 6 MOS PCA

Cheese ripening eNOSE 5000 (1ZCP 8 MOS); CDA
60MB; 10MOS-FET + 5MOS;
.Smart Nose (MS)

Milk spoilage 14 CP (Bloodhound) BP-NN, DFA, PCA, CA

(yeast/bacteria)

Espresso (7 blends) Pico-1 (5 Thin Film MOS) PCA, ANN

Espresso 4 thin film Tin Oxide PCA, MLP ANN, data is drift

Beans/Groundlliquid corrected

Coffee 12 MOS Fuzzy ARTMAP

Vanillin fortified Grapefruit ion-trap MS chemical sensor PCA, DFA

Juice

Fruit Ripeness monitoring Tin Oxide NN

Fruit Quality thickness shear mode quartz PCA and Learning Vector
resonators (TSMR) coated Quantization (LVQ) neural
with pyrrolic macrocycle network

Tomato Aroma e-NOSE 4000 ( 12 CP) MVDA (CDA)

Soft rot detection in potato 2 MOS and 3 MOS (2 Threshold

tubers ' experiments)

Oatmeal oxidation Fox 3000 (Alpha MOS) PCA, SlMCA

Barley Grain Quality 10 MOSFET, 6 MOS, 1 C02 PCA, PLS, PLS-DA, SlMCA
monitor

Cereal quality BH114, Blood hound, PCA,’DA, CA
14surface-responsive
polymer array

Wheat classification by 16 electrochemical K-NN, NN

grade

Wheat quality CP array RBF-ANN (92 samples in

training)
Rice Quality 10 MOSFET and 12 MOS PCA

Capelin spoilage for fishmeal
production

Mahi-mahi freshness

Chicken Freshness
Minced Meat Rancidity
Swine Products

Olive oil quality

Frying Fat Quality

Corn oils

Maize Corn oil rancidity
Tansy Essential Oil
Golden Rod Essential Oil
Wood Chip Sorting

FreshSense (9
electrochemical gas sensors)

AromaScan (32 CP)

8 M08

HP 4440

FOX 2000, 6 M08
8 CP

4 MOS

AromaScan (32 CP)
MOSES ll- 8 MOS, 8 QMB's
32 CP

32 CP

32 CP

61

PLS1, saturated generalized
linear model

MDA using AromaScan A328
VVll'ldOWS software v. 1.3
Neural net

PCA

LDA, SlMCA

PCA

Compared results of MOS
sensors to reference food oil
sensor using line plots.

PCA

PCA

PCA

PCA

PCA

\fisser and Taylor (1998) [131] tested the performance of a commercial
electronic nose, Aromascan A328 equipped with 32 CP sensors, by analyzing
varieties of food products. The fresh and cooked milk samples were differentiated
using a sensory panel (triangle test), and the headspace volatiles of salt-added
samples (to reduce relative humidity) were clearly discriminated in PCA analysis.
However, the electronic nose was not capable of differentiating aromas from
cheese, coffee and banana. This was due to the high moisture sensitivity of CP
sensors, and the sensor responses were mainly influenced by the water vapor
instead of the volatiles from the product. Mulville (2000) [132] reported that an
electronic nose equipped with MOSF ET, MOS and IR sensors was able to
differentiate boiled/non-boiled milk mixtures in ratios of 0/100, 10/90 and 50150
using PCA analysis.

Zondervan et al. (1999) [133] used an electronic nose (Neotronics eNOSE
4048) equipped with 12 CP sensors and an autosampler to analyze 8 blockmilks
(obtained by heating and drying/concentrating a mixture of milk and sugar to ca.
98% dry matter) at various stages of processing, and associated the results with
the sensory scores given by a trained panel of 23 members using quantitative
descriptive analysis. Volatiles generated in the Maillard reaction during the
caramelization step are the main contributors of the product flavors. The results
showed that the electronic nose was capable of differentiating samples (PCA and
DFA) and predicting the sensory scores (ANN).

Since off-flavors in milk comprise a mixture of numerous volatiles, a gas

chromatograph can be used to determine the volatile contents, which then can

62

be analyzed using the same multivariate analysis techniques. Vallejo-Cordoba
and Nakai (1993, 1994, 1995) [134-137] developed pattern recognition
techniques to differentiate pasteurized milk with different shelf-lifes, which were
determined by sensory evaluation based on ADSA guidelines. The shelf life was
ended whenever a score of 5 or lower was recorded by three of the five judges.
Incubation for 18 hours at 24°C produced headspace volatiles from milk samples
which were then quantified by DH-GC (chapter 2.5) and analyzed using
multivariate analysis. Principle component regression (PCR) based on the DH-
GC peak areas was capable of predicting the milk shelf life within an accuracy of
1:2 days, which was much better than that obtained using psychotropic bacteria
counts (PBC). An artificial neural network (ANN) was also used for shelf-life '
prediction and it performed better than PCR. Linear discriminant analysis (LDA)
was able to classify the milk with different shelf-life and identify the spoilage-
associated volatiles. Marsili (1999, 2000) [126, 138] investigated cooked (UHT
milk), light-oxidized, lipid-oxidized (added copper), and microbial spoiled UHT

milk via a similar approach, using SPME-GC instead of DH-GC.

2.4.4.2. Electronic nose in cheese applications

Electronic noses analyze the mixture of volatiles directly, as typical
cheese flavors do not depend upon a single key component, but originate from a
variety of aroma compounds, i.e. component balance theory [139]. Most studies
in this field focus on either differentiating the types or origins of cheese samples,

correlating to the sensory quality, or monitoring the aging processes of cheese

63

manufacturing. Similarly, DH-GC or SPME-GC coupled with pattern recognition
techniques may also be generalized as an “electronic nose” [140].

Wijesundera and Walsh (1998) [141] analyzed various cheeses using an
electronic nose fitted with 18 MOS sensors (FOX 3000 from AlphaMOS). Six
varieties of cheese (Edam, Gouda, Parmesan, Pecorino, Jarlsberg and Cheddar)
were cut into 3 x 1 x 1 cm pieces, sealed in 20 ml headspace vials, and
equilibrated at 40°C for 15 minutes. Using PCA, most of the six varieties were
discriminated except for two overlapping pairs: Edam/Gouda and
Parmesan/Pecorino. Cheddar cheeses at different aging times (1, 3, 7, and 22
months) were also fully discriminated with PCA.

Drake et al. (2003) [142] exploited a mass spectrometer (MS) based
electronic nose (HP 4400, Agilent Technology) to differentiate aged Cheddar
cheeses from different locations, using PCA and CA (cluster analysis). Pillonel et
al. (2003) [143] differentiated 20 Emmental cheese samples from Switzerland,
Germany, France, France, Austria and Finland using DH-GC and PCA analysis.

Schaller et al. (1999) [144] tested five sensor technologies and four
instruments: eNose 5000 (EEV Chemical Sensor Systems) with 12 CP and 8
MOS sensors, QMB6 (HKR-Sensorsysteme) with 6 QMB sensors, NST 3220
(Nordic Sensor Technologies) with 10 MOSFET and 5 MOS sensors, and SMart
Nose (SMart Nose Ltd.) based on a mass spectrometer (MS), to analyze the
Swiss Emmental cheese samples at different stages of ripening. The results of
PCA and DFA showed that MOS sensors gave the best discrimination, but the

MOS sensors seem to be damaged by short-chain fatty acids released from the

Emmental cheese [15]. The CP sensors had pronounced sensor drift and
resulted in poor selectivity. The QCM sensors had relatively lower sensitivity and
were not capable of detecting the differences between cheese samples, and
neither did the MS system. The MOSFET sensors did not give good
discrimination between samples. Their attempt [145] to detect the rind taste off-
flavor due to inadequate ripening and/or storage in Emmental cheese was not
successful using the NST 3220 system. Schaller et al. (2000) [146] then applied
preconcentration techniques (purge-and-trap or SPME) and significantly
improved the performance of the SMart Nose system (MS based) to differentiate
Emmental cheese samples ripened for 1, 21, 98 and 180 days.

Squibb (2001) [147] evaluated two handheld electronic nose systems,
pprAE (RAE Systems) and Cyranose 320TM (Cyrano Sciences). The pprAE
uses a photoionization detector and was able to detect differences between
rancid and normal rapeseed oil samples and to detect the spiking of cardboard
and tissue paper with disinfectant. The Cyranose 320TM instrument was able to
distinguish mature Cheddar cheese from the other cheese samples, but could
not differentiate between mild and medium cheese.

Trihaas et al. (2002) [148-150] focused on the microbiological quality of
Danish blue cheese and Camembert cheese, and analyzed them using the two
electronic nose systems: BH-114 (Bloodhound) employing 14 CP sensors and
aFOX-3000 (AlphaMOS) was equipped with 6 MOS, 4 CP and 2 QCM sensors.
Both systems proved to be capable of defining the ripening stage of the cheese

samples.

65

O’Riordan and Delahunty (2003) [151, 152] evaluated cheese-grader-
classified (based on market specification of sensory characteristics) Cheddar
cheese samples using an electronic nose system fitted with 8 MOS sensors.
Electronic nose discrimination (PCA) between cheeses was related to
composition, the profile and abundance of headspace volatiles, and the

descriptive sensory characteristics of the cheeses.

2.4.4.3. Electronic nose in packaging applications

Several studies have used electronic noses to study off-flavors from
packaging materials, e.g. HDPE, PET, and paper cartons. As discussed
previously (Chapter 2.2.1), migrants such as oxidative hydrocarbons and residual
solvent or additives may cause packaging off-flavors in the food contents.

Maneesin (2001) [73] investigated the packaging off-flavor from one-gallon
HDPE water containers. The electronic nose (12 MOS sensors, FOX 3000,
AlphaMOS) was capable of discriminating the volatiles from HDPE produced with
different antioxidants (lrganox 1010 and a-tocopherol), but not the water
contained in the HDPE containers. Similarly, Das (2003) [66] used the same
electronic nose to analyze 8 HDPE resin samples, and also found that the
instrument was capable of discriminating the resins but not the water samples
containing the resins at 40°C for one week.

To detect retained solvents on printed packaging films, van Deventer and
Mallikarjunan (2002) [153, 154] evaluated the performance of three electronic

nose systems: FOX3000 (AlphaMOS) equipped with 12 MOS sensors, Cyranose

66

320m (Cyrano Sciences) equipped with 32 CP sensors, and QMB6 (HKR
Sensorsysteme) equipped with 6 QCM sensors. The results of linear discriminant
analysis (LDA), which were displayed on two-dimensional canonical
discriminants plots, indicated that all three systems correctly identified 100% of
the unknown samples, while the FOX3000 and Cyranose 320TM were superior
based on discriminatory power and practical features.

For quality control purposes, Poling et al. (1997) [155] constructed
multivariate models of volatiles from various qualities of PET pellets and
paperboard, based on the sensor responses of the electronic nose FOX 4000
(AlphaMOS) fitted with 18 MOS sensors. The models were then used to
successfully predict the qualities of unknown samples.

To monitor taints related to printed solid boards, Heinio and Ahvenainen
(2002) [156] utilized an electronic nose NST 3320 (Nordic Sensor Technologies)
equipped with 10 MOSFET, 12 MOS, one 002 sensor and one humidity sensor,
to differentiate 20 solid board samples that were unprinted, lacquered, and offset-
printed in 14 different colors. The electronic nose succeeded (PCA) in grouping
these materials according to their coloring agents or Iacquering, despite slight
overlapping of replicates, and showed some of the off-flavor intensities obtained

from sensory tests.

67

2.5 Solid-phase microextraction (SPME)

Solid-phase microextraction is a solvent-less extraction technique
developed in 1990 [157], which involves the exposure of a fused silica fiber with
a thin layer of polymer coating, to isolate and concentrate analytes (volatiles or
semivolatiles) from a gaseous or liquid sample, or the headspace of a liquid or
solid sample. Analytes are absorbed or adsorbed by the fibers, and later
thermally desorbed in the injection port of a gas chromatograph (Figure 15), or
dissolved into the mobile phase of a SPME/HPLC interface, and then delivered to
the HPLC column for separation [158].

At equilibrium, the amount of an analyte extracted by the coating is
determined by the thickness of the polymer coating [159], and the magnitude of
the partition coefficient of the analyte between the sample matrix and the coating
materials [160]. Although SPME is an equilibrium sampling method, it can still be
highly reproducible through proper calibration and precise control of the exposure
time (Figure 16). Temperature can also affect the equilibrium during extraction
since the partition coefficient of the analyte is temperature dependent. Other
factors which can affect SPME precision [160] include: agitation conditions,
sample volume, headspace volume, vial shape, conditions of the fiber coating
(e.g. cracks, adsorption of high molecular weight species), geometry of the fiber
(thickness and length of the coating), sample matrix components (e.g. salt,
organic material, humidity), time between extraction and analysis, analyte losses
(adsorption on the walls, permeation through Teflon, absorption by septa),

geometry of the injector, fiber positioning during injection, condition of the injector

68

(pieces of septa), stability of the detector response, and moisture in the needle,
etc. These experimental parameters should be kept constant to ensure good

reproducibility of the SPME extraction.

(a)

(b)

 

 

 

 

 

 

Figure 15 Solid phase microextraction (SPME) to concentrate headspace
volatiles: (a) extraction; (b) desorption [159].

69

 

 

L
y

Pre-Equilibrlum / E __ f
\ .

Time Control Not

 

 

 

 

 

'8 As Critical.
2 1 Small change
0 in time results
.8 in small or
< ‘ no change in _
(I) \-\\ analyte absorbed. }
c Time Control i Equrlrbrrum
< Is Critical. ! Reached
Small change in i
time results in ]
large change i
in analyte ,
absorbed. i
Extraction Time >

 

Figure 16 Effect of time on amount of analyte absorbed (modified from [161]).

Based on the characteristics (e.g. volatility and polarity) of target analytes,
polymer coatings and/or coating thickness should be selected. In general, highly
volatile compounds require a thick coating, and a thin coating is most effective for
adsorbing/desorbing semivolatile analytes. Table 6 lists the types of SPME fibers
available (Supelco, Bellefonte, PA) and recommended use.

A technical document from Supelco Inc. [162] gives a detailed review of
SPME applications up to the year 2001 in the areas of foods, polymers/coatings,
natural products, pharmaceuticals, biological matrices, toxicology, forensics, and
environmental (water, pesticides, soil and air). Applications using SPME-GC to

analyze the headspace volatiles of milk and cheese are reviewed below.

70

Table 6 Types of SPME fibers and their recommended use (modified from the
table “SPME Fiber Assemblies” in [162]).

 

Film Thickness

Recommended use

 

Polydimethylsiloxane
(PDMS)

PolydimethylsiloxnelDivinylbenzene
(PDMS/DVB)

Polyacrylate
(PA)

Carboxen/Polydimethylsiloxane
(CARIPDMS)

CarbowaxlDivinylbenzene
(CW/DVB)

Carbowax/Templated Resin
(CW/T PR)

DivinylbenzenelCarboxen/PDMS
(DVB/CAR/PDMS)

Considered nonpolar for nonpolar
analytes

Ideal for many polar analytes,
especially amines

Highly polar coating for general use,
ideal for phenols

Ideal for gaseous/volatile analytes,
high retention for trace analysis

For polar analytes, especially for
alcohols, low temperature limit

Developed for HPLC applications,
eg. surfactants

Ideal for broad range of analyte
polarities, good for C3-C20 range

 

71

Marsili (1999) [126] quantified the light-induced headspace volatiles in 2%
milk and skim milk. Spiked pentanal and hexanal in milk were analyzed by SPME
(75 um CARIPDMS fiber, 45°C, 15 minutes) and DH (dynamic headspace, i.e.
purge-and-trap and thermal desorption using the absorbent Tenax). to compare
the method performance. It was found that the SPME method showed
significantly better repeatability, i.e. a lower coefficient of variation (%CV), and
had comparable detection limits (0.3 nglml pentanal and 0.8 nglml hexanal in 2%
milk) and linear least squares correlation coefficients. A study conducted by
Contarini and Povolo (2002) [163] using a DVBICARIPDMS fiber concluded that
SPME and DH had similar repeatability.

Marsili (1999) [126] used the same SPME method to develop a rapid
technique to study the off-flavors in milk. A mass spectrometer (MS) and
multivariate statistical analysis (MVA), which was PCA in the study, were used to
identify and differentiate the headspace volatiles from abused milk samples with
detectable malodor or off-flavors, including cooked (UHT milk), light-oxidized,
lipid-oxidized (added copper), and microbial spoiled UHT milk. Furthermore,
Marsili (2000) [138] collected commercial 2% milk and chocolate milk and stored
them at 7.23:0.5°C until the end of shelf-life, which was determined when 3 of the
4 judges gave a score below 5, followed the ADSA guidelines. A SPME-MS-MVA
technique, using partial least squares (PLS), was utilized to correlate the
headspace volatiles and the shelf-life as determined by sensory evaluation. It

was found that the SPME-MS-MVA technique was a viable alternative to current

72

commercial electronic nose instruments, and capable of differentiating off-
flavored milk, and also could predict the milk shelf-life.

Van Aardt et al. (2001) [12, 77] used a SPME technique (75 um
CARIPDMS fiber, 45°C, 15 minutes) to quantify the acetaldehyde in milk, which
can originate from both light-induced oxidation and through migration from PET
bottles. Gonzélez—Cérdova and Vallejo-C6rdoba (2003) [164] using SPME (85pm
PA fiber, 70°C, 30 minutes equilibrium time, and 60 minutes exposure time) to
determine the hydrolytic rancidity, i.e. amounts of free fatty acids (F FA), and was
able to correlate this to the rancid flavor intensity sensory scores using stepwise
multiple linear regression. Piero et al. (2003) quantified the short chain saturated
aldehydes (Cs-C9) in the headspace of infant formula (powder) using SPME
(65pm PDMS/DVB fiber, 25°C, 15 minutes) coupled with GC-MS. Hexanal, a
potential marker of milk powder oxidation, was quantified in the ppm level using
an isotope dilution technique. I

SPME coupled with GC or HPLC has been widely used to analyze aroma
[140, 146, 165-173], off-flavors [107] and contaminants [174, 175] in cheese.
Frank et al. (2004) collected the headspace volatiles of Cheddar, blue-mold, and
hard-grating (Parmesan, Pecorino and Grana Padano) styles of cheese varieties
using a CARIPDMS fiber (22°C, 16 hours), and analyzed them using GC-
olfactometry and GC-MS. Sulfur compounds such as dimethyl trisulfide and
methionol played an important role in cheese aroma. Pionnier et al. (2004) [173]
studied the aroma release during eating in vivo, using a 100 um PDMS fiber to

collect headspace volatiles of stirred cheese slurries at 25°C over time. Peres et

73

al. (2001) [166] used different SPME fibers (PDMS, PA, PDMSIDVB, and
CARIPDMS) to extract the headspace volatiles of Camembert cheese samples
(20°C, 10 minutes), and a model was constructedusing CARIPDMS fiber
coupled with GC-MS and stepwise discriminant analysis (SDA). Pinho et al.
(2002, 2003) [168, 170, 171] quantified volatile free fatty acids (FFA) in Terrincho
ewe cheese, and compared the performance of the six fibers: PDMS,
PDMSIDVB, CW/DVB, DVBICARIPDMS, PA, and CARIPDMS. It was concluded
that the CARIPDMS fiber (20°C, equilibrium time of 20 minutes, and exposure
time of 30 minutes) gave the most complete cheese volatile profiles. Kim et al.
(2003) studied the light-oxidized volatile compounds in goat’s milk cheese using
a 65 pm PDMSIDVB fiber (40°C, equilibrium time of 30 minutes and exposure
time of 30 minutes), and concluded that fluorescent light can increase the
headspace volatile compounds including 1-heptanol, heptanal, nonanal, and 2-
decenal [107]. Zambonin et al. (2001 and 2002) [174, 175] extracted two types of
mycotoxins, cyclopiazonic acid (CPA) and mycophenolic acid (MPA), from the
surface of semi-soft cheeses and blue-veined cheeses, respectively, using a 60
pm PDMSIDVB fiber (room temperature, pH 3 buffered solution, 30 minutes)
coupled with HPLC-UV.

74

2.6. Multivariate statistical techniques
2.6.1. Unsupervised Ieaming techniques
2.6.1.1 Hierarchical clustering analysis (HCA)

Hierarchical clustering analyses (HCA) proceeding by either a series of
successive mergers (agglomerative hierarchical methods) or a series of
successive divisions (divisive hierarchical methods), can be used to examine the
interrelationships between all observations in a two-dimensional plot and is
known as a dendrogram [124, 176]. It is a measure of dissimilarity between
group observations, based on the pairwise dissimilarities among the observations
in the two groups [177]. Three linkage criteria for agglomerative hierarchical
procedures are illustrated in Figure 17, including single linkage (minimum '
distance or nearest neighbor), complete linkage (maximum distance or farthest
neighbor), and average linkage. A single linkage dendrogram is generated based
on their neamess in row space; in this each sample is initially treated as an
individual cluster, then joined to the “nearest neighbors” to create fewer numbers
of clusters, as the analysis proceeds. This continues until only one cluster
remains (Figure 17d). Cutting the dendrogram horizontally at a particular height
partitions the data into disjointed clusters represented by the vertical lines that
intersect it. Groups that merge at high distance values, e.g. objects (1, 2) and (3,
4, 5) (Figure 17d), are candidates for natural clusters. It is interesting to compare
the actual class membership, if available, with the natural clustering. If they are
not coincident, the measurements (X '8) might not be sufficient to differentiate

different groups, and clustering might be affected by factors not considered.

75

In brief, samples which are more “similar", e.g. milk or Cheddar cheese

subjected to the same duration of light exposure, are expected to be naturally

clustered, i.e. in the same or nearby clusters.

 

 

 

 

 

 

 

 

 

 

I \ I ‘
I \ I .3\
r1. \ r \
M4 j
5
\ 2 I \ .I
\.-’I \~“’
(a) o
o
:1 l r
r r.4 r
\ 2.1 I
\~’/ \_’
(b)
’_\ I—\
I \ I_.3\
f1 \
r r 4 l d .
\ 2 I ( 5, 1 2 ,3 4
‘__,’ ‘...-’ Objects
(C) (d)

Figure 17 An example of hierarchical clustering analysis (HCA). The intercluster

 

0'.

distances can be defined as (a) single linkage, d24, (b) complete linkage, d15,

and (c) average linkage, 6

dendrogram for distances between 5 objects [176].

76

(’13 + d14 + 0'15 + (’23 “’24 + d25 ’ (d) single linkage

2.6.1.2 Principle component analysis (PCA)

Principle components are the linear combinations of the p random
variables X1, X2, X p , which represent a new coordinate system with
maximum variability [176]. The first principle component (PC1) is the projection of
the p -dimensional data that explains the most variance of the original variance-

covariance structure. The second principle component (PC2) is orthogonal to
PC1 and explains the maximum amount of the remaining variation, and so on.

Let 2 be the covariance matrix associated with the random vector

XT = [X1, X2, ..., Xp] with the mean u = E(X) such that

z = Cov(X) = E(X — u)(X - p)T .

Using singular value decomposition (SVD), Z can be written as

 

 

£=PAPT,
"A, o o i
o 12 :
where P =[e1,e2,...,ep], A = . . 0 .
_0 0 1p-
T .
eigenvectors e,- = [e,-1,...,e,-p] , and eigenvalues 2.. _>. 22 2 2 1p 2 0.
The im principle component PCi is given by
PCi = e,-TX,
or PCi=e,-1X1+e,-2X2+---+eipo i=1,2,..., p,
where
Var(PCI)=e,T2-e,-=,1, i=1,2,..., p

77

Cov(PCi,PCi')=e,oT2-e,« = o, i: i'.

Computationally, let x be a n x p data matrix representing n observations

for the p variables. Assume i is mean-centered (u = 0 ), i.e. the estimated mean

has been subtracted from each column:

 

 

 

 

 

 

 

 

 

 

 

 

 

’211 212 21p _ n ~
53 = .21 where ’ =1 = 0
- n
L52," 2,,2 inp-nxp
Eon-Of E(X1-0XX2-0) E(X1-oxxp-0)
Z = E(X2 -0)(X1—0) ' -- ‘
[E(xp — 0)(x1—0) E(xp —0)2
’ .. .. . ... 'l
i121+m+ir221 i11i12+m+in1in2 x11xlp+”'+xn1xnp
n —1 n—1 n -1
X12X11+“'+ Xn2xn1 : : i7)?
= n —1 = —
. . . . n -1
~ ~ ~ ~ ~2 ~2
x1px11+m+xnpxn1 1p+---+x,,p
_ n —1 n -1 _
The principle components PC1, PCk can be obtained using the
52’s: . .
singular value decomposition of Z = ———1 usrng the same procedure described

on the previous page.

78

p
Notice that the total population variance ZVar(X,-) equals the total
i=1
variance of the principle components,

P ‘ P
ZVar(X,-) = tr(Z) = tr(PAPT) = tr(l\) = 11 + 22 + + AP = ZVar(PCi) ,
i=1 i=1

and the proportion of total population variance explained by each PCi is

1" i=1,2,...,p.
’11 + 12 + - ~ + 1p

Principle component analysis can be used for data reduction. Since the
algorithm used to select each PC explains the maximum amount of the remaining

variance of X, most variation is expected to be represented by PC1, PCk,
k s p. Scatter plots of the first two or three PC’s (k = 2 or 3) are useful for
observing p -dimensional data on two dimensional (Figure 18) or three
dimensional plots, which will explain most of the variation of the original data
matrix explained.

Geometrically, PCA involves rotation of the original coordinates

(dimension p) to the new coordinates (dimension k s p):

x = TPT + e.
T is called the score matrix, with columns as latent vectors. P, the loading matrix,
contains the information about how the original measurements are related to the
principle components. In PCA, the columns of P are simply the eigenvectors of
the covariance matrix 2. Error e = 0 when all principle components are included

in the model (k = p). Dimension reduction (k < p) is achieved when most of the

79

variation can be represented by fewer principle components so that e is small

and can be ignored.

(a)

Second Prlnclple Component (PC2)

 

 

 

 

 

 

 

 

 

 

 

 

*
° 0
Q 0
. o o" o PC1
0. ‘°
0 o 0 .0
0.‘ O .
o
0
X1
x2
0
O
O O . O.
0.. O o ..0
o . o. 0 °
.. at...) 49° °: ~°°
I'te Ore?” ..., .0: °
. o O ‘ 00. . O
o. e e o
0
O O
0
First Prlnclple Component (PC1)

Figure 18 An example of principle component analysis (PCA). (a) Principle
components PC1 and PC2 in the original three dimensional space composed by
variables X1, X2 and X3; (b) scatter plot of the first two principle components PC1
and PC2. Diagrams were generated using Minitab R13 (Minitab Inc., PA).

80

2.6.2. Supervised Ieaming techniques

2.6.2.1 Linear/quadratic discriminant function analysis (LDA/QDA)
Discrimination and classification involves defining decision boundaries so

as to separate observations from different populations. R. A. Fisher (1938) first

introduced the linear discrimination method described above, for two or more

populations [178]. Let x be a px1 data vector which represents one observation
of the p variables. From Bayesian decision theory, the posterior probabilities of
assigning x to class a),- is

Pm, M = clo<xlw.-)I-'>(w.-)

Z [’0‘ I 60])P(€0j)
j=1

where P(w,-)is the prior probabilities of class (0;, and p(x | (0,) is the conditional
densities of x given 02,-. A set of discriminant functions is defined as
g,-(x),i =1,...,c, which allocates x to class wiif g,-(x) >g,-u(x), for all i¢ i'. To

minimize the error rate of classification, x should be assigned to the group with

the highest posterior probability [179],

 

MK I (WWW)
C

Zp<x|wj)P(w,-)
j=1

thus g.-<x>=P(w,- IX)=

C
If g,-(x) is redefined by ignoring the scale factor X p(x | wj)P(wI-) and taking its
j=1

natural logarithm, it will become

g,-(x)=lnp(x|w,-)+lnP(a2,) i=1,...,c,

81

and random variables X1, X2, ,Xp are multivariate normal distributed
P(X I mi) " Np (Until
and the discriminant functions are

1 _ 1 .
g,- (x) = -§(x -p,-)T Z,-1(x—u,~)-§ln2n—§ln|£,-|+lnP(w,-) I: 1, 0.

Moreover, if it assumes all classes have identical covariance matrices,

Z,- = Z, the discriminant functions are simplified as,
1 T ..1
9i (X) = --2—(X-|.Ii) Z, (x-pi)+lnP(a),-),
which involves measuring the squared Mahalanobis distance(x — p,- )T 2‘1 (x - pi)

from x to the nearest group mean pi. Dropping the quadratic term xTZ'1x

which is independent of i, the discriminant functions then are linear:
-1 T 1 1' -1 .
g,(x)=(2 pi) x+ --2-p,-Z p,+lnP(w,-) r=1,...,c.
Linear decision boundaries can be used to separate groups with the same

covariance matrices, with the assumption of equal and unequal P(w,-)’s, and

which are demonstrated in Figure 19a and Figure 19b, respectively.

For cases where we have unequal group covariance matrices, 2,- ¢ 2, the

quadratic term xTZ‘1x cannot be dropped, and the discriminant functions. are

quadratic:

T
910‘) = XT(--;—Z,71]x +(Z’1pi) x +|:—%uf2‘1pi+lnP(wi)] i= 1, c.

82

A quadratic decision boundary used to separate two groups with different

covariance matrices is demonstrated in Figure 19c.

(a) ’51 =22, P(w1)=P(°°2) (c) :1 =42. P(w1)=P(w2)

 

 

 

 

Figure 19 Decision boundaries defined based on the discriminant functions. (a)
linear boundaries of two classes with identical covariance and equal prior
probabilities; (b) linear boundaries of two classes with identical covariance and
unequal prior probabilities; (c) quadratic boundaries of two classes with different
covariance and equal prior probabilities; (d) decision boundaries for four normal
distributions [179].

83

In short, linear discriminant function analysis (LDA) can be used to
separate classes that are normally distributed using the equal covariance
assumption. This can be evaluated using the “test of homogeneity” on the group
covariance matrices [180]. Quadratic discriminant function analysis (QDA) can be
applied when covariances are assumed or tested unequal. Figure 19d shows an
example of decision boundaries for multiple group discrimination.

For visual inspection or graphical description of LDA, canonical
discriminant analysis (CDA) may be used to reduce the dimension from a large

number p to a relatively few (2 or 3) linear combinations, such that the between-

class variance B is maximized relative to the within-class variance W [176, 177].

The between-class covariance matrix is,
c T
B = Zn,- (in -u)(ui -u) .
i=1

where c is the number of classes,

It; is the percentage of class i samples in the entire data set,

pi is a column vector denoting the mean vector of class i, and

C
p is the overall mean vector such that p = 2p,- /c.

i=1
The within-class variance W is the common covariance matrix 5:. Note that
B + W equals the total covariance of X .To obtain the linear combination of X

that maximizes the ratio of between-class variance and within-class variance, the

1 1
singular value decomposition (SVD) is applied to (W 2 )TBW 2 , to find its

eigenvectors v1, v2, vs, corresponding to the nonzero eigenvalues. We then

obtain the discriminant coordinates

1 .
a,-=W 2v,- i=1,2,..., ssmin(c—1,p).

Therefore, the k ‘“ canonical discriminant (CAN k) can be written as

CANk=a;x, k=1,2,..., s

or CAN/r = ak1X1+ akzxz +~- + akpX , where a,- = [a,-1,...,a,-p ]T.

Figure 20a is an example of the first canonical discriminant (CAN1), which
is the linear combination of the original variables X1, X2 and X3 that maximizes
the difference between groups “. ” and “o ”. It is not necessary that the canonical
discriminants are the same as the principle components, which are the linear
combinations of the original variables that maximize the total variance. When the
total variance is mainly contributed by the between—group variance, the canonical
discriminants are expected to be similar to the principle components. Figure 20b
shows an example when the first canonical discriminant (CAN1) is very different

from the first principle component (PC1).

85

 

 

 

 

 

 

 

 

 

Figure 20 Examples of canonical discriminant analysis. (a) The first canonical
discriminant (CAN1) in the three dimensional space composed by variables X1,
X2 and X3; (b) a case where the CAN1 is very different from PC1, the first
principle component. Diagrams were generated using Minitab R13 (Minitab lnc.,
PA).

86

2.6.2.2. k-nearest neighbor (k-NN)

The k-nearest neighbors (k-NN) method is a nonparametric classification
technique, which classifies unknown observations based on their similarity with
observations in “training” data. That is, for a given unlabeled object x, the
method finds k closest labeled objects in the training data set, and assigns x to
the class that appears most frequently within the k “neighbors”. For example,
using Euclidean distances and k = 3 to measure the similarity between unknown
and training data in a two dimensional space is illustrated in Figure 21a.

The selection of k and the metric to define closest “neighbors” are critical

to the performance of k-NN. Considering

1sksmin(n,-) i=1,...,c,
where n,- is the number of training samples in class mi, and c is the number of

the classes. If the classes are well separated, the one nearest neighbor has a
high probability to be in the same class, thus k=1 provides a good classification
rule [124]. If classes are not clearly separated, using k>1 may yield smoother
decision regions and provide probabilistic information, though it may also destroy
the locality of the estimation and increase the computational burden. Weighted
distances may be applied instead of Euclidean distances for higher dimensional
data, to reduce the severe requirements for computation time and storage, as a
result of lack of data reduction in such a nonparametric procedure [179].

In k-NN, the identification of an unknown observation depends on the

“majority votes", if the closest k neighbors of an unknown observation do not

87

have a majority class, a “tie” occurs and the unknown data cannot be assigned to

any group (Figure 21b).

‘A
. 1.
a A
”0.) Ox I
1 I
A
I

(b)

 

Figure 21 Examples of k-nearest neighbor (k-NN). (a) 3-NN, the k-nearest
neighbor such that k = 3, where the unknown observation x is assigned to the
class 001, since two out of the three “neighbors” from the training set belong to

w1. (b) A “tie” occurs in 4-NN, where the unknown observation x cannot be
assigned to either (01 or (oz.

88

2.6.3. Quantitative analyses
2.6.3.1 Partial least square (PLS) regression
Partial least square regression generalizes and combines the features

from principle component analysis and multiple linear regression, to predict
dependent variables (Y) from independent variables (XT = [X1, X2, X p]) and

to describe their common structure [181], for example, to predict sensory scores
(Y) from the instrumental measurements (X). When Y is a vector and X is full
rank, the prediction can be accomplished using ordinary multiple linear
regression (MLR). MLR is not applicable if X's are highly correlated, or the

number of observations are smaller than the number of X 's (n < p). Principle

component regression (PCR) applies multiple linear regression using principle
components of X as predictors, to eliminate the multicollinearity problem by
using the orthogonality of the principle components. However, PCR does not
choose an optimum subset of predictors for Y. Principle components are
selected to explain X and not necessary to be the best subsets to explain Y.
The PLS approach is an indirect modeling technique (Figure 22) which
finds components from X that are also relevant for Y, using simultaneous -
decomposition of X and Y. The selection of these components, called latent

vectors, is to explain as much as possible the covariance between X and Y.
The decomposition of X and Y (the estimator of Y) are

x = TPT, and

f = TBCT,

where T is the score matrix and P is the loading matrix.

89

 

 

 

 

 

 

 

 

 

1 ll

Factors (X) Responses(Y)

 

 

 

 

 

 

 

 

Sample

 

 

 

 

/ \
f N F \

 

 

Factors (X) _> Responses(Y)

 

 

 

 

L J L J
Population

 

 

J

Figure 22 The indirect modeling concept of partial least squares (PLS) approach
(modified from [182], Figure 3).

90

The columns of P are the latent vectors, which are not orthogonal in PLS
(note that the loading matrix P in PCA is orthogonal). B is a diagonal matrix with
the regression weights as diagonal elements. The PLS. is to find two sets of
weights (w and c ), which can be used to create linear combinations of the
columns of X and Y such that their covariance is maximum, i.e., to find the first

pair of vectors t and u such that
{t = Xw, u = Yc| wTw =1, max [cov (t,u)]} .

When the first latent vector is found, it is subtracted from both X and Y and the
procedure is re-iterated. PLS can be performed using the iteration algorithm

above [181] or singular value decomposion [124].

2.6.3.2. Multilayer perceptron (MLP)

Neural networks are adaptive computer architectures based on an
analogy of the biological neural system, and are used to learn or discover the
association between input and output patterns, and to analyze the structure of
the input patterns. The basic neural network units of neurons (Figure 23a), are
connected by weighted connections (synapses), and arranged in layers. Values
of the parameters (i.e. the synaptic weights) of the networks are modified
iteratively as a function of the model performance. Mean square error (MSE), the
average squared error between the network outputs and the target outputs, is a
common criterion function for feedforward networks such as multilayer

perceptrons [1 83].

91

Multilayer perceptrons (MLP, Figure 23b) are used in a supervised
manner with an error back-propagation algorithm. Network Ieaming starts with an
untrained network, and a training pattern is presented to the input layer. The
signal is passed through hidden layers, and the output obtained at the output
layer. Synaptic weights and biases are then adjusted based on an error-
correction rule, e.g. to minimize the MSE [184]. Back-propagation Ieaming
updates the network weights and biases in the direction in which the MSE

decreases most rapidly:

xk+1 = Xk - 0ka
where xk is a vector of current weights and biases, 9,, is the current gradient,
and ak is the Ieaming rate [179, 185].

Neural network transfer functions are denoted by f in Figure 23, and can
be any differentiable functions which generate outputs. They are. required to be
differentiable since the back-propagation algorithm calculates the derivatives of
any transfer functions used. A typical MLP contains one or more non-linear
sigmoidal functions (e.g. log-sigmoid or tan-sigmoid functions) in the hidden
layers, and a linear function in the output layer [185].

Training the MLP using gradient decent in the criterion function (e.g. MSE)
ultimately reaches the lower bound of the error when having infinite training data.

The choice of Ieaming rate (ak) and number of weights affects the asymptotic

error value, as well as how fast the training error decreases, since in practice we
do not have infinite data points for training. Also, when the lower bound of

training error is reached, it often makes the models too optimistic, i.e. the models

92

may not be applicable for another independent data set. Therefore, an early stop
criterion based on a validation data set, is usually needed. Network training stops

when the error reaches a minimum for the validation set [179].

Bias x0: 1
x1 cell 1
\ mo = - 9
(Ho 4-0
*' a fla) 3
—~ rs
w.

Computation Transformation
of the activation of the activation

 

Input layer Hidden layer Output layer

Figure 23 Multiplayer perceptron (MLP) structure. (a) A neuron, the basic neural
unit; (b) a multilayer perceptron [183].

93

CHAPTER 3
MATERIALS AND METHODS

3.1. Sample preparation

Milk in one gallon clear HDPE containers (3.785 L) was obtained from a
local retail store (Meijer, Okemos, MI) and distributed into the glass or other milk
packaging materials on the same day. At Meijers, the milk was displayed on
open stainless steel shelves at ~5°C, with lighting by fluorescent bulbs covered
by yellow plastic tubes, i.e. gold shield [54, 55]. Milk samples, within 10-14 days
of sell-by dates, were picked from the middle of the bottom shelves, immediately
wrapped in brown paper bags, transported to the laboratory, and stored in a 5°C
refrigerator within 15 minutes. A fan was installed inside of the refrigerator to help

reduce the temperature fluctuation to within :l:1°C.

3.1.1. Light-oxidized off-flavors in 2% milk

Reduced fat (2%) milk was distributed into glass bottles (nominal capacity
8 oz, clear, Boston Rounds, Qorpak, Bridgeville, PA) in a 5°C environmental
chamber. Samples were then exposed to cool-white fluorescent light (4 bulbs of
Light of America F15 T8 CW OP11 15W) for 48 hours at 5°C. The light intensity
was measured from the top of the sample bottles using a digital light meter
(Model SLM 110, A.W. Sperry Instruments, NY), and the average light intensity
was recorded as ~1000 lx. All sample bottles were wrapped in aluminum foil prior

to light exposure. The aluminum foil wraps of randomly selected sample bottles

were removed for 2, 4, 8, 12, 24, 36 and 48 hours. Two controls (“0" and “d”)
were prepared using the same type of glass bottles that were wrapped in
aluminum foil and stored in the dark for 0 or 48 hours. Milk was light exposed for
specified times, immediately removed from each container, and prepared for use
in sensory evaluation and headspace analyses using SPME or the electronic
nose (experimental flowchart in Figure 24).For sensory evaluation, 20 ml of the
milk samples were distributed into glass vials (nominal capacity 20 ml, screwed
top with foil-lined cap) in a 5°C environmental chamber, covered with aluminum
foil and stored at 5°C. Milk samples were moved to ambient temperature (~21°C)
approximate one hour prior to each sensory session. The milk temperature was
maintained at 10-15°C for all sensory testing [186]. For instrumental analyses, 1
ml of the milk sample was removed from each container using a 1 ml serological

glass pipet, sealed in a 10 ml headspace vial, and stored below -18°C.

 

Reduced fat (2%) milk in glass bottles exposed to fluorescent light

 

 

(1000 Ix) at 5°C for 0,2, 4, 6, 8, 12, 24, 36, or 48 hours.

.................. I.----------_----

Instrumental analyses

 

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Sensory evaluation

 

SPME-GC (45°C headspace)

 

 

Triangle tests (consumer panel)

 

 

 

Flavor rating (trained panel)

 

 

 

 

 

(45, 70, 95°C headspace)

 

:
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Electronic nose I
l
:
E

 

 

 

 

 

 

 

 

 

 

 

Correlation

 

Figure 24 Experimental flowchart to investigate the light-oxidized off-flavors in
2% milk, using sensory evaluation and instrumental analyses.

95

3.1.2. Packaging off-flavors in 2% milk

Samples of HDPE. HDPE compounded with TIOz (HDPE-TiOz), and PET
bottles (food grade, nominal capacity 8 02., Boston Rounds, eBottles.com,
Woodbridge, CT) were purchased from an internet store (eBottles.com). PE-
coated paper cartons (nominal capacity 8 oz., Gable top) were obtained from a
local dairy plant (Melody Farms, Lansing, MI); the empty cartons were erected
and sealed using equipment at Melody Farms. The empty cartons were opened.
filled manually with milk or water, and resealed using binder clips (Figure 25).
Glass bottles (nominal capacity 8 02., clear, Boston Rounds, Qorpak, Bridgeville,

PA) filled with milk or water were used as the control.

 

Figure 25 Packaging materials used for milk and/or water. Front row left to right:
a PE-coated paper carton, a HDPE-TiOz bottle, and a PET bottle. Back row left to
right: a HDPE bottle, a glass bottle, and a glass bottle wrapped in aluminum foil.

96

To investigate the effects of packaging off-flavors, samples of various
packaging materials were analyzed using the electronic nose. Water and 2% milk
stored in various packaging materials at 5°C for 3 days were analyzed using the
electronic nose, and sensory evaluation performed using a consumer and/or

trained panel. The experimental flowchart is shown below (Figure 26).

 

Pieces of glass, HDPE, HDPE- Inswmenmj analysis

 

TiOz, PET bottles, and PE- Electronic nose

 

coated paper cartons (45, 70, 95°C headspace)

 

 

 

 

Water stored in glass, HDPE, Electronic nose

 

 

(95°C headspace)

HDPE-TiOz, PET bottles, and

 

 

 

PE-coated paper cartons at 5°C

I Sensory evaluation
for 3 days :

 

 

 

Triangle tests

 

Reduced fat (2%) milk stored in

 

 

 

(consumer panel)

 

glass, HDPE, HDPE-TiOz, PET

 

bottles, and PE-coated paper Flavor rating

cartons at 5°C for 3 days (trained panel)

 

 

 

 

 

 

 

 

 

Figure 26 Experimental flowchart to investigate packaging off-flavors in 2% milk,
using sensory evaluation (triangle tests and flavor rating based on ADSA
guidelines) and instrumental analysis (electronic nose).

97

One gram of the HDPE, HDPE-TiOz, PET bottles, or PE-coated paper
cartons cut into pieces of ~1 cm2 were sealed in 10 ml headspace vials for
electronic nose analysis. One gram of glass from broken glass bottles was used
as the control. The headspace samples of various packaging materials were
generated at 45, 70 or 95°C for 15 minutes, using the oven located on the

autosampler of the electronic nose.

Water (HPLC grade in 4 L amber glass jugs, J.T. Baker, Phillipsburg, NJ)
stored in various packaging materials at 5°C for 3 days, was analyzed using a
series of triangle tests and the electronic nose. Twenty ml of the water samples
were distributed into 20 ml glass vials and evaluated at ambient temperature
(~21°C) using a consumer panel. One ml of water samples was removed from
each container using a 1 ml serological glass pipet, sealed immediately in a 10
ml headspace vial, and stored at ambient temperature prior to electronic nose
analysis. Water in the original 4L jugs was used as the control.

Reduced fat (2%) milk was stored in the glass, HDPE, HDPE-TiOz, PET
bottles, and PE-coated paper cartons at 5°C for 3 days. One ml milk sample was
removed from each container using a 1 ml serological glass pipet, sealed in a 10
ml headspace vial immediately, and stored below -18°C for electronic nose
analysis at a later time. Twenty ml of the milk samples that had been stored in
glass, HDPE, and PET bottles, and PE-coated paper cartons was distributed into
20 ml glass vials in a 5°C environmental chamber and evaluated at 10-15°C by a

consumer panel or a trained sensory panel.

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3.1.3. Light-oxidized and packaging off-flavors in 2% milk

Reduced fat (5%) milk stored in glass, HDPE, HDPE-TiOz, PET bottles,
and PE-coated paper cartons were exposed to fluorescent light (1000 Ix) at 5°C
for 12 hours (Figure 27). One ml milk sample was removed from each container
using a 1 ml serological glass pipet, sealed immediately in a 10 ml headspace
vial, and stored below -18°C for electronic nose analysis at a later time. Twenty
ml milk samples was distributed into 20 ml glass vials in a 5°C environmental

chamber and evaluated at 10-15°C by a consumer or a trained sensory panel.

 

Reduced fat (2%) milk in glass, HDPE, HDPE-TiOz, PET bottles, and

PE-coated paper cartons exposed to fluorescent light (1000 Ix)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

at 5°C for 12 hours.
E Sensory evaluation E Instrumental analysis E
E Triangle tests E E Electronic nose E
(consumer panel) I (95°C headspace) I
E Flavor rating
E (trained panel) E

Figure 27 Experimental flowchart to investigate the light-oxidized and packaging
off-flavors in 2% milk, using sensory evaluation and instrumental analysis
(electronic nose).

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3.1.4. Cheddar cheese samples

A block of cheedar cheese (21.3 kg, medium sharpness, aged at 5°C for 7

months) was obtained from the Michigan State University Dairy Plant. The outer

layer (~ 5 cm) of the cheese block was trimmed off and discarded. The cheese

block was then cut into one-pound (454 9) blocks and vacuum-sealed in

transparent shrink bags (Cryovac® B-series, Cryovac, Duncan, SC), which were

characterized as high gloss and low oxygen permeability. Table 7 lists the

oxygen and water vapor transmission rates of the Cryovac® B-series bags.

Table 7 Oxygen and water vapor transmission rates of the Cryovac® B-series
bags, given the temperature (°C) and relative humidity (%RH).

 

 

 

 

 

 

 

Transmission rate Value Source
Oxygen transmission 3 _ 6 cc Product specification
rate at 44°C, 0% RH m2 -day from the supplier
Oxygen transmission 24 CC Measured using an
rate at 5°C, 60% RH m2 -day Oxtran1
:Nater vapor t t 0.5 _ 0.6 _9___ Product specification

ransmrssron ra e a 100,,"2 . day from the supplier

378°C, 100% RH

 

 

 

" A modified Oxtran 100 - Twin (Mocon Inc, Minneapolis, MN), operated inside of

an environmental chamber controlled at 5°C, 60% RH.

100

 

The Cheddar cheese in 1-lb vacuum-sealed blocks was arranged
randomly in the environmental chamber and exposed to fluorescent light (2 bulbs
of Philips F48T12/CWIHO 60W) for 0, 2, 4 and 6 weeks. The relative humidity
fluctuated between 50—70% RH due to the malfunctioning relative humidity
control of the chamber. The oxygen transmission rates of the vacuum bag did not
vary significantly at different relative humidity (Table 7), thus the fluctuation of
relative humidity was not expected to affect the results. The light intensity was
measured from the top of the cheese blocks using a digital light meter (Model
SLM 110, A.W. Sperry Instruments, NY), and the average light intensity was
recorded as ~2000 Ix. All cheese blocks were wrapped in aluminum foil prior to
placement to the chamber, and the foil wraps were removed for 0, 2, 4 or 6
weeks for randomly selected samples. To avoid deviations in light exposure, all
cheese blocks were rearranged followed a random sequence generated using
Matlab 6.1 (The MathWorks Inc, Natick, MA) every 3 days, during the 6 weeks of
light exposure. Figure 28 shows the arrangement of the Cheddar cheese blocks
in the 5°C environmental chamber equipped with the light fixture. I

Two samples, i.e. surface slabs (the top surface) and interior slabs (~1 cm
below the top surface), were taken from the Cheddar cheese samples exposed
for a specified duration of light. For color measurement and sensory evaluation,
3mm thick slabs were sliced off using a cheese cutter (Model CC-12, Nelson-
Jameson, Marshfield, WI), vacuum-sealed in Nylon/PE bags (0.75 mil nylon/ 2.25
mil polyethylene, Koch, Kansas City, MO), and stored at 5°C until one-hour prior

to sensory evaluation. All samples were evaluated at ambient temperature

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(~21°C). For headspace analysis, 1 mm thick slabs were removed using a razor
blade, and cut into a disk-like specimen (0.15: 0.01 9, id. = 11 mm) using a test
tube. Each specimen was sealed in a 10 ml headspace vial and stored below

—1 8 °C for analysis at a later time.

 

Figure 28 The arrangement of the Cheddar cheese samples exposed to
fluorescent light (2000 lx) at 5°C for 0, 2, 4, or 6 weeks. The aluminum foils were
unwrapped at the specified weeks. Cheese blocks were rearranged randomly
every 3 days.

Figure 29 shows the experimental flowchart to investigate the light-
induced deterioration of Cheddar cheese and its effect on color, body/texture,
and flavor, using sensory (different-from-control and ADSA rating) and

instrumental methods (color measurement, SPME-GC and the electronic nose).

102

 

Surface and interior slabs of the Cheddar cheese samples exposed to

fluorescent light (2000 lx) at 5°C for 0, 2, 4, or 6 weeks.

 

 

 

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E Sensory evaluation I Instrumental analyses E
Different-from-control in color, E E Color measurement E
body/texture, and flavor 5 (CIE L* a* b*)
E (trained panel) E SPME-GC E
I I E < :
E Rating in body/texture and E E (60°C headspace) E
flavor (trained panel) E E Electronic nose E
(60 and 90°C headspace)

5 I

Correlation

 

 

 

 

 

 

 

Figure 29 Experimental flowchart to investigate the light-induced deterioration of
Cheddar cheese in color, body/texture, and flavor, using sensory evaluation and
instrumental analyses.

103

3.2. Sensory evaluation

Triangle tests were performed by a 24-member consumer panel, which
was recruited mainly from the students and faculty members of the School of
Packaging and Department of Food Science & Human Nutrition, Michigan State
University. A rating based on the ADSA guidelines and different-from-control
tests were performed using a trained panel of 8-9 members, which consisted of
the coach and members of the Michigan State University dairy judging team for
Collegiate Dairy Products Evaluation Contest, 2002. The trained panelists
received training on the ADSA guidelines for six types of dairy products: milk,
Cheddar cheese, creamed cottage cheese, Swiss type yoghurt, butter and ice
cream, for 4-6 hours per week for a 6-8 week period.

Panelists were told to spit out all test specimens after judging (foam cup
was provided), and rinse their mouth with deionized water between specimens if
necessary. A three-digit random number was assigned to each milk, water or
cheese specimen. Random number codes and permutations were generated
using Matlab 6.1 (Mathworks lnc., Natick, MA). The sensory evaluation designs

and test forms for all sensory tests are shown in Appendix 6.2.

3.2.1. Triangle tests performed by a consumer panel

Triangle tests were conducted in the sensory laboratory using a consumer
panel of 24 panelists, to determine the overall flavor difference between samples
and the control. Each panelist evaluated samples in an individual booth and

recorded the results on the data sheet provided. For each sample set, a series of

104

four triangle tests were presented sequentially and randomly. Each triangle test
contained a total of three specimens of two samples (treatment and control), with
two identical and one “odd” specimen. Panelists were told to determine the odd

specimen, and had to guess even if no difference was apparent.

3.2.2. Rating based on ADSA guidelines performed with a trained panel

Numerical sensory scores of milk flavor (from 1: pronounced off-flavor to
10: no criticism) were obtained from an 8-member trained panel. Evaluation was
performed using the scoring guidelines from the American Dairy Science
Association (Table 8 and Appendix 6.2.2). No criticisms were assigned a score of
“10”. Sixteen milk flavor criticisms were evaluated, and an overall flavor score
assigned based on the existence and intensities of the off-flavor(s). Eight milk
specimens were presented simultaneously in each sample set, and the panelists
were asked to rate the specimens from left to right. Milk from the original one
gallon HDPE containers was provided as a reference, panelists were not asked
to give a sensory score to the control.

Rating the body/texture and flavor of the Cheddar cheese was performed
using a 9-member trained panel, following the ADSA guidelines, which assigned
scores according to the perceived defects of cheese samples (Table 9). Rating
on body/texture (from 1: defective to 5: no criticism) was given based on the
criticisms listed in Table 9. The criticisms “gassy” and “open” were excluded
since they are not expected as a result of light exposure. For flavor, all 13

criticisms were evaluated (from 1: pronounced off-flavors to 10: no criticism).

105

Table 8 Product attributes and scores of the ADSA guideline for scoring off-
flavors in milk (S: slight, D: definite, P: pronounced). No criticism is assigned a
score of 10. Pronounced acid, rancid and unclean off-flavors are not acceptable
and denoted as “ *" [80].

Flavor

Acid

Bitter

Cooked

Feed

Fermented! Fruity
Flat

Foreign

 

#

Garlic/onion
Lacks Freshness
Malty

Oxidized - Light
Oxidized --Metal
Rancid

Salty

Unclean

#AAAmA—Lxl-LU'IOD-F

woohmmmoooitncomcocomwm
’#

am—xwhmxrwwoowoooowao

 

 

 

106

Table 9 Product attributes and scores of the ADSA guideline for scoring flavor
and body/texture defects of Cheddar cheese (S: slight, D: definite, P: pronounced)
[80].

 

 

 

 

 

Flavor S D P
Bitter 9 7 4
Feed 9 8 6
Fermented 7 5 3
Flat/Low Flavor 9 8 7
Fruity 7 5 3
Heated 9 8 7
High Acid 9 7 5
Oxidized 8 6 3
Rancid 6 4 1
Sulfide 9 7 4
Unclean 8 6 3
Whey Taint 8 7 5
Yeasty 6 4 1
Body/1' exture

Corky 4 3 2
Crumbly 4 3 2
Curdy 4 3 2
Gassy 3 2 1
Mealy 4 3 2
Open 4 3 2
Pasty 4 3 1
Short 4 3 2
Weak 4 3 2

 

 

 

107

3.2.3. Difference-from-control tests using a trained panel

Evaluation was performed by a 9-member trained panel. A different-from-
control test using a rating of 0 (no difference) to 5 (extreme difference) was
applied to determine the difference in color, body/texture, and flavor, between the
test samples and the control, i.e. the sample slabs of ~1 cm below the Cheddar

cheese having no light exposure.

3.3. Electronic nose analysis

Headspace volatiles generated from 2% milk, water, packaging materials
(glass, HDPE, HDPE-TiOz, PET bottles, and PE-coated paper cartons) or cheese
samples were analyzed using the electronic nose (Fox 3000, Alpha-MOS,
Toulouse, France) equipped with 12 metal oxide semiconductor sensors
(chamber A: T30/1 , P10/1 , P10/2, P40/1 , 170/2, PA2; chamber C: LY/LG, LYIG,
LYIAA, LY/Gh, LYlgCTl, LY/gCT). Liquid or solid samples were sealed in 10 ml
headspace vials and placed in the sample trays (Figure 30c). The autosampler
was programmed to move the sample vials to the oven (Figure 30b) to generate
headspace volatiles, which were then injected into the injection port (Figure 30e)
of the sensor chamber (Figure 30b) using a 5 ml gas-tight syringe (Figure 30d).

Twelve MOS sensors reacted simultaneously to the injected headspace

sample, which was a mixture of volatiles, which resulted in a change in

 

resistance (R) from the resistance at equilibrium R0. Maximum values of RRRO
0

were recorded for preprocessing and multivariate statistical analysis. Four

replicates of each sample set were analyzed in a semi-random order. Each

108

sample set was independently evaluated twice; one for building a supervised
model and the other for testing model generalization.

Each sample vial was taken directly from -18°C storage and placed on the
tray of the autosampler (HS 100) immediately before each injection. Headspace
was generated at specified temperatures for 15 minutes, and then 2.5 ml of the
static headspace was taken by syringe and directly injected by the autosampler
into the sensor chambers. Sensor responses (the scaled sensor resistance
change, R-Ro, divided by the equilibrium sensor resistance prior to injection, R0)
were recorded and processed using a personal computer and the acquisition

software (Alpha SOFT version 8.0, Alpha-MOS, Toulouse, France).

 

Id)

 

 

 

 

 

 

 

 

 

 

 

 

 

(b) "'

 

 

‘ /

 

(c) :21
(a) 1::

 

 

 

 

 

 

 

 

O 000

 

 

 

 

, ...a /I

 

 

 

 

 

 

 

 

(1')

Figure 30 The electronic nose system (Fox 3000, Alpha-MOS): (a) compressed
air; (b) oven, used to generated headspace volatiles at specified temperatures
and times; (c) sample trays to hold vials; (d) gas-tight syringe; (e) injection port; (f)
sensor chamber containing the 12 MOS sensors; (9) computer, to collect and
analyze data. The parts (b), (c), (d) belong to the autosampler (HS 100).

109

3.4. Solid-Phase Microextraction-Gas Chromatography (SPME-GC)
Headspace analysis was performed using a gas chromatograph coupled

with solid-phase microextraction (SPME) sampling [159]. A 75 pm
Carboxen/Polydimethyl Siloxane (CARIPDMS) fiber, or a 65 pm

PolydimethylsiloxnelDivinylbenzene (PDMSIDVB) fiber was mounted in a manual
holder (Supelco, Bellefonte, PA) and used to collect the headspace volatiles
generated by heating milk or Cheddar cheese specimens in 10 ml headspace
vials, using an oven with precise temperature control (gas chromatograph HP
5890). A Hewlett-Packard gas chromatograph (HP 6890) equipped with FID
detector and integrator (HP 3395) was used to separate the headspace volatiles
desorbed from SPME fibers.

To select the proper analytical condition, a standard solution of 10 nglml of
pentanal, hexanal, dimethyl disulfide, methional (3-methylthiopropionaldehyde),
and 20 nglml of lntemal standard 4-heptanone was prepared inHPLC grade
water and sampled using two different SPME fibers (CARIPDMS and
DVBIPDMS). One ml standard solution was sealed in a 10 ml crimp-top
headspace vial and stored at 5°C. Sampling temperature was controlled using a
refrigerator (5°C) or GC oven (30 and 45°C). Five minutes of preheating was
followed by sampling times at 5, 15 or 25 minutes. A complete randomized
design was applied to 27 specimens, at three sampling temperatures (5, 30 and
45°C) and times (5, 15 and 25 minutes). Three replicates were performed for
each combination. Reproducibility was evaluated by comparing the coefficient of

variation (%CV), which is a measure of the deviation of a variable from its mean,

110

% CV = standard devratron x 100% .

mean

 

Normalization of the peak areas using the peak area of the internal standard [28,

126] was also evaluated.

3.4.1. SPME-GC for milk samples

One ml milk sample was taken using a 1 ml serological glass pipet,
transferred to a 10 ml sealed crimp-top headspace vial, and stored below -18°C
for analysis at a later time. Headspace sampling was performed using a
CARIPDMS fiber at 45°C for 15 minutes with 5 minutes of preheating. The
separation of volatiles from milk was accomplished using a 30 m x 0.32 mm x

0.25 pm HP5 column. Column temperature was initially set at 35°C for 12

minutes, which allowed all target volatiles to pass through the column, and then
heated to 220°C at a rate of 20°Clmin, to clean up the volatiles generated by
degradation of the fiber.

External standards were prepared by spiking analytical grade pentanal,
hexanal and dimethyl disulfide into 2% milk, at levels of 0, 0.5, 1, 2, 4 and 10
nglml. Four replicates of each concentration were analyzed. Three replicates
were used to build the correlation models, i.e. training data, and one replicate
was used to test the model performance. Initial levels of pentanal, hexanal, and
dimethyl disulfide in the 2% milk were estimated by extrapolating the regression
line to the x-axis. Quantification was based on the linear regression equations of
the calibration curves, with the addition of the estimated initial concentration of

the volatile components.

111

3.4.2. SPME-GC for Cheddar cheese samples

Cheddar cheese specimens (disk-shaped solid, 0.15: 0.01 g) were sealed
in 10 ml vials and stored below -18°C for analysis at a. later time. Separation of
the volatiles from Cheddar cheese was accomplished using a 60 m x 0.25 mm x

0.25 pm Supelcowax 10TM column (Supelco, Bellefonte, PA). Column

temperature was set initially at 35°C, and then heated to 220°C at a rate of
2°Clmin. Headspace of cheese samples were generated at 60°C for 15 minutes,
and four replicates were performed for each sample. Volatiles were identified by
comparing the peak retention times of the standards, and the peaks with better
reproducibility were selected for further multivariate analyses. Three replicates
were used to build the correlation models, i.e. training data, and one replicate

was used to test the model performance.

3.5. Light transmission of milk packaging materials

Percent light transmission of the various packaging materials (HDPE,
HDPE-TiOz, PET, and PE-coated paper cartons in white or blue) in the 200-1100
nm range was measured using a UV-visible spectrophotometer (Lambda 25,
PerkinElmer, Wellesley, MA) equipped with an integrating sphere (RSA-PE-20).
Samples were cut into 4 cm x 4 cm pieces, mounted on a sample holder with a

window (id. = 1 cm) and aligned perpendicular to the light path.

112

3.6 Color measurement of cheese

Surface slab (top 3 mm surface) and interior slab (3 mm slices of ~1 cm
below the top surface) color (CIE L* a* b*) of the of the Cheddar cheese exposed
to fluorescent light were measured using a spectrophotometer (Hunterlab
ColorQuest 45°l0° Spectrophotometer, Hunter, VA). Sample slabs packaged in
Nylon/PE bags were placed on top of the sample holder with a window (i.d.=
1 cm ), and the color measured reflectively in a 45°l0° geometry (Figure 31a).

The CIE L*a*b* color space (Figure 31b) is the color standard developed
by the lntemational Commission on Lighting (Commission lntemationale
d‘Eclairage, CIE) in 1976 [187]. It defines the color perceived by human eyes
using three parameters: L* indicates lightness or luminosity, from white to black.
The chromaticity coordinates a* and b* indicate color directions: +a to -a is from
red to green, and +b to -b from yellow to blue, and the center is achromatic, hues
of gray. As the values of a* and b* increase, such that the point moves out from
the center, the chroma or purity of the color increases. The Euclidean distance

between two color points (AE) is

 

AE = J(AL *)2 + (Aa *)2 + (Ab *)2
where AL *, Aa *, Ab * are the differences in lightness, redness and yellowness.

It is a convenient way of presenting color differences between the standard and

the samples [188].

113

Sample

(a)

 

 

 

E Light Source

 

Detector

White
L‘ = 100

lb)

 

Black
L“ = '0

Figure 31 Schematic diagram of (a) 45°l0° geometry of the spectrophotometer
[189]; (b) the CIE L* a* b* color space [187].

114

3.7. Multivariate statistical techniques

3.7.1 Data preprocessing

R-Ro

O

 

Maximum values of the relative sensor resistance changes, max(

).

were recorded and mean centered, i.e. subtracting the average from each sensor
among different samples. Mean centering was applied to account for an intercept
term in the regression models [124]. The mean values of the training sets were

used for mean centering of both training and test sets.

3.7.2. Unsupervised learning techniques

Hierarchical clustering analysis (HCA) and principle component analysis
(PCA) were performed using PROC CLUSTER and PROC PRINCOMP
procedures (SAS 8.0, SAS Institute, Cary, NC), respectively. In HCA, single
linkage (nearest neighbor) was used as the algorithm for similarities, with the
computation of eigenvalues and normalizing of distances suppressed. Results of
HCA were presented in a dendrogram, where each step in the clustering process
is illustrated by a join of the tree. In PCA, principle components were calculated
from the covariance matrix, and scatter plots of the first two or three principle
components were generated for visual inspection of the data points. The scree
plots (PROC FACTOR) may also be plotted to examine the fraction of total

variance in the data as explained or represented by each principle component.

115

3.7.3. Supervised Ieaming techniques

Linear/quadratic discriminant function analysis (LDNQDA), and k-nearest
neighbor (k-NN) were performed using PROC DISCRIM procedure (SAS 8.0,
SAS Institute, Cary, NC). In LDA/QDA, “testing of the equality of normal
population parameters” [180] was carried out prior to the analysis. If there is a
strong evidence of unequal variances among the groups, QDA is expected to
have a better performance of defining the decision boundaries. On the other
hand, LDA is used to separate classes that are normally distributed with equal
covariance. A parametric method, based on a multivariate normal distribution
within each class, was used to derive the LDA or QDA. Models were cross-
verified by the leave-one-out method, i.e. each observation was classified using a
discriminant function computed from the other observations in the training set. An
independent test data set was used to test the model generalization. Canonical
discrimination plots with the first two or three LDA discriminants were generated
for visual inspection of the sample discrimination. Nonparametric classification
was based on a k-NN algorithm (k s number of replicates), i.e. no assumption of
multivariate normal distributed data was required. Cross-validation and testing of
the model generalization was performed using similar procedures as used in
LDA/QDA analyses.

The correct identification rates (i.e. hit rates) were calculated at the steps
of training (model building), validation (cross validation of training set using
leave-one-out method), and testing (applying an independent test data set).

Correct identification rates of training data are usually the highest for the

116

“training” step, as the models are built using the same data set. The “validation”
step, which is used to verify the model stability, may have lower correct
identification rates: cross validation was performed by leaving out one data point
at a time and verifying its “identified” group using the reduced models built by the
rest of the training data set. The “test” step is applied to test the model
generalization, i.e. how well the model works when applied to an independent
data set. It may have either a higher or lower correct identification rate than the
validation step. A model is considered to be unstable if the correct identification
rate is extremely high for training but low for validation, or to be “optimistic” when

the rate is high for training but low for the test step.

3.7.4. Quantitative analyses

Partial least squares analysis (PLS) was performed using PROC PLS
(SAS 8.0, SAS Institute, Cary, NC). The singular value decomposition (SVD)
algorithm was used to compute extracted PLS factors. Models were verified
using cross validation (leave—one-out method in training set) for PLS, along with
the test set validation. A SAS macro “plsplot.sas” (available at
http:/Iwww.sas.com) was used to generate scatter, loading and residual plots for
visual inspection of model performance.

Multilayer perceptrons (MLP) was performed using the Neural Network
Toolbox 4.0 of Matlab 6.1 (The MathWorks Inc., Natick, MA). A feed-forward
backpropagation network was constructed, with a tan-sigmoid function in the

hidden layer, and a linear function in the output layer (Figure 32). An independent

117

data set was applied for validation (early stopping to avoid over-fitting) as well as
for testing (estimating a network’s ability to generalize), with the following training

parameters (Figure 33).

 

m
eyc c744»
m m

12 12 1

 

 

 

Figure 32 The feed-forward backpropagation network with 12 input nodes,
which correspond to the mean-centered 12 sensor responses), and one output
node, i.e. numerical sensory score (figure modified from Matlab output).

 

 

 

 

 

 

 

 

 

 

1 Training Parameters E E
epochs 100 mu_dec 0.1
goal 0 mu_inr: 10
max_fai| 5 mu_max 10000000001 ‘
mem_reduc 1 show 25
min_grad time Inf
mu

 

 

 

 

Figure 33 Training parameters of the feed-forward backpropagation network
(figure modified from Matlab output).

118

Root mean square error (RMSE) was used to evaluate the performance of
the models. RMSE values are determined by calculating the deviations of
predicted responses from their expected ones, summing up the measurements,

and then taking the square root of the sum:

 

i(fi(xi)-F(Xi))2

RMSE: i .
n

 

where E(xi) is the predicted response based on the model, F(x,-) is the

expected response, n is the number of specimens. The smaller the RMSE, the

better the performance of the model.

119

CHAPTER 4
RESULTS AND DISCUSSION

4.1. Light-oxidized off-flavors in 2% milk
4.1 .1 . Sensory evaluation

Using a 24-member consumer panel, a series of four triangle tests were
conducted sequentially, to determine if milk exposed for 2, 4, 8, or 12 hours could
be differentiated from the control, i.e. milk stored in aluminum foil-wrapped glass
bottles (Table 10). It showed that light exposure (1000 Ix, 5°C) resulted in a
subtle olfactory change in reduced fat milk after 4 hours (p < 0.20). A significant
overall difference was detected after 8 hours (p < 0.05), and 13 out of 24
consumer panelists had the correct responses. The flavor change was more
noticeable after 12 hours (p < 0.01).

The “occurrence” of light-oxidized off-flavors in milk is dependant on the
light exposure conditions and sensory techniques. The light-oxidized off-flavors
were reported to appear in milk as soon as 15 minutes to 12 hours (Chapter
2.1.3, [6-8, 54, 60, 62]). Fluid milk in clear transparent packages has been shown
to be highly susceptible to consumer-detectable light-induced quality changes
due to retail display. Fluid milk is often displayed for times ranging from at least 8

hours [6, 9] to 2-3 days [10].

120

Table 10 Triangle test results on light-oxidized 2% milk in glass bottles against
control, i.e. 2% milk milk stored in aluminum foil-wrapped glass bottles. Tests
were performed by a 24-member consumer panel.

 

 

Duration of light Number of correct responses from
exposure 24 subjects p
2 hours 9 —*
4 hours 11 0.20
8 hours 13 0.05
12 hours 15 0.01

 

Milk samples with differing levels of light oxidation were then evaluated by
an 8-member trained panel, based on ADSA guidelines. The test was performed
in a semi-ascending sequence, such that samples were presented to the
panelists from short to long light exposure times, to avoid the carry-over effect of
light-oxidized off-flavors. The sensory scores of 2% milk in glass bottles
decreased with increase in the duration of light exposure, to a Score of ~5.5 in 24
hours (Table 11 and Figure 34). Compared to the control sample (no light ‘
exposure), the decrease in sensory scores of the milk samples was not
statistically significant until after 36 hours of light exposure (Table 11). However,
the sensory scores of 2% milk decreased after 4 hours of light exposure and
were further reduced in milk exposed to light for 8 hours or longer (Figure 34).
The variances were significantly greater for milk samples exposed to light for 8
hours and longer, which was evidence of the off-flavor “detection” on the part of
the trained panel. Increased variance among different treatments limits the
application of analysis of variance (ANOVA) and multiple comparisons (e.g.

Tukey’s studentized range test) to indicate a change in sensory scores. The

121

ADSA guidelines are designed to monitor various off-flavors in milk from different
sources and not specific for light-oxidized off-flavor. Milk samples with “slight”,
“definite” or “pronounced” light-oxidized off-flavors should receive a score of “8”,
“6" or “3”, respectively (chapter 3.3.3). Oxidized but not highly oxidized milk
samples tended to receive a “6” as they contained a “definite” but not quite
“pronounced” off-flavor. Thus similar ratings may be given to milk samples with
slightly different intensities of light oxidized off-flavors. Due to the large variation
and more “clustered” rating scale, light-oxidized quality change was not
statistically significant until after 36 hours of light exposure (1000 Ix), although the
detection of off-flavors by some members of the trained panel was observed in 4
or 8 hours.

The consumers picked up a moderate light-oxidized flavor change after 4
hours of fluorescent light exposure (1000 Ix, 5°C), and the change was significant
after 8 hours and more pronounced after 12 hours. The trained panel gave
significantly lower sensory scores to milk exposed to light for 36 hours or longer,
although the reduction in average sensory scores and increased sensory score

variances indicated that the off-flavor development was occurring in 4 or 8 hours.

122

Table 11 Sensory scores of light-oxidized 2% milk based on ADSA guidelines.
Scores were given by an 8-member trained panel.

 

Duration of light S ‘
ensory score

 

exposure (hours)

0 8.50 10.54 a‘

2 8.38 11.06 a,b
4 7.75 10.89 a, b,c
8 6.63 11.60 a, b,c
12 6.38 12.20 a, b,c
24 5.63 12.93 a, b,c
36 5.38 12.62 c

48 5.50 11.77 b,c

 

‘ Means with the same letter are not statistically different at a=0.05 (T ukey’s
studentized range test).

 

€ng

7...
6— ar- -x- A
5- _
4...
3-
2..
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FLAVOR SCORE

..E

—-IJ°
I

 

 

 

r I l I I I
D 2 4 B 12 24

TIME (HOURS)

8—
3

Figure 34 Flavor sensory scores based on ADSA guidelines of 2% milk in glass
bottles exposed to 0, 2, 4, 8, 12, 24 or 48 hours of fluorescent light (1000 Ix, 5°C).
.; mean values of each treatment; 1: possible outliers.

123

4.1.2. Headspace analysis using SPME-GC
4.1.2.1. Optimizing the headspace sampling parameters

Two different SPME fibers (CARIPDMS and DVBIPDMS) were used to
collect the headspace volatiles of the standard aqueous solution (pentanal,
hexanal, dimethyl disulfide, methional, and 20 nglml of internal standard 4-
heptanone) at 5, 30 or 45°C for 5, 15 or 25 minutes. Pentanal, hexanal, dimethyl
disulfide and internal standard 4-heptanone can be clearly identified and
quantified from the GC chromatograms, while the peak for methional had very
poor resolution and reproducibility (data not shown). Methional is not stable in
aqueous solutions and tends to decompose to lower molecular weight sulfur
compounds such as methyl mercaptan and dimethyl sulfide [1, 20]. Preliminary
testing showed that volatized methional can be collected using a CARIPDMS
fiber, along with decomposed products including dimethyl disulfide.

High temperature may increase SPME volatile adsorption by increasing
the headspace volatile content, or decreasing the adsorbtion by reducing the
partition coefficient of volatiles between the fiber and vial headspace. Sampling
temperatures (5, 30 and 45°C) had the opposite effect on CARIPDMS and
DVBIPDMS fibers, such that higher temperature resulted in more volatile
adsorption by the CARIPDMS fiber (Figure 35a, b and c), but less volatile

adsorption by the DVBIPDMS fiber ((Figure 35, fand g).

124

100000 -

80000 -

60000 -

40000 .

Area response

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20000 -

 

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120000 1
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80000 .
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Area response

IN

20000 -

 

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10 15 20 25
Sampling Time (min)

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30000 1

20000 -

Area response

10000 ~

 

5 10 15 20 25
Sampling Time (min)

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Sampling Time (min)

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12000 1
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4000 -
2000 -

 

5 10 15 20 25 30
Sampling Time (min)

(9)
m

 

8000

6000 'I

Area response

N «b
O O
O O
O O
l l

5 10 15 20 25 30
Sampling Time (min)

(f)

 

 

Sampling Time (min)

Figure 35 Area responses for (a) pentanal, (b) hexanal and (c) dimethyl disulfide
sampling using CARIPDMS, or DVBIPDMS fiber (d, e, f, respectivelY). at different
sampling temperatures (9: 5°C, 0: 30°C, A: 45°C). Error bars show the

standard deviations of three replicates.

Table 12 Coefficient of variation (%CV) for the CARIPDMS fiber used to quantify

volatiles in the standard aqueous solution.

 

Temperature Time

Without Normalization

Normalized Using LS.‘

 

 

(°C) (mm-I PEN HEX DMDS PEN HEX DMDS
5 5 11.50 21.19 20.22 19.96 12.99 16.43
15 10.95 11.54 24.72 24.21 21.96 10.67
25 25.23 38.27 35.71 20.37 11.43 15.74
30 5 2.01 3.45 11.66 13.76 9.24 16.35
15 3.43 6.92 9.57 4.77 3.22 16.32
25 5.34 3.17 6.66 3.30 4.53 6.50
45 5 6.71 5.41 16.09 5.07 4.26 16.07
15 3.55 0.68 12.03 5.36 6.13 9.65
25 2.24 9.55 22.72 16.00 10.61 12.16

 

‘ normalized using the area response of the internal standard 4-heptanone

Table 13 Coefficient of variation (%CV) for the DVBIPDMS fiber used to quantify

volatiles in the standard aqueous solution.

 

 

 

Temperature Time Without Normalization Normalized Using LS.1
(°C) (mln.) PEN HEX DMDS PEN ' HEX DMDS
5 5 5.69 2.10 21.60 2.57 2.35 19.13

15 6.75 4.70 6.99 12.30 2.38 4.91
25 35.67 25.11 31.24 39.64 6.61 12.67
30 5 20.95 23.40 37.63 17.54 9.97 21.96
15 15.63 6.16 7.15 11.76 10.59 9.19
25 16.79 6.97 10.06 22.48 11.73 10.61
45 5 17.40 19.70 70.28 34.16 11.23 59.66
15 9.09 17.52 16.73 16.76 9.37 6.05
25 44.1 1 20.64 6.53 29.62 26.99 6.54

 

fir normalized using the area response of the internal standard 4-heptanone

126

Sampling methods with higher adsorption may provide better sensitivity
and reproducibility (lower %CV if the standard deviations are similar). The
CARIPDMS fiber, which is designed to analyze tracevolatiles and to have high
affinity to volatiles in general, had much higher area responses than the
DVBIPDMS fiber (Figure 35).

The amounts of pentanal and dimethyl disulfide absorbed by the
CARIPDMS fiber were closer to the equilibrium at higher temperatures (30°C and
45°C), as the increments of adsorbed volatiles over time were decreasing (Figure
35), while the adsorption was still linearly dependent on sampling time at 5°C.
More time was required for hexanal adsorption to reach equilibrium. SPME may
be used as a pre-equilibrium or an equilibrium sampling technique. If the
sampling time is long enough for a system (among liquid phase, headspace, and
fiber) to reach equilibrium, the area response will not increase with sampling time
(Figure 16). In general, the higher the sampling temperature, the sooner the
system reaches equilibrium. With accurate timing, quantification using SPME is
repeatable, even though equilibrium is not reached. Therefore, sampling using
the CARIPDMS fiber at 45°C for 15 minutes was selected to be used in this study
based on its more reproducible pentanal and hexanal contents, i.e. lower %CV
(Table 12).

The use of an internal standard, 4-heptanone, was suggested [28, 126] to
improve reproducibility. However, normalizing the area responses of pentanal,
hexanal, and dimethyl disulfide with the internal standard did not always reduce

the coefficients of variation (Table 12 and Table 13). The amount of 4-heptanone

127

absorbed varied similarly as did the other volatiles, and the variation was not
always cancelled out by normalization. Different volatiles have different affinity to
SPME fibers, which can also be affected by the other volatiles in the system.
Since the internal standard did not always improve the reproducibility of the
process, it was eliminated to simplify the sample preparation and to avoid

unnecessary variation.

4.1.2.2. Quantifying the headspace volatiles of light-oxidized milk

Under fluorescent light exposure (1000 Ix, 5°C), pentanal content
increased rapidly in 2% milk over time, while no significant increase in pentanal
occurred in samples in the dark (Figure 36). It has been reported that [28, 190]
formation of pentanal increased with a second-order polynomial correlation, and
that riboflavin plays an important role in the formation of pentanal. Hexanal
content also increased with increased exposure time, but not as rapidly as
pentanal during the first 12 hours. Marsili (1999) [28] exposed 2% milk to 200 ft-c
(2152 Ix) fluorescent light in half-gallon (1.89 L) HDPE jugs, and found that the
amount of pentanal and hexanal continuously increased, with a more rapid
increase of pentanal for the first 12 hours.

Hexanal, a secondary product of lipid oxidation, has often been used as
an indicator of lipid oxidation. In this study 2% milk exposed to light for 4 hours
had a very mild flavor change (Table 10), and the hexanal level was 2511.0
nglml (i.e. ppm). The light-oxidized off-flavor was detected by consumers when

light exposure time reached 8 hours (p < 0.05), whereas the hexanal content

128

remained at about the same level, 2511.1 nglml (Figure 36). Pentanal, which is
also a secondary oxidative product of lipid oxidation but is generated via different
mechanisms than hexanal, had concentrations of 3710.6 and 6.2105 nglml in
2% milk exposed to light for 4 and 8 hours, respectively. Lee (2002) [190] found
that the antioxidants ascorbic acid and BHA (hydrogen atom donors as
antioxidants) decreased the formation of pentanal but that sodium azide (singlet
oxygen quencher) increased the formation of pentanal. Both antioxidants and the
singlet oxygen quencher reduced the formation of hexanal and heptanal.

Dimethyl disulfide has been reported as being mainly responsible for the
light oxidized off-flavors in skim milk, as a major byproduct from milk protein
deterioration [20]. However, the expected increase in dimethyl disulfide was not
detected (Figure 36). Marsili (1999) [28] was not able to quantitatively determine
the concentration of dimethyl disulfide, since the dimethyl disulfide peak
response was very small on the GC chromatograms, obtained using a
CARIPMDS fiber or Tenax trap system coupled with a thermal desorption unit for
headspace sampling. Conversely, Jung et al. (1998) claimed that in skim milk,
dimethyl disulfide content increased significantly within 8 hours of light exposure
(2400 lx, 20°C) in a 150 ml Erlenmeyer flask. He quantified the amount of DMDS
using a Tenax trap coupled with a thermal desorption unit for headspace

sampling.

129

  
  

.5
N
1

Concentration (nglml

 

 

 

4H +. a
0 12 24 36 48
Time (hours)

12 - (b)

 

Concentration (nglml) ‘

 

o l I l l I

 

0 12 24 36 48 .
Time (hours)

12 fl (0)

Concentration (nglml)
o0

 

 

Time (hours)

Figure 36 The amount of (a) pentanal (b) hexanal, and (c) dimethyl disulfide in
2% milk with (O) and without (6) light exposure for 0 to 48 hours. Error bars
show the standard deviations of three replicates.

130

4.1.3. Headspace analysis using the electronic nose

Figure 37 shows the profiles of sensor response over time, to the 95°C
headspace samples of 2% milk exposed to fluorescent light for 0 or 48 hours.
The differences between sensor profiles of the two samples were not distinct and
difficult to visually justify by comparing the 12 MOS sensor responses directly.

R-Ro

O

 

Therefore the steady-state parameter, max( ), was recorded (Figure 12)

and analyzed using multivariate statistical methods for pattern recognition
(Chapter 3.7).

Unsupervised learning techniques, hierarchical clustering analysis (HCA)
and principle component analysis (PCA), did not show an obvious pattern or
separation among the headspace volatiles generated at 45, 70 or 95°C, for 2%
milk exposed to light for 0, 2, 4, 8, 12, 24, 36 or 48 hours (Figure 38, Figure 39
and Figure 40). Although an unclear pattern with few “outliers” was observed in
the 45°C headspace sample set (Figure 38), the milk samples exposed to 36 and
48 hours of fluorescent light were partially clustered within the group in HCA as
well as PCA. However, it was difficult to define a clear separation boundary.

The first two principle components (PC1 and PC2) were able to explain
88.63% (i.e. 52.44% + 36.19%), 94.21%, and 92.14% of the total data variation
of the 12 sensor responses for 45, 70, and 95°C headspace volatiles,
respectively. That is, after projecting the 12 data dimensions into the two
dimensional space constructed by the PC1 and PC2, most of the total variance

was preserved in the two dimensional models.

131

 

(a) 1.04,

 

 

 

 

 

 

0 100 200 ‘ 300 400 500 600 700 0
Time (sec)

 

 

 

 

 

 

 

 

0 '100'200'300'400'500'600'700'600
Time (sec)

R—Ro

R0

of 2% milk (a) without (b) with 48 hours of light exposure at 5°C.

Figure 37 Sensor responses (

 

) of the electronic nose to 95°C headspace

132

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Figure 38 (a) HCA and (b) PCA of the sensor responses for 45°C headspace of
2% milk exposed to 0, 2, 4, 8, 12, 24, 36, or 48 hours of light at 5°C. Sample “d”
was stored in the dark for 48 hours.

133

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Figure 39 (a) HCA and (b) PCA of the sensor responses for 70°C headspace of
2% milk exposed to 0, 2, 4, 8, 12, 24, 36, or 48 hours of light at 5°C. Sample “d”
was stored in the dark for 48 hours.

134

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Figure 40 (a) HCA and (b) PCA of the sensor responses for 95°C headspace of

2% milk exposed to 0, 2, 4, 8, 12, 24, 36, or 48 hours of light at 5°C. Sample “d”
was stored in the dark for 48 hours.

135

Supervised Ieaming techniques (LDA/QDA and k-NN) were applied to
discriminate the sensor responses for 45, 70 and 95°C headspace volatiles of
light oxidized 2% milk (Figure 41, Figure 42, and Figure 43). Discriminatory
methods were evaluated by comparing the correct classification rates (i.e. hit
rates) to the training, validation and test steps. As discussed in Chapter 2.6.2, the
LDA linear approach is more applicable for data with equal group variances,
while QDA may work better for data with unequal group variances (Figure 19).
The equality of normal population parameters was tested, and the results

p > 0.05 (data not shown) indicated there was no strong evidence of unequal

group variances. LDA had a comparable or higher correct identification rate than
QDA at the test steps (Figure 41, Figure 42, and Figure 43). The QDA
identification rates were higher for training and lower for the validation and test
steps, which implied that QDA models might be too optimistic; the quadratic
functions were not necessary for separating groups with equal variances and
therefore more classification mistakes occurred in predicting the group identities
of the unknown samples at the test steps.

Among the k-NN methods with various k, 1-NN had the most correct
identification rate at the test step. When unknown samples were subjected to the
model, their identities were determined by their closest one “neighbor” in the
training data. In 2-NN, 3-NN or 4-NN, taking the closest two, three or four points
did not improve the identification. It is mainly the occurrence of “ties” that
prevents better identification, thus the model is not able to identify the unknown

sample if the two “neighbors” belong to different groups in 2-NN. This also

136

implies that the groups were fairly close to each other in the models, so counting
more “neighbors” resulted in lower correct identification rates.

Only one third or less of the light-oxidized milk samples in the test data set
were correctly identified in terms of their exact light exposure duration. Poor
differentiation among the milk samples with similar extent of light oxidation was

mainly responsible for the low identification rates.

 

1.0 ,3 A —e—Training
0.9 . / +Validatlon
‘08~ --G--Test
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LDA QDA 1-NN 2-NN 3-NN 4-NN

Discrimination Method

 

 

 

 

 

 

 

 

Figure 41 Classification rates for discrimination methods applied to the sensor
responses using a headspace temperature of 45°C for 2% milk exposed to 0, 2,
4, 8, 12, 24, 36, or 48 hours of light, using LDA, QDA, and k-NN for k = 1, 2, 3,
and 4.

137

 

—6— Training

 

 

1.0 A A
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LDA QDA 1-NN 2-NN 3-NN 4-NN
Discrimination Method

 

 

 

 

Figure 42 Classification rates for discrimination methods applied to the sensor
responses using a headspace temperature of 70°C for 2% milk exposed to 0, 2,
4, 8, 12, 24, 36, or 48 hours of light, using LDA, QDA, and k-NN for k = 1, 2, 3,
and 4.

 

+ Training

3'2 q <3/V +Validation

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g 0.4 «
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LDA QDA 1-NN 2-NN 3-NN
Discrimination Method

 

 

 

 

 

 

 

Figure 43 Classification rates for discrimination methods applied to the sensor
responses using a headspace temperature of 95°C for 2% milk exposed to 0, 2,
4, 8, 12, 24, 36, or 48 hours of light, using LDA, QDA, and k-NN for k = 1, 2, and
3.

138

The LDA models can be graphically displayed as canonical discrimination
scatter plots (Figure 44a, Figure 45a, and Figure 46a). Independent test data
sets were then evaluated by the models (Figure 44b, Figure 45b, and Figure 46b).
The first two canonical discriminants CAN1 and CAN2 were able to explain
86.20% (i.e. 71.23% + 14.97%), 78.75% and 91.53% of the total variances of the
12 sensor responses for the 45, 70 and 95°C headspace volatiles of the light
oxidized milk, respectively, which means most of the total variance was
preserved in the two dimensional models constructed by CAN1 and CAN2. Milk
samples with the same duration of light exposure were not differentiated
completely, but samples with similar extent of light oxidation still tended to be
closer to each other. This trend was more obvious at 95°C (Figure 46) rather than
70°C or 45°C.

The goodness of the model generalization, i.e. the ability of the models to
be applicable for unknown sample prediction, can be evaluated by comparing the
group regions on the discriminant scatter plots at the training and test steps. The
95°C training and test data had a more consistent group distribution. The test
data belongs to the same group (i.e. same light exposure duration) as was
projected as shown by their similar group territories, compared to the 45°C and
70°C samples. For both training and test data, the groups arranged along the
CAN1 coordinate with the levels of light exposure (Figure 46). It reflected the fact
that all sensors responded to the light oxidized volatiles in a similar way, the

more volatiles, the higher the sensor responses.

139

(a) Training

 

 

 

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Figure 44 Canonical discriminant scatter plots of the (a) training (b) test data
sets. Headspace of 2% milk samples exposed to 0, 2, 4, 8, 12, 24, 36, or 48
hours of light was generated at 45°C.

140

(a) Training

 

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Figure 45 Canonical discriminant scatter plots of the (a) training (b) test data
sets. Headspace of 2% milk samples exposed to 0, 2, 4, 8, 12, 24, 36, or 48
hours of light was generated at 70°C.

141

(a) Training

 

 

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Figure 46 Canonical discriminant scatter plots of the (a) training (b) test data
sets. Headspace of 2% milk samples exposed to 0, 2, 4, 8, 12, 24, 36, or 48
hours of light was generated at 95°C.

142

The data were then evaluated by “pooling” the results. Instead of
assigning off-flavor samples to their exact light exposure duration, a “harmful light
exposure time” was defined as the “threshold” point for milk deterioration, i.e. the
differentiation between the two categories “good” and “bad” which was then
tested. For instance, the LDA or 1-NN model based on the 45°C headspace
samples was able to recognize 89% of 2% milk exposed to light for 24 hours or
longer at the test step (Figure 47). The LDA and 1-NN models based on the 70°C
headspace samples identified 83% of the 2% milk exposed to light for 24 hours
or longer (Figure 48). More importantly, the LDA model based on 95°C
headspace samples correctly recognized 97% of the 2% milk exposed to light for
8 hours or longer (Figure 49), which had comparable sensitivity to the consumer
triangle sensory testing (Table 10).

The electronic nose provided better discrimination at 95°C than at 70°C
and 45°C. Higher headspace temperature increased the amount of volatiles and
resulted in higher sensitivity and better discrimination. Although undesired
volatiles may also be generated due to overheating the milk, it will not be an
issue if the volatiles from overheated milk are consistent or generated in a similar

manner.

143

(‘1 LDA ((1)2-NN

 

 

   

 

 

 

 

 

 

 

 

   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Threshold of Harmful Light Exposure (hours) Threshold of Hannful Ught Exposure (hows)

Figure 47 Classification rates of the defined threshold of harmful light exposure
for discrimination methods applied to the sensor responses using a headspace
temperature of 45°C using LDA, QDA, and k-NN for k = 1, 2, 3, and 4.

144

(I) LDA

 

 

 

 

 

 

 

 

 

 

 

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Threshold of Harmful Light Exposure (hours)

 

 

 

 

 

2 4 8 12 24 36 48
Threshold of Hannful Light Exposure (hours)

Figure 48 Classification rates of the defined threshold of harmful light exposure
for discrimination methods applied to the sensor responses using a headspace
temperature of 70°C using LDA, QDA, and k-NN for k = 1, 2, 3, and 4.

145

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

(a)LDA (d)2-NN
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Threshold of Harmful Ught Exposure (hours)

Threshold of Harmful Light Exposure (hours)

 

 

 

 

 

 

Figure 49 Classification rates of the defined threshold of harmful light exposure
for discrimination methods applied to the sensor responses using a headspace
temperature of 95°C using LDA, QDA, and k-NN for k = 1, 2, 3.

146

4.1.4. Correlation of results from sensory and instrumental analyses

Since light oxidized milk received lower sensory scores and had higher
levels of oxidative byproducts in the headspace, a negative correlation is
expected between sensory quality and headspace volatiles, analyzed using the
electronic nose or SPME-GC. The goal is to set up an instrumental method which
can be applied in a rapid quality evaluation of milk and which has high correlation
to sensory scores.

Headspace pentanal, hexanal, and dimethyl disulfide in 2% milk were
quantified using SPME-GO. Correlation between their headspace volatile
contents and the numerical sensory scores was investigated using PLS (Figure
50) and MLP (Figure 51). The predicted sensory scores and the root mean
square error (RMSE) were used to evaluate the performance of the models. The
smaller the RMSE, the better the performance of the model.

The MLP model, which was based on a non-linear backpropagation
algorithm, gave more precise predicted sensory scores and lower root mean
square error (RMSE) than the PLS model. The PLS model is a linear statistical
method and was used to find latent component(s) of the independent variables,
i.e. headspace levels of pentanal, hexanal, and dimethyl disulfide.

Similar RMSE values at the training and test steps in both models

indicated the models were not over-fitted.

147

 

(a) Training (RMSE = 0.4434) 8

PREDICI'ED FLAVOR SCORE
N

 

 

 

 

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5 6 7 6 9

ACTUAL FLAVOR SCORE

 

 

 

 

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5 6 7 6 9

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Figure 50 PLS predicted versus actual flavor scores of 2% milk exposed to 0, 2,
4, 8, 12, 24, 36, or 48 hours of light, based on levels of pentanal, hexanal and
dimethyl disulfide quantified using SPME-GC. Models were applied to (a) training
data and (b) test data. The reference line indicates equal values of predicted and
actual sensory scores.

148

 

 

 

 

 

 

 

 

 

 

9i (a) Training (RMSE = 0.1951)
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Figure 51 MLP predicted versus actual flavor scores of 2% milk exposed to 0, 2,
4, 8, 12, 24, 36, or 48 hours of light, based on levels of pentanal, hexanal and
dimethyl disulfide quantified using SPME-GO. Models were applied to (a) training
data and (b) test data. The reference line indicates equal values of predicted and
actual sensory scores.

149

PLS and MLP models were also used to investigate the correlation
between sensory scores and electronic nose sensor responses for the 45, 70
and 95°C headspace samples of light oxidized 2% milk (Figure 52 to Figure 57).
It showed that the electronic nose was not able to provide an effective prediction
of sensory scores at 45°C or 70°C headspace, whether the applied model was
PLS or MLP. The 45°C or 70°C headspace temperature was not high enough to
generate sufficient volatiles in the headspace and resulted in low sensitivity and
poor prediction, regardless of the applied multivariate technique.

On the other hand, the sensor responses for 95°C headspace samples
resulted in a relatively more precise prediction (Figure 54 and Figure 57). The
95°C PLS model (RMSE = 0.3828) had predicted sensory scores varied with a
range ~1, which increased slightly but was still reasonable when applying to the
test data set (RMSE = 0.5470). The 95°C MLP model had a low RMSE (0.0040)
and narrower ranges on predicted sensory scores, compared to the 95°C PLS
model (Figure 54a) at the training step. However, it gave a poor prediction at the
test steps (RMSE = 1.0748) which indicates over-fitting by the MLP model.
Training loops based on a backpropagation algorithm may have proceed too long,
which resulted in an over-fitted model. The MLP model was too optimistic for the
training data and failed to be adapted for an independent data. The model did not

cover the reasonable variation among future unknown samples.

150

PREDICTED FLAVOR SCORE

PREDICTED FLAVOR SCORE

 

9 3 (a) Training (RMSE = 0.6968)

 

 

 

 

 

    
 
   
 
   

 

 

 

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151

Figure 52 PLS predicted versus actual flavor scores of 2% milk exposed to 0, 2,
4, 8, 12, 24, 36, or 48 hours of light, based on the sensor responses using a
headspace temperature of 45°C. Models were applied to (a) training data and (b)
test data. The reference line indicates equal values of predicted and actual
sensory scores.

 

(a) Training (RMSE = 0.9204)

PREDICTED FLAVOR SCORE

 

 

 

 

erTIIITTIlIITIIIIII'ITIIIIIIITIIIIIIIII'

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ACTUAL FLAVOR SCORE

 

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PREDICT ED FLAVOR SCORE
N

46

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5 6 7 6 9
ACTUAL FLAVOR SCORE

 

 

 

Figure 53 PLS predicted versus actual flavor scores of 2% milk exposed to 0, 2,
4, 8, 12, 24, 36, or 48 hours of light, based on the sensor responses using a
headspace temperature of 70°C. Models were applied to (a) training data and (b)
test data. The reference line indicates equal values of predicted and actual
sensory scores.

152

 

 

 

 

 

 

 

 

 

91
; (a) Training (RMSE = 0.3626)
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Figure 54 PLS predicted versus actual flavor scores of 2% milk exposed to 0, 2,
4, 8, 12, 24, 36, or 48 hours of light, based on the sensor responses using a
headspace temperature of 95°C. Models were applied to (a) training data and (b)
test data. The reference line indicates equal values of predicted and actual
sensory scores.

153

 

(a) Training (RMSE = 0.6906)

PREDICTED FLAVOR SCORE
N

 

 

 

 

ITTIIIIIITIIIIIIIIIIIIIIIITIIIrIIITIIIII

5 6 7 6 9
ACTUAL FLAVOR SCORE

 

(b) Test (RMSE = 1.2620)

 

PREDICTED FLAVOR SCORE

 

 

 

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5 6 7 6 9
ACTUAL FLAVOR SCORE

Figure 55 MLP predicted versus actual flavor scores of 2% milk exposed to 0, 2,
4, 8, 12, 24, 36, or 48 hours of light, based on the sensor responses using a
headspace temperature of 45°C. Models were applied to (a) training data and (b)
test data. The reference line indicates equal values of predicted and actual
sensory scores.

154

 

 

 

 

 

9 3 (a) Training (RMSE = 0.3722)

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Figure 56 MLP predicted versus actual flavor scores of 2% milk exposed to 0, 2,
4, 8, 12, 24, 36, or 48 hours of light, based on the sensor responses using a
headspace temperature of 70°C. Models were applied to (a) training data and (b)
test data. The reference line indicates equal values of predicted and actual
sensory scores.

155

 

é (a) Training (RMSE = 0.0040)

PREDICTED FLAVOR SCORE

 

 

 

 

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5 6 7 6 9
ACTUAL FLAVOR SCORE

 

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Figure 57 MLP predicted versus actual flavor scores of 2% milk exposed to 0, 2,
4, 8, 12, 24, 36, or 48 hours of light, based on the sensor responses using a
headspace temperature of 95°C. Models were applied to (a) training data and (b)
test data. The reference line indicates equal values of predicted and actual
sensory scores.

156

The PLS approach was to find latent component(s) from the sensor
responses that were also relevant to the sensory scores. It was expected that
some of these would be well defined since higher volatile contents or sensor
responses were expected from light-oxidized samples with lower sensory scores.
The MLP was a better model than PLS when over-fitting was not a concern. The
models, developed based on training data, should cover potential variation in
both training and future unknown samples. This usually depends on the

reproducibility of the instrumental measurements or the selection of training data.

4.2. Packaging and light-oxidized off-flavors
4.2.1. Off-flavors from packaging materials

The milk packaging materials, glass, HDPE, HDPE-TiOz, PET, and PE-
coated paper cartons, were cut into ~1 cm2 pieces, sealed in 10 ml headspace
vials, and analyzed using the electronic nose. The sensor responses for the 45,
70 or 95°C headspace samples were then analyzed using the unsupervised
(HCA and PCA) and supervised (LDA/QDA and k-NN) Ieaming techniques.

A natural clustering and clear separation among various packaging
materials were observed on HCA dendrograms and PCA score plots (Figure 58
to Figure 60). PE-coated paper carton (denoted as “P”) headspace samples were
more separated from glass, HDPE and HDPE-TiOz samples at all headspace
temperatures. This implies that PE-coated carton material, which contains
additives from both paper and PE layers, had a distinctively different headspace

volatile composition than that of the glass and plastic materials.

157

PC2 (0.30%)

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Figure 58 (a) HCA and (b) PCA of sensor responses for 45°C headspace of
various packaging materials: glass (G), HDPE (H), HDPE-Ti02 (T), PET (E), and
PE-coated carton material (P).

158

(a)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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(b)

 

 

 

PC2 (24-72%)

 

 

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PCI(74.91%)

Figure 59 (a) HCA and (b) PCA of sensor responses for 70°C headspace of

various packaging materials: glass (G), HDPE (H), HDPE-TiOz (T), PET (E), and
PE-coated carton material (P).

159

0.6“

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PC1(91.50%)

Figure 60 (a) HCA and (b) PCA of sensor responses for 95°C headspace of
various packaging materials: glass (G), HDPE (H), HDPE-TiOz (T), PET (E), and
PE-coated carton material (P)

160

Supervised Ieaming techniques, LDA/QDA and k-NN for k = 1 to 4, were

used to build differentiation and recognition models (Figure 61 to Figure 63). The

training data did not have significant evidence of unequal variance among groups,

i.e. testing the equality of normal population parameters had p > 0.05 (data not

shown), thus using the quadratic approach (QDA) was not necessary and may

have overfitted, which resulted in lower correct identification rates. Instead, the

linear boundaries (LDA) provided better recognition for the unknown samples at

the test step. The nonparametric k-NN approach had the same correct

identification rates at all k’s as did the LDA. The high and constant correct

identification rates indicated that the groups were well-separated in the models,

therefore, the classification did not depend on the discriminant methods.

Correct Identification I

1.0

 

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LDA QDA 1-NN 2-NN 3—NN 4-NN
Discrimination Method

Figure 61 Correct classification of various discrimination methods applied to
sensor responses for the 45°C headspace of various packaging materials, using
LDA/QDA and k-NN for k- - 1, 2, 3, 4.

161

Correct Identification

 

 

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LDA QDA 1-NN 2-NN 3-NN 4-NN
Discrimination Method

Figure 62 Correct classification rates of various discrimination methods applied
to sensor responses for the 70°C headspace of various packaging materials,
using LDA/QDA and k-NN for k = 1, 2, 3, 4.

Correct Identification

1.0

 

0.9 1
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LDA QDA 1-NN 2-NN 3—NN 4-NN
Discrimination Method

Figure 63 Correct classification rates of various discrimination methods applied
to sensor responses for the 95°C headspace of various packaging materials,
using LDA/QDA and k-NN for k = 1, 2, 3, 4.

162

The correct identification rates at the test steps for the LDA and k-NN
models were 0.6, 0.7 and 0.95 for models based on 45, 70 and 95°C headspace
samples, respectively (Figure 61 to Figure 63). More volatiles were released at
higher headspace temperature, which resulted in more intense sensor responses,
and gave more clearly defined regions for correct recognition of unknown
samples.

Linear discriminant models can be graphically displayed in two-
dimensional canonical discriminant scatter plots (Figure 64 to Figure 66). The
first two discriminants, CAN1 and CAN2, explained 99.73%, 99.05% and 96.42%
of the total variance of the sensor responses for 45, 70, and 95°C headspace
samples, respectively. It showed clear separation among the different packaging
materials. Unknown samples (i.e. an independent test data set), while being
projected to the models, were mostly located at the expected regions, with A
slightly larger group variances. The PE-coated carton material and PET were
well separated from the other packaging materials, and all PE-coated carton
material and PET samples in the test data set were fully recognized using LDA or
k-NN (data not shown).

The closeness of glass, HDPE and HDPE-TiOz headspace samples was
responsible for the incorrect identification. This implies that the headspace
volatiles from HDPE (and HDPE-TiOz) were relatively low, which resulted in
similar sensor responses to glass, which is an inert material and no or little

headspace volatiles were expected under the test conditions.

163

(a) Training

 

 

 

 

 

 

 

 

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W1 (95.456)

Figure 64 Canonical discriminant scatter plots of the (a) training (b) test data
sets. Headspace samples of various packaging materials: glass (G), HDPE (H),
HDPE-Ti02 (T), PET (E), and PE-coated carton material (P), were generated at

45°C.

164

8M2 (2.568)

m2 (2-55‘)

(a) Training

 

 

 

 

 

 

 

 

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(b)Test
20;
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CflNl (96.4%)

Figure 65 Canonical discriminant scatter plots of the (a) training (b) test data
sets. Headspace samples of various packaging materials: glass (G), HDPE (H),
HDPE-TiOz (T), PET (E), and PE-coated carton material (P), were generated at

70°C.

165

(a) Training

 

CflN2(30.46%)

 

 

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-so -40 -2o 0 20 40 so so too 120
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Figure 66 Canonical discriminant scatter plots of the (a) training (b) test data
sets. Headspace samples of various packaging materials: glass (G), HDPE (H),

HDPE-TiOz (T), PET (E), and PE-coated carton material (P), were generated at
95°C.

166

Direct analysis of the packaging materials led to more intense sensor
responses from the more concentrated headspace volatiles and resulted in iclear
differentiation. In addition, significantly less interference from water vapor was
expected, since MOS sensors also respond to water vapor, which is odorless.
However, not all types of volatiles in packaging materials will migrate to food
contents; when the migration takes place, not all amounts will migrate to the food
matrix. Quantifying the volatiles from packaging materials directly may not
correctly reflect the actual impact of migration of packaging off-flavors, which

were mostly not detected by consumers.

4.2.2. Packaging off-flavors in water and 2% milk
4.2.2.1. Sensory evaluation

Two series of triangle tests, each performed by 24 consumer panelists,
were conducted to investigate if the direct contact of packaging materials (glass,
HDPE, PET and PE-coated paper cartons) resulted in detectable flavor changes
in water or 2% milk at 5°C for 3 days. It was found that water stored in HDPE
bottles had a very mild flavor difference (p < 0.10) from the control, i.e. deionized
water (Table 14). No significant packaging off-flavors in 2% milk were detected
(Table 15).

Packaging off-flavors from HDPE bottles were very subtle but still had the
most correct responses from the consumers among the tested packaging
materials, in both water and 2% milk (Table 14 and Table 15). Oxidative

hydrocarbons generated during plastic processing are responsible for the

167

packaging off-flavors [11-13], and the intensities vary dependent on the severity

of the processing conditions (temperature and/or pressure).

Table 14 Triangle test results on HPLC grade water in glass, HDPE, PET bottles
and PE-coated paper cartons at 5°C for 3 days; HPLC grade water stored in a

4 L amber glass jug was used as the control. Tests were performed by a 24-
member consumer panel.

 

Number of Correct Responses from

 

Packaging Material 24 Subjects p
Glass 9 -
HDPE 12 0.10
PET 8 —
Paper Cartons 7 --

 

Table 15 Triangle test results on 2% milk in glass, HDPE, PET bottles and PE-
coated paper cartons, at 5°C for 3 days; 2% milk stored in a 4 L amber glass
jugs was used as the control. Tests were performed by a 24,-member consumer
panel

 

Number of Correct Responses from

 

Packaging Material 24 Subjects p f
Glass 6 -

HDPE 10 --

PET 9 —

Paper Cartons 6 -

 

168

The results are contrary to those from analysis of the packaging materials
using the electronic nose, where HDPE was found to have less headspace
volatiles than PET and PE-coated carton material (Figure 64 to Figure 66).
Qualitatively, the volatiles in the high temperature headspace (45, 70 or 95°C for
the electronic nose analysis) of packaging materials were not necessarily odor-
active migrants causing packaging off-flavors in water or 2% milk after storage at
5°C for 3 days. In addition, the responses and threshold levels of volatiles are
different for the human nose and the electronic nose. Quantitatively, analysis of
the potential migrants in packaging materials directly may overestimate the
concentrations of the actual migrants. In addition, the migrants which cause
packaging off-flavors may have low volatility and mostly exist in the liquid phase
instead of the headspace.

An 8-member trained panel evaluated 2% milk in glass, HDPE, HDPE-
TiOz, PET bottles and PE-coated paper cartons, and was not able to show a
significant flavor change, when stored at 5°C for 3 days (Table 16 and Figure 67).
2% milk samples in glass bottles received a slightly lower score (63812.07) than
2% milk in HDPE (70011.77) and HDPE-Ti02 (7.751139), which have been a
result of the large variance of the sensory scores. Statistically, 2% milk in glass,
HDPE, HDPE-TiOg and PET bottles did not have significant flavor difference
(Table 16). Samples in PE-coated cartons had a slightly lower absolute sensory
score (4.63:2.26). However, there was no strong evidence of a significant flavor
change in PE-coated cartons, due to the highly diverse sensory scores (two

outliers at score 1 and 9).

169

Table 16 Sensory scores of 2% milk in glass, HDPE, HDPE-TiOz, PET bottles
and PE-coated paper cartons at 5°C for 3 days. Scores were given by an 8-
member trained panel. ,

 

 

Duration of light Sensory Score
exposure (hours) (average 1 standard deviation)
Glass 6.38 12.07 a b
HDPE 7.00 11.77 a b
HDPE-Ti02 7.75 11.39 a
PET 5.88 12.90 a b
Paper Carton 4.63 12.26 b

 

‘ Means with the same letter are not statistically different at 0 =0.05 (T ukey’s
studentized range test).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

FLAVOR scone
U1
1
——+
. :—

 

1— «-

 

 

 

l I l I l
GLASS HDPE HDPE-TIOQ PET PAPER CARTON

PACKAGING MATERIAL

Figure 67 Sensory scores of reduced fat milk stored in glass, HDPE, HDPE-
TiOz, PET bottles, and PE-coated paper cartons, at 5°C for 3 days.

170

The highly diverse sensory score, especially for the samples stored in
PET and PE-coated cartons, might indicate detection of packaging off-flavors by
part of the trained panel. The same trained panel (but not conducted on the same
day) gave a sensory score of 8.501054 to the 2% milk in glass bottles and
stored in the dark for 48 hours (Table 11), which was unexpectedly different from
the score assigned to the 2% milk in glass bottles and stored in the dark for 3
days (63812.07). Several panelists commented that there was long lasting
aftertaste in water and 2% milk stored in various packaging materials (data not
shown). Since the samples were evaluated in random order, the aftertaste of
packaging off-flavors may have interfered with the evaluation of further samples,

and led to the large variance in sensory scores.

4.2.2.2. Headspace analysis using the electronic nose

The 95°C water and 2% milk headspace samples were analyzed using the
electronic nose. Unsupervised Ieaming techniques, HCA and PCA, did not show
an obvious pattern or separation among the water or 2% milk samples stored in

various packaging materials at 5°C for 3 days (Figure 68 and Figure 69).

171

(a)

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P C T P C G H P G H H E T H E P T C E C E G

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.

 

-o.04 -o.0a -0.02 -0.0I 0.00 0.01 0.02 0.03
PCI (61 .6510)
Figure 68 (a) HCA (b) PCA of sensor responses for the 95°C headspace of
water stored in various packaging materials: glass (G), HDPE (H), HDPE-TiOz

(1'), PET (E), and PE-coated paper cartons (P), at 5°C for 3 days. Control (C) is
water from the original 4L amber glass jug.

172

.08 ' (a)

 

 

 

 

 

 

 

 

 

 

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C C E P H T E G T E H T C C B P H T G E G P H P

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-0.l3 -0.08 -0.03 0.02 0.07 0.12 0.1?
PCI(88.55$)

Figure 69 (a) HCA (b) PCA of sensor responses for the 95°C headspace of 2%

milk stored in various packaging materials: glass (G), HDPE (H), HDPE-TiOz (T),
PET (E), and PE-coated paper cartons (P), at 5°C for 3 days. Control (C) is milk

from the original gallon HDPE jug.

173

Supervised Ieaming techniques, LDA/QDA and k-NN for k = 1 to 4, had
poor correct identification rates at the test step, for water and 2% milk stored in
various packaging materials less than 0.3 and 0.2, respectively (Figure 70 and
Figure 71). A graphical display of LDA results as canonical discriminant scatter
plots showed a grouping trend, but no group separation in the models can be
clearly defined (Figure 72a and Figure 73a). When projecting unknown samples
on the models, it showed poor recognition for both water and 2% milk samples
(Figure 72b and Figure 73b). The headspace volatiles of each packaging
material were clearly differentiated by the electronic nose (Figure 66), while the
water or 2% milk samples were not able to be differentiated based on their
packaging materials. It was probably a result of low concentration of the migrants
in the water and 2% milk headspace, and the interference of water vapor, which
MOS sensors respond to. Maneesin (2001) [73] and Das (2003) [66] found that
the electronic nose (FOX 3000, AlphaMOS) was capable of discriminating
volatiles originated from different HDPE jugs or resins, but the water samples
having direct contact to the HDPE or resins were not fully differentiated.

In conclusion, packaging off-flavors did not prove to be a concern from
storing 2% milk in glass, HDPE, HDPE-TiOz and PET bottles, and PE-coated
paper cartons (half-pint, 237 ml) at 5°C for 3 days. Both sensory and instrumental

analyses did not indicate any strong evidence of significant packaging off-flavors.

174

 

—e—Training
1.0 + Validation
Q9l --e-- Test

0.8 .
0.7 -
0.6 -
0.5 .
0.4 -
0.3 -
0.2 -
0.1 -
0.0 u u r .

LDA QDA 1-N N 2-NN 3-NN

Discrimination Method

 

 

 

 

Correct Identification

 

 

 

 

Figure 70 Correct classification rates of various discrimination methods applied
to sensor responses for 95°C headspace of water in various packaging materials,
using LDA/QDA and k-NN for k = 1, 2, 3, 4.

 

—e—Training
1.0 A +Validation
0'9 .. H." TGSt

0.8 .
0.7 -
0.6 -
0.5 -
0.4 ~
0.3 ~
0.2 j
0.1 4
0.0

 

 

 

 

Correct Identification

 

 

 

 

LDA QDA 1-NN 2-NN 3-NN 4-NN
Discrimination Method

Figure 71 Correct classification rates of various discrimination methods applied
to sensor responses for 95°C headspace of 2% milk in various packaging
materials, using LDA/QDA and k-NN for k = 1, 2, 3, 4.

175

(a) Training

 

 

 

 

 

 

 

 

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s E E P
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-20 -10 o lo 20 30 4o

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Figure 72 Canonical discriminant scatter plots of the (a) training (b) test data
sets. Headspace samples of water stored in various packaging materials: glass

(G), HDPE (H), HDPE-TiOz (T), PET (E), and PE-coated paper cartons (P), were
generated at 95°C.

176

(a) Training

 

 

 

 

 

 

 

 

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Figure 73 Canonical discriminant scatter plots of the (a) training (b) test data
sets. Headspace samples of 2% milk stored in various packaging materials:
glass (G), HDPE (H), HDPE-TiOz (T), PET (E), and PE-coated paper cartons (P),
were generated at 95°C.

177

4.2.3. Packaging and light oxidized off-flavors in 2% milk

Packaging materials also have different light barrier properties and thus
offer different protection for milk against light-induced oxidation, which can be
induced by light in both the UV and visible ranges (chapter 2.1.1). Glass is a very
poor light barrier and allows light in the higher UV range (300- 400 nm) and
throughout the visible range (400-700 nm) to pass through (Figure 8). Clear
transparent PET also allows light transmission in the higher UV range (320- 400
nm) as well as 90% of the visible light (400-700 nm) to transmit into the product
(Figure 74). Translucent, unpigmented HDPE has up to 70% light transmission at
400—700 nm. Addition of TiOz to HDPE reduces light transmission significantly,
from ~ 70% to < 20% of visible light. PE-coated paper cartons have the lowest
visible light transmission ~ 10%, and the blue colored portion provides additional
light protection at 550-750 nm. Table 17 shows the thickness of the tested
packaging materials.

To protect milk and other dairy products from photosensitized oxidation in
the presence of riboflavin, it is critical to reduce the UV and visible light
transmission, especially at 500 nm and below (Figure 7). Addition of white (T ioz)
and/or yellow pigment results in substantial reduction in light transmission [84].
Paper cartons had the lowest light transmission, and more protection can be
expected from the paper cartons printed in deep yellow or orange [1], although it
has been reported that similar protection is provided by paper cartons printed in

different colors (unpigmented, yellow, red, blue, and black) [8].

178

Table 17 Thickness of the tested packaging materials.

 

 

 

 

 

 

 

Packaging material Thickness1 (mil) Thickness (mm)
HDPE 30713.4 077910.086
HDPE-Ti02 29211.5 ' 074110.037
PET 19611.9 049910.048
PE-coated paper carton 17.0101 043210.002
‘ measured using a micrometer (Model 549, Testing Machines, lnc., Amityville,
MY.)
1000 .
90 . (a)
so .
(b)
E 70 .
.9
g 50.
C
E
,_ 40 .
Be 30 .
2°+ (c)
10. (d)
_ (e)
0.0 5..., 1 ,-,w..,-.~h..—..-_-‘ -- , " I _____
200.0 300 400 500 600 700 0000

Wavelength (nm)

Figure 74 Percent light transmission (200-1100 nm) of various packaging

materials: (a) PET; (b) HDPE; (c) HDPE-TiOz; (d) PE-coated paper cartons in

white; (e) PE-coated paper cartons (blue).

179

4.2.3.1. Sensory evaluation

A series of triangle tests (Table 18) showed that 2% milk in PE-coated
paper cartons had no significant olfactory quality change after exposure to 12
hours of fluorescent light (1000 Ix) at 5°C, while light-induced quality change in
milk stored in other packaging materials (HDPE, HDPE-TiOz and PET) was
detectable by consumers. Incorporation of the pigment Ti02 into HDPE (HDPE-
TiOz) reduced but did not eliminate flavor changes (p < 0.01), compared to HDPE
and PET (p < 0.001). The light protection offered by milk packaging was closely
related to their light barrier properties (Figure 74). Paper cartons allowed the
least light transmission and provided the best light protection among the

packaging materials tested, followed by HDPE-TiOz.

Table 18 A series of triangle tests on 2% milk stored in HDPE, HDPE-TiOz, PET
bottles and PE-coated paper cartons, and exposed to 12 hours of fluorescent
light at 5°C; 2% milk stored in aluminum foil wrapped glass bottles was used as
the control. Tests were performed by a 24-member consumer panel.

 

Packaging Material Number of Correct Responses from

 

24 Subjects
HDPE 17 0.001 ,
HDPE-TiOz 15 0.01
PET 17 0.001
Paper Carton 9 -

 

The sensory scores, from an 8-member trained panel, of 2% milk in paper
cartons and HDPE-TiOz bottles were slightly, but not significantly, higher than
others (T able 19 and Figure 75).

180

Table 19 Sensory scores of 2% milk stored in glass, HDPE, HDPE-TiDz, PET
bottles, and PE-coated paper cartons, and exposed to 12 hours of fluorescent
light at 5°C. Scores were given by an 8-member trained panel.

 

 

. . Senso score
Packaging material (average 1 starrIdard deviation)
Glass 5.75 12.55 a
HDPE 5.25 12.92 a
HDPE-Ti02 6.50 11.41 a
PET 5.25 12.19 a
Paper Carton 6.88 12.36 a

 

1 Means with the same letter are not statistically different at o = 0.05 (T ukey’s
studentized range test).

 

Q—
B— J

 

 

 

 

 

 

 

7—l .
6—

 

 

 

 

 

 

 

 

 

 

 

 

4..

 

 

FLAVOR SCORE

 

3—

2—l

 

 

 

 

 

 

g...

 

I I I I I
GLASS HDPE HDPE-TIC; PET PAPER CARTON

PACKAGING MATERIAL

Figure 75 Sensory scores using a trained panel of 2% milk stored in glass,
HDPE, HDPE-TiOz, PET bottles, and PE-coated paper cartons exposed to 12
hours of light at 5°C.

181

Milk exposed to light for 12 hours in glass bottles received similar sensory
scores of 6.381220 (Table 11) and 5.751255 (Table 19) in different tasting
sessions. It indicates a reproducible sensory ability of the trained panel for light

oxidized off-flavors.

4.2.3.2. Headspace analysis using the electronic nose

Unsupervised Ieaming techniques, HCA and PCA, did not show an
obvious pattern or separation among the light oxidized 2% milk samples stored in
various packaging materials (Figure 76). I

Supervised Ieaming techniques, LDA and 1-NN, had correct identification
rates at the test step, 0.54 and 0.63, respectively (Figure 77). Graphical display
of LDA results as canonical discriminant scatter plots showed a grouping trend
and partial differentiation (Figure 78a), when samples with no or moderate light-
oxidized off-flavors (control, paper cartons and HDPE-TiOz) were clearly
differentiated from oxidized samples (glass, HDPE and PET). The first canonical
variable (CAN 1) explained 97.09% of the total variance. Along the CAN1
coordinate, the control (milk in aluminum foil wrapped glass bottles), PE-coated
paper cartons and HDPE-TiOz, glass, HDPE and PET, were roughly aligned in
the order of expected “good milk” to “light-oxidized milk”, based on light barrier
properties of the packaging materials. The distribution was less distinct when
projecting unknown samples on the models (Figure 78b). A similar trend was
also observed among the light oxidized 2% milk prepared in glass bottles for 0 to

48 hours (Figure 46).

182

PCZ(13.41§)

Figure 76 (a) HCA (b) PCA of sensor responses for the 95°C headspace of 2%
milk exposed to fluorescent light at 5°C for 12 hours, in various packaging
materials: glass (G), HDPE (H), HDPE-TIC; (1), PET (E), and PE-coated paper

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183

 

0.1?

PE-coated paper cartons provided the best light barrier and resulted in the
least light-oxidized off-flavors in 12 hours of light exposure, compared to glass,
HDPE, HDPE-TiOz and PET, although the potential for packaging off-flavors in
paper cartons has been reported [13, 81] and recognized as a flavor defect
“lacks freshness” in the ADSA sensory guidelines (Table 8). Since the amounts
of migrants, e.g. oxidative hydrocarbons, at the surface of the PE coating depend
on the processing conditions, the intensity of packaging off-flavors varies. In this
study the packaging off-flavors were not significantly perceived by either
consumer or trained panels. Compared to light-oxidized off-flavors, the

packaging off-flavors were less intense and more difficult to detect.

1.0
0.9 -
0.8 -
0.7 -
0.6 -
0.5 -
0.4 _

0.3 ~ . .
+Tralnlng
0'2 ‘ +Validation
0.1 - --O--Test
0.0 . I I . .
LDA QDA 1-NN 2-NN 3-NN 4-NN

Discrimination Method

 

 

 

Correct Identification

 

 

 

 

 

 

Figure 77 Correct classification rates of various discrimination methods
(LDA/QDA and k-NN for k = 1, 2, 3, 4) based on sensor responses for 2% milk
stored in various packaging materials and exposed to fluorescent light at 5°C for
12 hours.

184

CflN2(1.658)

03120-65”

(a) Training

 

 

 

 

(b) Test

CfiNl(97.09%)

 

 

H

E
T

 

 

jTliill

Illlll

-|0

llfill'lillll

0
CflNI(97.09%)

ITUrTITIIIFTf

10 20

Figure 78 Canonical discriminant scatter plots of the (a) training (b) test data
sets. Headspace of 2% milk stored in glass (G), HDPE (H), HDPE-TiOz (T), PET
(E), and PE-coated paper cartons (P) and exposed to light for 12 hours was
generated at 95°C.

185

4.3. Light oxidation of Cheddar cheese

A Cheddar cheese block (21.3 kg) was cut into one pound (454 9) pieces,
vacuum sealed in Nylon/PE bags, and exposed to cool-white fluorescent light
(2000 lx) at 5°C for 0, 2, 4 or 6 weeks. The surface slabs (top surface) and
interior slabs (1 cm below the top surface) were sampled and evaluated by both
sensory and instrumental analyses, to determine any quality changes in color

and flavor.

4.3.1. Discoloration of Cheddar cheese

Annatto extract, the water based colorant, was used to enhance the color
appearance of the Cheddar cheese, however, it is also susceptible to oxidation
and sensitive to processing parameters such as pH and temperature [89]. A 9-
member trained panel using a different-from-control test was used to investigate
the color change in Cheddar cheese due to light exposure (Table 11 and Figure
79). Cheddar cheese had a significant color change on the top surface in 2
weeks, and a more pronounced discoloration when exposed to light for 4 and 6
weeks. Visual examination revealed that color bleaching was responsible for the
differences in color. The Cheddar cheese surface slab which was wrapped in
aluminum foil, i.e. no light exposure, had the same color as the interior slabs of
all cheese samples, regardless of the light exposure duration of the cheese
samples. The interior slabs did not show significant change in flavor or color,
which shows that the light-induced discoloration only took place at the top

surface, which was the portion exposed to light.

186

Table 20 Difference-from-control sensory scores in color1 of Cheddar cheese
samples. Interior and surface slabs were taken from Cheddar cheese blocks at
0, 2, 4 or 6 weeks of light exposure (2000 Ix). Scores were given by a 9-member
trained panel.

 

Time (weeks) Color (different-from-control)

 

 

Interior Surface
0 0.22 10.44 C 0.33 10.50 c
2 0.44 10.53 c 2.00 10.71 b
4 0.22 10.44 c 3.00 10.87 a
6 0.22 10.44 C 3.00 10.87 a

 

‘ Ranged from 0: no difference to 5: extreme difference.
2 Means with the same letter are not statistically different at 0 =0.05 (T ukey’s
studentized range test).

(2) Interior (b) Surface

 

 

 

DIfferent-from-Control In Color (Sensory)

 

 

l l
U 2

Time (weeks)

1— fl * *
0— Ifi [i]

l l I I

0 2 4 5

A—l
m

Figure 79 Difference-from-control sensory scores in color of Cheddar cheese
samples. (a) Interior and (b) surface slabs were taken from Cheddar cheese
blocks at 0, 2, 4 or 6 weeks of light exposure (2000 Ix). .; mean values of each
treatment; 1: possible outliers.

187

Color measurements using a spectrophotometer (CIE L* a* b*) showed a
significant increase in lightness (L*) and decrease in redness (a*) and yellowness
(b*), on the top surface of Cheddar cheese exposed to light (Table 21).
Yellowness decreased continuously over the 6 weeks of the light exposure (2000
Ix), from 45.361 0.82 to 30.601 0.73. Redness started to drop after 4 weeks.
Lightness had a relatively small increase over the 6 weeks period.

The overall color change (AE) represents the color differences between
the sample and control, i.e. vacuum packed Cheddar cheese wrapped in
aluminum foil. The AE values showed a similar trend as the different-from-control
sensory scores, with smaller variation (Figure 79). The color change of the top
surface took place in 2 weeks, and developed further in 4 and 6 weeks.

Hong et al. (1995) [90] suggested using hue angles (0° for red and 90° for
yellow) to measure the color change of Cheddar cheese, to evaluate “pink
discoloration”, a common problem that is normally a result of several processing
factors such as cooking temperature, emulsifying salts [92], pH or light exposure
[90]. They reported that Cheddar cheese exposed to fluorescent light (3500 lx,
cool-white) at 8°C for 14 days had significant decreases in yellowness and hue
angles as the oxygen transmission rates of the packaging films increased. Table
22 shows the hue angles of the Cheddar cheese samples. The top surface of the
Cheddar cheese that was exposed to fluorescent light (2000 Ix) had a significant
decrease in hue angles (i.e. more “pink” in color visually) in 2 weeks, while no

further decrease in hue angles was observed in 4 or 6 weeks.

188

Table 21 Color measurements (CIE L* a* b*) of the Cheddar cheese samples
exposed to 0, 2, 4 or 6 weeks of the fluorescent light (2000 Ix).

 

 

 

Time (weeks) Lightness (L*)
Interior Surface
0 75.141 0.25 b, 6‘ 74.811 0.40 c
2 75.161 0.46 b, c 75.531 0.35 a, b
4 75.001 0.43 b, c 75.701 0.32 a
6 74.941 0.32 0 76.001 0.45 a

 

‘ Means with the same letter are not statistically different at o = 0.05 (Tukey’s

studentized range test).

 

Time (weeks)

Redness (a*)

 

 

Interior Surface
0 12.061 0.27 b1 11.801 0.30 b
2 12.591 0.21 a 11.701 0.40 b
4 12.521 0.26 a 10.291 0.28 c
6 12.751 0.13 a 9.891 0.24 d

 

7' Means with the same letter are not statistically different at a = 0.05 (T ukey’s
studentized range test).

 

Yellowness (b*)

Time (weeks)

 

 

Interior Surface
0 46.891 0.55 a1 45.361 0.82 b
2 46.201 0.59 a, b 37.571 0.76 c
4 45.761 0.44 b 32.811 0.72 d
6 45.731 0.76 b 30.601 0.73 e

 

1 Means with the same letter are not statistically different at a = 0.05 (T ukey’s
studentized range test).

189

(8) Interior (0) Surface

 

10— $1

leferentfrom-Control in Color (AE )

 

 

 

 

l I I I
4 6 U 2

Time (weels)

Figure 80 Color difference (AE) of Cheddar cheese samples. (a) Interior and (b)
surface slabs were taken from Cheddar cheese blocks with 0, 2, 4, 6 weeks of
light exposure (2000 Ix). 0: mean values of each treatment; 1: possible outliers.

“-1
0'1

1 s1 1 E11 53

Table 22 Hue angle, tan'1 (bla), of the Cheddar cheese samples exposed to 0, 2,
4 or 6 weeks of the fluorescent light (2000 Ix).

 

Time (weeks) Hue angle (degree)

 

 

Interior Surface
0 75.57 10.27 a1 75.42 10.35 a
2 74.76 10.26 b 72.71 10.31 c
4 74.71 10.31 b 72.59 10.47 c,d

6 74.42 10.33 b 72.08 10.68 c,d

1 Means with the same letter are not statistically different at a = 0.05 (T ukey’s
studentized range test).

 

190

4.3.2. Body and texture of Cheddar cheese

Sensory tests were conducted to evaluate if there was a change in the
body and texture of the Cheddar cheese, due to the light exposure. A different-
from-control test (from 0: no difference to 5: extreme difference) showed no
significant changes occurred (Table 11 and Figure 81). There was a slight but not
significant increase in the “differences” in body and texture of the top surface
slabs exposed to light for 4 or 6 weeks.

“Body and texture” is part of the sensory quality evaluation of Cheddar
cheeses, based on ADSA guidelines (Table 9). A score from 5 (no criticisms) to 1
(poor quality) was assigned based on the sample defects in body and texture, e.g.
crumbly, pasty or weak. The results were in agreement with the different-from-
control test. No significant deterioration in body and texture, and slight but not
significant decreases in the sensory scores of the top surface of the Cheddar

cheese occurred for cheese exposed to light (Table 11 and Figure 81).

191

Table 23 Difference-from-control sensory scores of body and texture1 of
Cheddar cheese samples. Interior and surface slabs were taken from Cheddar
cheese blocks with 0, 2, 4 or 6 weeks of light exposure (2000 lx). Scores were
given by a 9-member trained panel.

 

Time (weeks) Body and Texture (different-from-control)

 

 

Interior Surface
0 0.44 10.73 a 0.11 10.33 a
2 0.22 10.44 a 0.56 11.01 a
4 0.56 10.88 a 1.44 11.33 a
6 0.56 10.73 a 1.44 11.42 a

 

1 Ranged from 0: no difference to 5: extreme difference.
2 Means with the same letter are not statistically different at o = 0.05 (Tukey’s
studentized range test).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

(8) Interior (b) Surface
“'3“ 3 — at» —— —-
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Figure 81 Difference-from-control sensory scores for body and texture of
Cheddar cheese samples. (a) Interior and (b) surface slabs were taken from
Cheddar cheese blocks with 0, 2, 4 or 6 weeks of light exposure (2000 Ix). -:
mean values of each treatment; *2 possible outliers.

192

Table 24 Sensory scores of body and texture of Cheddar cheese samples,
based on ADSA guidelines‘. Interior and surface slabs were taken from Cheddar
cheese blocks with 0, 2, 4 or 6 weeks of light exposure (2000 Ix). Scores were
given by a 9-member trained panel.

 

Body and Texture (ADSA)

 

 

Time (weeks)
Interior Surface
0 4.56 1 0.53 a 4.89 1 0.33 a
2 4.44 1 0.53 a 4.78 1 0.44 a
4 4.56 1 0.53 a 4.22 1 0.67 a
6 4.44 1 0.73 a 4.11 1 0.78 a

 

I Ranged from 1: poor quality to 5: no criticisms.
2 Means with the same letter are not statistically different at 0 =0.05 (T ukey's
studentized range test).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

(8) Interior (b) Surface
5 -— — — — — _ _..
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Figure 82 Sensory scores for body and texture of Cheddar cheese samples,
based on ADSA guidelines. (a) Interior and (b) surface slabs were taken from
Cheddar cheese blocks with 0, 2, 4, 6 weeks of light exposure (2000 IX). 0: mean
values of each treatment; *: possible outliers.

193

4.3.3. Light-induced flavor changes of cheese
4.3.3.1. Sensory evaluation

Different-from-control evaluation (Table 11 and Figure 81) showed a
significant flavor difference from the control, on the top surface of the Cheddar
cheese exposed to light for 2 weeks. After 4 weeks of light exposure, differences
were slightly but not significantly higher, and remained almost constant at 6
weeks. The sensory scores from the trained panel (Table 26 and Figure 84) were
in agreement with the different-from-control test. The scores were assigned to
samples based on the flavor criticisms of Cheddar cheese, e.g. bitter, high acid
or rancid, etc. (Table 9). From the listed 13 criticisms, the top surface of Cheddar
cheese exposed to light for 2, 4, or 6 weeks received lower sensory scores due

to the criticisms such as “oxidize , rancid” and “unclean” (data not shown).

4.3.3.2. Headspace analysis using SPME-GC

Using the CARIPDMS fiber coupled with GC, 24 volatiles were identified
by comparing the peak retention times of the samples to standards (Table 27).
The volatiles identified in the Cheddar cheese headspace included aldehydes,
ketones, alcohols, acids and sulfur compounds. It was reported [105] that
aldehydes and ketones were the major constituents of the volatile fraction of
shredded Cheddar cheese packaged under C02 and N2, respectively. Acetic
acid and sulfur compounds were also quantified. High concentrations of lipid
oxidation products, e.g. aldehydes, appeared in light-oxidized shredded

Cheddar cheese.

194

Table 25 Difference-from-control sensory scores in flavor1 of Cheddar cheese
samples. Interior and surface slabs were taken from Cheddar cheese blocks at
0, 2, 4 or 6 weeks of light exposure (2000 Ix). Scores were given by a 9-member
trained panel.

 

Time (weeks) Flavor (drfferent-from-control)

 

 

Interior Surface
0 0.56 10.73 b 0.56 10.73 b
2 1.11 11.05 b 2.44 11.01 a
4 0.78 10.97 b 3.67 11.00 a
6 0.56 10.73 b 3.56 10.88 a

 

TRanged from 0: no difference to 5: extreme difference.
2 Means with the same letter are not statistically different at 0 =0.05 (T ukey’s
studentized range test).

(8) Interior (b) Surface

 

Flavor (different-from-control)
M
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0—1 _ I] 3(-

I I I I I
U 2 4 6 U 2 4 6

Time (weeks)

 

 

 

 

 

 

—
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-

Figure 83 Difference-from-control sensory scores in flavor of Cheddar cheese
samples. (a) Interior and (b) surface slabs were taken from Cheddar cheese

blocks at 0, 2, 4, or 6 weeks of light exposure (2000 Ix). 0: mean values of each
treatment; 1: possible outliers.

195

Table 26 Flavor sensory scores of Cheddar cheese samples based on ADSA
guidelines‘. Interior and surface slabs were taken from Cheddar cheese blocks
with 0, 2, 4 or 6 weeks of light exposure (2000 lx). Scores were given by a 9-
member trained panel.

 

Flavor (ADSA)

 

 

Time (weeks)
Interior Surface
0 8.89 10.60 a,b 8.89 10.93 a,b
2 8.89 10.60 a,b 7.22 11.56 b
4 8.44 11.33 a,b 5.22 11.48 b
6 9.11 10.33 a 5.11 12.03 b

 

I Ranged from 1: poor quality to 10: no criticism.
2 Means with the same letter are not statistically different at 0 =0.05 (T ukey’s
studentized range test).

 

 

 

 

 

 

 

(8) Interior (b) Surface
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Figure 84 Flavor sensory scores of Cheddar cheese samples based on ADSA
guidelines. (a) Interior and (b) surface slabs were taken from Cheddar cheese
blocks with 0, 2, 4, or 6 weeks of light exposure (2000 IX). 0: mean values of
each treatment; *2 possible outliers.

196

Table 27 Volatiles identified1 in the headspace (60°C, 15 minutes) of Cheddar

 

 

cheese.

Peak Compound Retention time (min) Selected2
1 pentane+hexane 5.80 *
2 heptane 6.61 *
3 acetaldehyde 6.80 _ *
4 carbon disulfide 6.93
5 methyl sulfide 7.40 *
6 acetone 8.60 *
7 ethyl acetate 9.28 *
8 methanol 9.85 *
9 2-butanone 9.94
10 2-propanol 10.29
1 1 ethanol 10.58 *
12 pentanal 1 1.76 *
13 2-butanol 12.92
14 1-propanol 13.27
15 dimethyl disulfide 14.75
16 2-methyl-1-propanol 14.84
17 hexanal 14.88 *
18 1-butanol 16.64 *
19 heptanal 18.10 *

20 2-heptanone 18.16

21 1-pentanol 20.10 *
22 acetic acid 26.95 *
23 propionic acid 29.47 *
24 butanoic acid 31.73 *

 

1 Comparing retention time of the standards.

2 Peaks with better reproducibility were selected for further multivariate
analyses

197

Peak compounds with good reproducibility were selected for further
multivariate analyses, i.e. HCA/PCA to investigate the sample differences.
LDA/QDA and k-NN were used to build models for unknown sample recognition,
and PLS/MLP to correlate the area responses of the selected volatiles to the

flavor sensory scores from the trained panelists (Table 26 and Figure 84).

Using the unsupervised learning techniques, HCA and PCA, showed clear
separation (Figure 85) between the surface slabs (SO, 82, S4, and S6) and
interior slabs (l0, I2, l4, and I6), except for two “outliers” (82 and S4). Further
group separation among the group of surface slabs (i.e. the top surface of the
Cheddar cheese samples) showed changes in the headspace volatile contents
due to light exposure, whereas the interior slabs (i.e. 1 cm depth from the top
surface) were relative close to each other on the PCA score plot (Figure 85b).

Supervised models using LDA/QDA and k-NN for k = 1, to 3 (Figure 86)
showed high correct identification rates at the training and test steps.
Considering the overall model performance, 1-NN provided the best
discrimination (Figure 86). Its correct identification rates were the highest at the
training and test steps, and relatively higher than LDA/QDA at the validation step.
Clear discrimination between different samples and an accurate identification of
unknown samples was shown for the LDA model graphically presented on the
canonical discriminant plots (Figure 87). The first two discriminants, CAN1 and
CAN2, explained 99.74% of the total variance. Note that CAN1 explained most of
the variation between interior and surface slabs, and CAN2 was able to identify

samples with different light exposure times.

198

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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samples were the interior (I0, I2, l4 and I6) and surface slabs (SO, 82, S4, S6) of
the Cheddar cheese samples exposed to fluorescent light for 0, 2, 4, or 6 weeks.

199

 

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Discrimination Method

 

 

Correct Identification

 

 

 

 

 

 

 

Figure 86 Correct classification rates of various discrimination methods
(LDA/QDA and k-NN for k = 1, 2, 3, 4) based on headspace volatiles of interior
and surface slabs of the Cheddar cheese samples exposed to fluorescent light
for 0, 2, 4 or 6 weeks, quantified using SPME-GC.

200

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Figure 87 Canonical discriminant scatter plots of headspace volatile contents
quantified using SPME-GC of (a) training (b) test data, Headspace samples of

interior (l0, l2, l4, I6) and surface (SO, S2, S4, 86) slabs of the Cheddar cheese,
were generated at 60°C.

201

Although the discriminant models showed clear group separation (Figure
87) and high correct identification rates (Figure 86), they may not be useful to
identify the light-oxidized Cheddar cheese. The separation among the samples
with different extents of light oxidation was much smaller than the separation
between the surface and interior slabs. CAN1 accounted for 99.34% of the total
variance, while only 0.40% for CAN2. Moreover, the sample SO (top surface with
no light exposure) had similar sensory scores as the interior slabs (I0, l2, l4 and
I6), but was separated from the interior slabs on the canonical discriminant
scatter plot (Figure 87). All interior slabs had similar sensory scores but were
positioned separately on the scatter plot, in a similar manner as the surface slabs
which were light-oxidized. It appears that the discrimination was more based on
where the samples were taken from than the light-induced flavor changes. The
collection of reproducibly identified volatiles may not cover all volatiles that were
responsible for the light induced flavor changes, and may have caused the
misleading results.

Quantitatively, PLS and MLP models were applied to investigate the
correlations between the sensory scores and the area responses of headspace
volatiles quantified using the SPME-GC (Figure 88 and Figure 89). The MLP
model performed better than the PLS model, in terms of both the ranges of
predicted sensory scores and the RMSE. However, the models may not be
reliable for predicting light-oxidized Cheddar cheeses for the same reasons

discussed previously.

202

 

10 (a) Training (RMSE = 0.8009)

PREDICTED FLAVOR SCORE

 

 

 

 

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PREDICTED FLAVOR SCORE

 

 

 

 

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5 6 7 8 9

ACTUAL FLAVOR SCORE

Figure 88 PLS predicted versus actual flavor scores of interior (l0, I2, l4, l6) and
surface (SO, 82, S4, 86) slabs of the Cheddar cheese, based on area response
of SPME-GC to 60°C headspace. Models were applied to (a) training and (b) test
data. The reference line indicates equal values of predicted and actual sensory
scores.

203

 

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Figure 89 MLP predicted versus actual flavor scores of interior (l0, I2, I4, l6) and
surface (SO, 82, S4, 86) slabs of the Cheddar cheese, based on area response
of SPME-GC to 60°C headspace. Models were applied to (a) training and (b) test
data. The reference line indicates equal values of predicted and actual sensory
scores.

204

4.3.3.3. Headspace analysis using the electronic nose

Headspace samples of light-oxidized Cheddar cheese were generated at
60 or 95°C for 15 minutes and then analyzed using the electronic nose. Figure 90
shows the profiles of sensor response over time, to the 60°C headspace samples
of the top surface of Cheddar cheese exposed to light for 0 and 6 weeks. The
differences between sensor profiles of the two samples were not distinct by
comparing the 12 MOS sensor responses directly. This may be due to the high
sensitivity of MOS sensors to ethanol and other alcohols, such that the intense
response may mask the sensor responses for other analytes of interest.

Unsupervised leaning techniques, HCA and PCA, were applied to view the
discrimination and natural grouping of the headspace samples of Cheddar
cheese generated at 60°C and 90°C (Figure 91 and Figure 92). No clear
separation was apparent in the 60°C PCA score plot (Figure 91b). A partial
grouping in the 90°C PCA score plot roughly followed the extent of light oxidation
(Figure 92b). Samples with no light exposure (l0, I2, l4, l6 and 80) formed a
cluster (around PC1 = 10.2 and PC2 = -0.01), and the top surface samples (82,
S4 and 86) then partially grouped and gradually moved away from this cluster as

the light exposure time increased.

205

 

 

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0 200 ‘ 400 ' 600 ' 800 ' 1000 ' 1200
Time (sec)
Figure 90 Sensor responses of the electronic nose to 90°C headspace of the

top surface samples of Cheddar cheese (a) without (b) with 6 weeks of light
exposure at 5°C.

206

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Figure 91 (a) HCA and (b) PCA of electronic nose sensor responses for the
60°C headspace samples of interior (l0, l2, I4 and I6) and surface slabs (SO, 82,
S4, S6) of the Cheddar cheese samples exposed to fluorescent light for 0, 2, 4 or
6 weeks.

207

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Figure 92 (a) HCA and (b) PCA of electronic nose sensor responses for the
90°C headspace samples of interior (l0, l2, l4 and I6) and surface slabs (SO, 82,
S4, S6) of the Cheddar cheese samples exposed to fluorescent light for 0, 2, 4 or
6 weeks.

208

Supervised learning techniques, LDA/QDA and k-NN for k = 1 to 4, were
applied to training data, which contained four replicates of the surface slabs (SO,
82, S4, and S6) and interior slabs (l0, l2, l4, and I6). The models were cross-
validated using the leave-one-out method, and the models were then used to
identify the “unknown” samples from an independent test data set, which
contained three replicates of the surface slabs SO, 82, S4, and $6.

LDA and 1-NN had the highest correct identification rates (0.4 and 0.5,
respectively) at the test steps, for both 60°C and 90°C headspace samples
(Figure 93 and Figure 94). A more distinct group separation for the 90°C
headspace samples of the Cheddar cheese with differing light oxidation was
apparent on the canonical discriminant scatter plot (Figure 96), while the 60°C
headspace samples were only partially separated (Figure 95). The Cheddar
cheese samples without light exposure, i.e. |0, l2, I4, l6 and SO, grouped at the
negative CAN1 (the first canonical discriminant) region (Figure 95a and Figure
96a). Along the CAN1 coordinate, Cheddar cheese top surface samples exposed
to light for 2, 4, and 6 weeks were aligned. While subjecting an independent test
data set to the models, the 90°C headspace samples stayed at similar group
territories (Figure 96b). The 60°C headspace samples from the test set were
scattered and shifted to a very different range, which implied a poor model
generalization. The supervised models based on sensor responses for 90°C
headspace samples had higher correct identification rates (Figure 94) and better

discrimination (Figure 96).

209

Correct Identification

1.0

 

—e— Training
A + Validation

 

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LDA QDA
Discrimination Method

1-NN 2-NN 3-NN 4-NN

Figure 93 Correct classification rates of various discrimination methods (LDA,
QDA, and k-NN for k = 1 to 4) applied to the sensor responses for 60°C
headspace of interior and surface slabs of the Cheddar cheese samples exposed
to fluorescent light for 0, 2, 4, or 6 weeks.

Correct Identification

1.0

 

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LDA QDA
Discrimination Method

1-NN 2-NN 3-NN 4-NN

Figure 94 Correct classification rates of various discrimination methods (LDA,
QDA, and k-NN for k = 1 to 4) applied to the sensor responses for 90°C
headspace of interior and surface slabs of the Cheddar cheese samples exposed
to fluorescent light for 0, 2, 4, or 6 weeks.

210

(a) Training

 

I4

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25 S4

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cane (8.54%)
55
ES
4)
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0 1 32 32
- l4 .6 39s

88

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-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7
CfiN1(79.09%)

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(b) Test

13 SB
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° 3 880
ss
1 92
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82

T—F T T T T T l I l I T I l l I I T T j T TiT 1 l l l l l l I
-10 0 10 20
CflN1(79.09%)

 

 

 

Figure 95 Canonical discriminant scatter plots of the (a) training (b) test data
sets. Headspace samples of interior (I0, I2, I4, I6) and surface (SO, 82, S4, S6)
slabs of the Cheddar cheese, generated at 60°C.

211

(a) Training

 

 

 

 

 

 

 

 

3: 32
3 82 34
2? 10 32 32
E 80|o S4
1: 10 34
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sf
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a .
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C3111 (85.08%)

Figure 96 Canonical discriminant scatter plots of the (a) training (b) test data
sets. Headspace samples of interior (l0, I2, l4, I6) and surface (SO, 82, S4, SB)
slabs of the Cheddar cheese, generated at 90°C.

212

The electronic nose can analyze the sample headspace as a whole and is
not concerned about “not covering all volatiles that are responsible for the light-
induced flavor changes”, as the SPME-GC attempted to do. As expected, the
sample SO (top surface without light exposure) was close to all interior slab
samples, which were not exposed to light. The grouping of light-oxidized cheese
samples in both PCA and LDA (Figure 92b and Figure 96) was in agreement with
the sensory score trend (Figure 83 and Figure 84).

Quantitative PLS and MLP models were used to investigate the
correlations between the sensory scores and the sensor responses of the
electronic nose to the 60°C and 90°C headspace samples of the light oxidized
Cheddar cheese (Figure 97 to Figure 100). Considering the RMSE and predicted
sensory score, 90°C headspace samples provided better fitted PLS and MLP
models, and more precise prediction of sensory scores. The 90°C PLS model
had a similar model RMSE but a better test set prediction, compared to the 90°C
MLP model. The PLS approach was to find latent component(s) from the sensor
responses that were also relevant for sensory scores, which can be well defined
since higher volatile contents and sensor responses were expected from light-

oxidized samples with lower sensory scores.

213

q
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PREDICTED FLAVOR SCORE

 

    

(a) Training (RMSE = 0.4862)

 

 

 

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ACTUAL FLAVOR SCORE

 

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(b) Test (RMSE = 2.6181)

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Figure 97 PLS predicted versus actual flavor scores of interior (l0, l2, l4, I6) and
S4, 86) slabs of the Cheddar cheese, based on electronic nose
sensor responses for 60°C headspace. Models were applied to (a) training and
(b) test data. The reference line indicates equal values of predicted and actual

surface (SO, 82,

sensory scores.

5

IllIIIllIIIIIlllIIIIIIIIIIIIIITIIIIIIIII

6 7 8
ACTUAL FLAVOR SCORE

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214

PREDICTED FLAVOR SCORE

PREDICTED FLAVOR SCORE

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(a) Training (RMSE = 0.7104)

 

 

 

  
    
    
 

 

 

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ACTUAL FLAVOR SCORE

Figure 98 MLP predicted versus actual flavor scores of interior (l0, l2, I4, l6) and
surface (80, 82, S4, 86) slabs of the Cheddar cheese, based on electronic nose
sensor responses for 60°C headspace. Models were applied to (a) training and
(b) test data. The reference line indicates equal values of predicted and actual
sensory scores.

215

 

 

 

 

 

 

 

 

 

 

 

 

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(b) Test (RMSE = 0.7903)
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ACTUAL FLAVOR SCORE

Figure 99 PLS predicted versus actual flavor scores of interior (l0, l2, l4, I6) and
surface (80, 82, S4, 86) slabs of the Cheddar cheese, based on electronic nose
sensor responses for 90°C headspace. Models were applied to (a) training and
(b) test data. The reference line indicates equal values of predicted and actual
sensory scores.

216

 

 

10 E
(a) Training (RMSE = 0.2873)

PREDICTED FLAVOR SCORE
N

 

 

 

 

 

 

 

 

=I§E
4‘]!IllllllllllllIIIIIIIIIIlllllrlIIIIIIIIIIIIIIIIII
5 8 7 8 9 10
ACTUAL FLAVOR SCORE
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j (b)Test(RMSE=1.1666)
3 99
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5 6 7 8 9

ACTUAL FLAVOR SCORE

Figure 100 MLP predicted versus actual flavor scores of interior (l0, l2, l4, l6)
and surface (80, 82, S4, 36) slabs of the Cheddar cheese, based on electronic
nose sensor responses for 90°C headspace. Models were applied to (a) training
and (b) test data. The reference line indicates equal values of predicted and
actual sensory scores.

217

Light-induced color and flavor quality changes were limited to the top
surface of the vacuum-packaged Cheddar cheese, which was exposed to light
(2000 Ix). Discoloration and off-flavors were detected by an 8-member trained
panel after exposure to light for 2 weeks, and the quality changes were more
pronounced after 4 and 6 weeks. Color measurement indicated that a continuous
decrease in yellowness contributed the most to the discoloration, along with a
relatively small decrease in redness and an increase in lightness. No body and
texture changes in light-oxidized Cheddar cheeses occurred. The electronic nose
has the advantage of being able to analyze the sample headspace as a whole
and is not concerned about “not covering all volatiles that are responsible for the
light-induced flavor changes” which is what the SPME-GC technique tries to do.
The discriminant models based on sensor responses for 90°C headspace
samples had higher correct identification rates and better discrimination than at
60°C. The 90°C PLS model had a similar model RMSE but a. better test set

prediction, compared to the 90°C MLP model.

218

CHAPTER 5

CONCLUSIONS

Both consumer and trained panelists detected the light-oxidized off-flavor
in reduced fat (2%) milk developed in glass bottles under 8 hours of light
exposure (1000 Ix) at 5°C. The CARIPDMS fiber was selected to collect the
headspace volatiles of light oxidized milk. Headspace pentanal and hexanal
increased as the light exposure time increased. Using a 95°C headspace
temperature for the electronic nose analysis provided better model discrimination
than 45°C or 70°C, due to the higher volatile contents generated at the higher
headspace temperatures. The first canonical discriminant was indicative of the
extent of light-oxidation. Light-oxidized milk was poorly recognized as a function
of light exposure time, but samples with a similar extent of light oxidation were
close to each other in the discriminant models. By defining the harmful light
exposure time as a milk deterioration threshold, the 95°C LDA model correctly
recognized 97% of the milk samples exposed to light for 8 hours or longer.
Quantitatively, the 95°C PLS model provided better prediction of the sensory
scores than the 95°C MLP model.

The different packaging materials (237 ml glass, HDPE, HDPE-TiOz, PET
bottles, and PE-coated paper cartons) were clearly discriminated and identified
using the electronic nose. However, no significant packaging off-flavors in water
or 2% milk were perceived by consumer panels, except water stored in HDPE

bottles had a very mild flavor difference (p< 0.10) from pure water. The water and

219

milk samples stored in the various packages were only partially differentiated in
electronic nose analysis. HDPE was found to have less headspace volatiles than
PET and PE-coated cartons when the packaging materials were analyzed
directly, but the sensory tests indicated that the water and 2% milk in HDPE had
more intense off-flavor than in PET and PE-coated cartons. The effect of
packaging materials on light-induced oxidation in milk was closely related to their
light barrier properties. PE-coated paper cartons reduced light-oxidation of 2%
milk significantly (12 hours of light exposure, 1000 Ix), while HDPE-TiOz gave
only partial light protection. Compared to light-oxidized off-flavors, the packaging
off-flavors were less intense and more difficult to detect. Packaging off-flavors in
water and 2% milk were not clearly defined by either sensory evaluation or
electronic nose analysis.

Light-induced quality changes (color and flavor) were limited to the top
surface of the vacuum-packaged Cheddar cheese, which was the portion that
was exposed to light (2000 lx). Discoloration and off-flavors were detected by the
trained panel after exposure to light for 2 weeks. Color measurement showed a
continuous decrease in yellowness contributed the most to the discoloration,
along with a relatively small decrease in redness and increase in lightness. No
body and texture changes in light-oxidized Cheddar cheeses were noticed. The
electronic nose has the advantage of analyzing the sample headspace as a
whole. The discriminant models of sensor responses using 90°C headspace

samples had higher correct identification rates and better discrimination than at

220

60°C. The 90°C PLS model had a similar model RMSE, but was able to better
predict the test set, compared to the 90°C MLP model.

The established discriminant and correlation models have shown the
potential of the electronic nose to be used as a complementary approach to
sensory evaluation to determine light-oxidized off-flavors in packaged milk and

Cheddar cheese.

Recommendations for future work

1. A standardized method for evaluation of light-induced changes in different
foods is needed.

2. Preconcentration techniques such as DH and SPME may be used for
headspace sampling, to improve the differentiation of milk with different
extent of light oxidation.

3. For low— or non- volatile off-flavored compounds in liquid or aqueous
systems (e.g. certain packaging off-flavors), an electronic tongue consists
of an array of liquid sensors can perform in a similar manner as an
electronic nose to the volatiles. The technology has been commercialized
and used in many applications such as analyzing beverages (e.g. wine
[191], juices [192, 193], and milk [193]) and monitoring fermentation [194].

4. Numerous mathematical techniques have been proposed to compensate
the sensor drifts and to reduce the instability of the discriminant models for
quality control routine. Verification and improvement of these long term

calibration techniques in food and packaging applications are needed.

221

APPENDICES

A.1. SAS and Matlab programs

A.1.1. Data import in SAS
(SAS 8.0, SAS Institute, Cary, NC)

/*Input macro variables below:*/

%let dsn = mk9501; /*training data set name*/

%let dsnv = mk9502; /*test data set name*/

%let score = mkscore; /*sensory scores of training set*/
%let scorev = mkscore; /*sensory scores of test set*/

data &dsn;

infile "d:\ehose\&dsn..txt" firstobs=2 expandtabs;

input obs idS n samp$ LY_LG LY_G LY_AA LY_Gh LY_gCTl LY_gCT T30_1 PlO_1
P10_2 P40_l T70_2 PA2;

if samp = 'CTR' then samp = '0'; if samp = '2LT' then samp = '2';
if samp = '4LT' then samp = '4'; if samp = '8LT' then samp = '8';
if samp = '12L' then samp = '12'; if samp = '24L' then samp = '24';
if samp = '36L' then samp = '36'; if samp = '48L' then samp = '48';
if samp = '48D‘ then samp = 'd';

run;

data &score;
infile "d:\enose\&score..txt" firstobs=2 expandtabs;
input smean smed logpen logdmds loghex; run;

data &dsn; merge &dsn &score; run;

data &dsnv;

infile "d:\enose\&dsnv..txt" firstobs=2 expandtabs;

input obs id$ n samp$ LY_LG LY_G LY_AA LY_Gh LY_gCTl LY_gCT T30_l P10_1
P10_2 P40_l T70_2 PA2;

if samp = 'CTR' then samp = '0'; if samp = '2LT' then samp = '2';
if samp = '4LT' then samp = '4'; if samp = '8LT' then samp = '8';
if samp = '12L' then samp = '12'; if samp = '24L' then samp = '24';
if samp = '36L' then samp = '36'; if samp = '48L' then samp = '48';
if samp = '48D' then samp = 'd';

run;

data &scorev;
infile "d:\enose\&scorev..txt" firstobs=2 expandtabs;
input smean smed logpen logdmds loghex; run;

data &dsnv; merge &dsnv &scorev; run;

222

A.1.2. HCA and PCA using SAS

options ls=64 ps=45 nodate nonumber;
goptions reset=all;

/*Input macro variables below:*/
%let dsn = mk9501; /*training data set name*/
%let sn = 950C; /*sample name, in text*/

/*Hierarchical Clustering Analysis*/
/*Single linkage (nearest neighbor) as the algorithm for similarities.

*/

proc cluster data=&dsn noeigen method=single nonorm out=clout;

id id;

var LY_LG LY_G LY_AA LY_Gh LY_gCTl LY_gCT T30_l PlO_l PlO_2 P40_l T70_2
PA2;

run;

proc tree data=clout;

id id;

title2 "Hierarchical Clustering Analysis (&sn)";
run;

/*Principle component analysis: proc princomp*/
/*Compute principle components from covariance matrix.
Proc factor was applied to generate scree plot.*/

proc princomp data=&dsn cov out=sout;

var LY_LG LY_G LY_AA LY_Gh LY_gCTl LY_gCT T30_l PlO_l P10_2 P40_l T70_2
PA2;

run;

proc factor data=&dsn scree;

var LY_LG LY_G LY_AA LY_Gh LY_gCTl LY_gCT T30_l P10_l PlO_2 P40_l T70_2
PA2;

run;

data labels;

set sout;

retain xsys '2' ysys '2';
y=prin2;

x=prinl;

text=samp;

keep xsys ysys x y text;

proc gplot data=sout;

plot prin2*prinl /annotate=labels;

label prinl='PCl' prin2='PC2';

symbol v=none;

title2 "Principle Component Analysis (&sn)";
run;

223

data label2;

set sout;

retain xsys '2' ysys '2';
y=prin3;

x=prin2;

text=samp;

keep xsys ysys x y text;

proc gplot data=sout;

plot prin3*prin2 /annotate=labe12;

label prin2='PC2' prin3='PC3';

symbol v=none;

title2 "Principle Component Analysis (&sn)";
run;

data label3;

set sout;

retain xsys '2' ysys ‘2' zsys '2';
z=prin3;

y=prin2;

x:prinl;

text=samp;

keep xsys ysys zsys x y 2 text;

proc g3d data = sout;

scatter prin2*prinl = prin3 /grid shape = 'point' annotate=label3;
label prinl='PCl' prin2='PC2' prin3 = 'PC3';

title2 "Principle Component Analysis 3D_l (&sn)";

run;

data label4;

set sout;

retain xsys '2' ysys '2' zsys '2';
z=prin3;

y=prinl;

x=prin2;

text=samp;

keep xsys ysys zsys x y 2 text;

proc g3d data = sout;

scatter prinl*prin2 = prin3 /grid shape = 'point' annotate=label4;
label prinl=‘PCl' prin2='PC2' prin3 = 'PC3';

title2 "Principle Component Analysis 3D_2 (&sn)";

run;

224

A.1.3. LDA/QDA and k-NN using SAS
goptions reset=all;

/*Input macro variables below:*/

%let dsn = mk4501; /*training data set name*/
%let dsnv = mk4502; /*validation data set name*/
%let sn = 450C; /*sample name, in text*/

/*Testing the Equality of Normal Population Parameters*/

proc discrim data=&dsn method=normal pool=test wcov pcov bcov manova;
class samp;

var LY_LG LY_G LY_AA LY_Gh LY_gCTl LY_gCT T30_l PlO_l PlO_2 P40_1 T70_2
PA2;

title2 "Testing the Equality of Normal Population Parameters (&sn)";
run;

/*LDA: normal populations with same cov*/

proc discrim data=&dsn out=lout outstat=lstat
method=normal list pool=yes pcov manova
crosslist outcross = lcross
testdata=&dsnv testlist testout = ltest;
class samp;
var LY_LG LY_G LY_AA LY_Gh LY_gCTl LY_gCT T30_1 P10_l PlO_2 P40_l T70_2
PA2;
title2 "Linear Discriminant Analysis (&sn)”;
run;

/*QDA: normal populations with different cov*/

proc discrim data=&dsn out=qout outstat=qstat
method=normal list pool=no wcov
crosslist outcross = gcross
testdata=&dsnv testlist testout = gtest;
class samp;
var LY_LG LY_G LY_AA LY_Gh LY_gCTl LY_gCT T30_l PlO_l PlO_2 p40_1 T70_2
PA2;
title2 "Quadratic Discriminant Analysis (&sn)";
run;

/*k-NN; k=1~4*/

/*k=l*/
proc discrim data=&dsn out=klout outstat=klstat
k=1 crosslist outcross = klcross
testdata=&dsnv testlist testout = kltest;
class samp;
var LY_LG LY_G LY_AA LY_Gh LY_gCTl LY_gCT T30_l PlO_1 P10_2 P40_l T70_2
PA2;

title2 'Nonparametric Discriminant Analysis: kNN, k = 1';
run;
/*k=2*/
proc discrim data=&dsn out=k20ut outstat=k2stat
k=2 crosslist outcross = k2cross

225

testdata=&dsnv testlist testout = k2test;
class samp;
var LY_LG LY_G LY_AA LY_Gh LY_gCTl LY_gCT T30_l PlO_l PlO_2 P40_l T70_2
PA2;

title2 'Nonparametric Discriminant Analysis: kNN, k = 2';
run;
/*k=3*/
proc discrim data=&dsn out=k30ut outstat=k3stat
k=3 crosslist outcross = k3cross

testdata=&dsnv testlist testout = k3test;
class samp;
var LY_LG LY_G LY_AA LY_Gh LY_gCTl LY_gCT T30_1 PlO_l PlO_2 P40_l T70_2
PA2;

title2 'Nonparametric Discriminant Analysis: kNN, k = 3';
run;
/*k=4*/
proc discrim data=&dsn out=k4out outstat=k4stat
k=4 crosslist outcross = k4cross

testdata=&dsnv testlist testout = k4test;
class samp;
var LY_LG LY_G LY_AA LY_Gh LY_gCTl LY_gCT T30_l PlO_l PlO_2 P40_1 T70_2
PA2;
title2 'Nonparametric Discriminant Analysis: kNN, k = 4';
run;

/*Canonical Discriminant Analysis*/

proc discrim data=&dsn outzcanout outstat=canstat
canonical crosslist outcross = cancross
testdata=&dsnv testlist testout = cantest;
class samp;
var LY_LG LY_G LY#AA LYhGh LY_gCTl LY*gCT T30_l PlO_l PlOWZ P40w1 T70_2
PA2;
title2 "Canonical Discriminant Analysis (&sn)";
run;

/*Plot Canonical Discriminant Model: 2D and 3D*/
/*2D plot-model*/

data labels;

set cancross;

retain xsys '2' ysys '2';

y=can2;

x=canl;

text=samp;

keep xsys ysys x y text;

proc gplot data=cancross;

plot can2*canl /annotate=labels;

label canl='CANl' can2='CAN2';

symbol v=none;

title2 "Canonical Discriminant Analysis: Model (&sn)";
run;

/*3D plot—model*/
data labelB; set cancross;

226

 

retain xsys '2' ysys '2' zsys '2';
z=can3; y=can2; x=canl;

text=samp;

keep xsys ysys zsys x y 2 text;

proc g3d data = cancross;
scatter can2*can1 = can3 /grid shape = 'pointf annotate=label3;
label canl='CANl' can2='CAN2' can3 = 'CAN3';

title2 "Canonical Discriminant Analysis: Model 3D (&sn)";
run;

/*Plot Canonical Discriminant Test Data; labeled with original
grouping*/

/*2D plot-test*/

data labels;

set cantest;

retain xsys '2' ysys '2';

y=can2;

x=canl;

text=samp;

keep xsys ysys x y text;

proc gplot data=cantest;

plot can2*canl /annotate=labels;

label canl='CANl' can2='CAN2';

symbol v=none;

title2 "Canonical Discriminant Analysis: Test (&sn)";
run;

/*3D plot test*/

data label3; set cantest;

retain xsys '2' ysys '2' zsys '2';
z=can3; y=can2; x=canl;

text=samp;

keep xsys ysys zsys x y 2 text;

proc g3d data = cantest;

scatter can2*canl = can3 /grid shape = 'point' annotate=label3;
label canl='CANl' can2='CAN2' can3 = 'CAN3';

title2 "Canonical Discriminant Analysis: Test 3D (&sn)";

run;

/*Plot Canonical Discriminant Test Data; labeled with identified
grouping*/

/*2D plot-testid*/

data labels;

set cantest;

retain xsys '2' ysys '2';

y=can2;

x=canl;

text=_into_;

keep xsys ysys x y text;

proc gplot data=cantest;

plot can2*canl /annotate=labels;
label canl='CANl' can2='CAN2';
symbol v=none;

227

title2 "Canonical Discriminant Analysis: Test_id (&sn)";
run;

/*3D plot testid*/

data label3; set cantest;

retain xsys '2' ysys '2' zsys '2';
z=can3; y=can2; x=canl;
text=_into_:

keep xsys ysys zsys x y 2 text;

proc g3d data = cantest;

scatter can2*canl = can3 /grid shape = 'point' annotate=labe13;
label canl='CANl' can2='CAN2' can3 = 'CAN3';

title2 "Canonical Discriminant Analysis: Test_id 3D (&sn)";
run;

/*Export classification results of resubsitution, cross-validation and
test data*/
data classes;
set lout(rename=_into_=lout); set lcross(rename=_into_=lcross); set
ltest(rename=_into_=ltest);
set gout(rename=_into_=qout); set qcross(rename=_into_=qcross); set
qtest(rename=_into_=qtest);
set klout(rename=_into_=klout); set klcross(rename=_into_=klcross); set
kltest(rename=_into_=k1test);
set k20ut(rename=_into_=k2out); set k2cross(rename=_into_=k2cross); set
k2test(rename=winto_=k2test);
set k3out(rename=_into_=k3out); set k3cross(rename=_into_=k3cross); set
k3test(rename=_into_=k3test);
set k4out(rename=_into_=k4out); set k4cross(rename=_into_=k4cross); set
k4test(rename=_into_=k4test);
set canout(rename=_into_=canout); set cancross(rename=_into_=cancross);
set cantest(rename=_into_=cantest);
keep samp lout lcross ltest gout gcross qtest klout klcross kltest
k20ut k2cross k2test k30ut k3cross k3test

k4out k4cross k4test canout cancross cantest;
run;

228

A.1.4. PLS using SAS and MLP using Matlab

A.1.4.1. Partial Least Square Analysis (PLS)

options ls=64 ps=45 nodate nonumber;

goptions reset=all;

%INCLUDE 'd:\enose\plsplot.sas';

/* plsplot.sas is available at http://www.sas.com */
/********‘kir*******~k******************************~k***+*********/

/*Partial Least Square (PLS) sensory scores vs.sensor response*/

/*Input macro variables below:*/

%let dsn = mk9501; /*training data set name*/
%let dsnv = mk9502; /*validation data set name*/
%let sn = 950C; /*sample name, in text*/
%let y_name = smean; /*Y variable(s) */

%let x_name = enose; /*X variables (5) */

%global xvars yvars predname resname xscrname yscrname num_x num_y lv;
%let xvars=LY_LG LY_G LY_AA LY_Gh LY_gCTl LY_gCT T30_1 PlO_l PlO_2
P40_l T70_2 PA2;

%let yvars=smean;

%let ypred=yhat1;

%let yres=yresl;

%let predname=yhat;

%let resname=res;

%let xscrname=xscr;

%let yscrname=yscr;

%let num_y=l;

%let num_x=12;

title2 "PLS (&sn) Y: &y_name; X's: &x_name";

proc pls data=&dsn method=pls(algorithm=svd) outmodel=estl
cv=one censcale;

model &yvars = &xvars;

output out=outpls p=&ypred yresidual=&yres

xresidual=xresl~xre812 xscore=xscr yscore=yscr

stdy=stdy stdx=stdx h=h press=press t2=t2

xqres=xqres yqres=yqres;

run;

/*Stop here and check the output*/
/************************************************+******************~k/
/* According CVTEST result, decide lv = number of latent components */

%let lv=4; *** Number of PLS components in model ***;
/**************+*******+*****~k**+***********************+~k**~k***++++11r/

/*Plot predicted vs. actual scores*/

data labels; set outpls;

retain xsys '2' ysys '2'; y=&ypred; x=&yvars;
text=samp;

keep xsys ysys x y text;

proc gplot data=outpls;

229

plot &ypred*&yvars /annotate=labels;

label &ypred='Predicted' &yvars='Actual ADSA Flavoring Score';
symbol v=none;

run;

/*Plot predicted vs. actual scores with 450 ref line */

data anno; _

function='move'; xsys='l'; ysys='l'; x=O; y=O; output;

function='draw'; xsys='l'; ysys='l'; color='red'; x=lOO; y=lOO; output;
run;

proc gplot data=outpls;

plot &ypred*&yvars / anno=anno haxis=axisl vaxis=axis2;

label &ypred='Predicted' &yvars='Actual ADSA Flavoring Score';
symboll i=none v=dot c=blue;

run;

%plot_scr(OUtP15)7
%plotxscrloutplS);

%res_plot(outpls);
%nor_plot(outpls);

%get_bpls(estl,dsout=bpls);
%get_vip(estl,dsvip=vip_data);
data eval;

merge bpls vip_data;

run;

proc print data=eval;

run;

/*******~k*******************************~k*****************************/

/*PLS: Analyzing test data using the PLS model above*/
goptions reset=all;
title2 "PLS (&sn) Y: &y_name; X's: &x_name";

/*step 1: refit the model with missing values on validation set*/
data dsnj; set &dsn &dsnv(drop=&yvars); run;

proc pls data=dsnj method=pls(algorithm=svd) outmodel=estl cv=one;
model &yvars = &xvars;

output out=outpl52 p=&ypred yresidual=&yres

xresidual=xresl-xrele xscore=xscr yscore=yscr

stdy=stdy stdx=stdx h=h press=press t2=t2

xqres=xqres yqres=yqres;

run;

/*step 2: put the predicted values and actual observations in the same
data set. */

data outplsv; set outplsZ; if obs > 36; keep &ypred samp n; run;

data dsnv; set &dsnv(keep=&yvars samp n); run;

data predict; merge dsnv outplsv; run;

/*Plot predicted vs. actual scores*/

data labels; set predict;

retain xsys '2' ysys '2'; y=&ypred; x=&yvars;
text=samp;

keep xsys ysys x y text;

230

proc gplot data=predict;

plot &ypred*&yvars /annotate=labels;

label &ypred='Predicted' &yvars='Actual ADSA Flavoring Score';
symbol v=none;

run;

/*step 3: calculate the residuals at the points in the test set. */
data predictr; set predict; '

yresl=&yvars—&ypred;

run;

%res_plot(predictr);

/***************~k******************~k********************************~k*/

*Export the predicted values and residuals, for calculating root-mean-
square (rme) of the models*/

data &dsn.rme;

set outpls (rename=yresl=resOl); set predictr (rename=yresl=re502);
keep resOl resO2;

run;

PROC EXPORT DATA: WORK.&dsn.rme
OUTFILE= "D:\tmp\&dsn.rme.xls"
DBMS=EXCELZOOO REPLACE;

RUN;

231

A.1.4.2. Multilayer Perceptrons (MLP)
The Neural Network Toolbox 4.0 of Matlab 6.1 (The MathWorks Inc., Natick, MA)

was launched (Figure 101) by typing in

 

>> nntool
-.= NetworldData Manager
npuls Networks Outputs
,_,,____ it“!
Targets Errors
npul Delay States Layer Dela‘r States

Networks and Data

 

[ Help HNewDala j[NewNetworln ]

HEMCM‘S only

 

 

 

 

Figure 101 Start-up view of Neural Network Toolbox 4.0 embedded in Matlab
6.1.

A feed-forward backpropagation network was constructed, with a tan-
sigmoid function in the hidden layer, and a linear function in output layer. An
independent data set was applied for validation (early stopping to avoid over-

fitting) as well as for test (estimating a network’s ability to generalize).

232

A.2 Designs and Forms of Sensory Tests
A.2.1. Triangle tests

A.2.1.1. Triangle test form (an example)

 

Triangle Test

 

 

 

 

Name Date 9/26/2002
Sample: 2% milk # 1
Instructions

Four independent sets (D, A, B, C) of samples will be presented sequentially.

Each set has 3 samples; 2 are identical; determine which one is the odd sample.
If no difference is apparent, you must guess.

Smell and taste samples from left to right.
You may spit the sample out after judging (foam cup is provided).
Rinse your mouth by water between samples if necessary.

Which is the odd sample? (Check the box)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Set A D 425 u 565 El 716

Set B D 513 El 422 D 641

Set C [3 356 C] 562 D 814

Set D r: 610 E] 701 D 375
COMMENTS:

 

233

A.2.1.2. Design of triangle tests on light-oxidized 2% milk

 

 

 

 

 

 

 

 

 

 

 

 

 

Set Sample Treatment Coding
A CONTROL without light exposure 102 763 685
24 HR 24 hours of light exposure 893 582 967
B CONTROL without light exposure 204 665 048
12 HR 12 hours of light exposure 912 462 460
C CONTROL without light exposure 506 615 951
8 HR 8 hours of light exposure 884 046 169
D CONTROL without light exposure 473 133 371
4 HR 4 hours of light exposure 683 464 581
Panelist Serve A B C D
Order

1 ABDC 893 763 967 912 665 048 884 615 951 473 464 581

2 ACDB 102 582 685 912 462 048 884 046 951 473 133 581

3 ADBC 893 582 685 204 462 048 884 615 169 473 464 371

4 ABCD 893 582 685 912 665 460 506 615 169 683 133 371

5 BDCA 893 582 685 912 462 048 506 615 169 473 133 581

6 DBCA 102 763 967 204 462 460 884 046 951 683 133 581

7 ADCB 893 763 685 204 665 460 506 046 169 683 133 371

8 DBAC 893 763 685 204 462 460 506 046 951 683 133 581

9 BDAC 102 582 685 912 462 048 884 615 169 683 464 371

10 CABD 893 763 685 912 665 460 506 046 951 683 133 371

1 1 BCDA 102 582 967 912 665 048 506 046 169 683 464 371

12 DCAB 893 763 967 204 665 460 506 046 169 683 464 371

13 CDAB 893 763 967 912 665 460 884 615 169 473 464 581

14 BCAD 893 763 967 204 462 048 506 046 951 473 464 371

15 BADC 102 582 685 204 462 048 884 046 951 473 464 581

16 DACB 102 763 967 912 665 048 884 615 951 473 133 581

17 CDBA 102 582 967 912 665 048 884 615 169 683 464 371

18 CADB 102 763 967 204 462 460 506 615 169 683 133 581

1 9 CBAD 893 763 685 204 665 460 506 046 951 473 464 371

20 DCBA 102 582 967 204 462 048 884 615 951 473 464 581

21 DABC 102 582 685 912 665 460 506 046 169 473 464 371

22 BACD 102 763 967 204 462 460 506 615 169 473 133 581

23 CBDA 893 582 685 912 462 048 884 615 951 683 133 371

24 ACBD 102 582 967 204 665 460 884 046 951 683 133 581

 

 

 

 

 

 

234

 

 

A.2.1.3. Design of triangle tests on water stored in various packaging materials

 

 

 

 

 

 

 

 

 

 

 

 

 

Set Sample Treatment Coding

A CONTROL 4L glass jugs 472 271 875
GLASS 237 ml glass bottles, 979 252 737
B CONTROL 4L glass jugs 681 831 709
HDPE 237 ml HDPE bottles 379 502 428
C CONTROL 4L glass jugs 150 378 853
PET 237 ml PET bottles 697 860 593
D CONTROL 4L glass jugs 838 370 546
CARTON 237 ml paper cartons 568 702 444

Panelist Serve A B C D

Order

1 CBAD 979 271 875 681 502 709 150 860 593 568 370 546
2 DACB 472 271 737 681 831 428 697 378 853 838 702 444
3 DBAC 979 271 875 379 502 709 697 860 853 568 370 444
4 BADC 472 252 875 379 502 709 1 50 860 593 568 702 546
5 ADCB 472 271 737 379 831 428 1 50 860 593 838 702 444
6 CDAB 979 271 737 379 831 428 697 378 593 838 370 444
7 ACBD 472 252 737 681 502 428 1 50 378 593 568 370 546
8 ABDC 979 271 875 379 831 428 150 860 853 838 702 546
9 CDBA 472 252 737 379 831 709 150 378 593 568 370 444
10 BDCA 979 252 875 379 831 709 150 860 853 838 370 444
1 1 DCBA 472 252 737 681 502 709 150 860 853 838 702 546
12 BDAC 979 271 875 681 831 428 697 378 593 568 370 546
13 DCAB 979 271 737 681 502 428 697 378 853 838 370 444
14 DABC 472 252 875 681 502 428 1 50 860 853 838 702 444
15 BACD 472 271 737 681 831 428 150 378 593 568 702 546
16 ABCD 979 252 875 379 502 709 697 860 853 838 702 546
17 ADBC 472 252 875 681 502 709 150 378 593 568 370 444
18 CABD 472 252 875 379 831 709 697 378 593 568 702 546
19 BCAD 979 271 737 681 831 428 697 378 593 568 702 546
20 DBCA 979 252 875 379 831 709 697 860 853 838 702 546
21 CBDA 979 252 875 681 502 428 697 860 853 838 702 444
22 ACDB 979 271 737 379 502 709 150 860 593 838 370 444
23 CADB 472 271 737 681 502 709 697 378 853 568 370 546
24 BCDA 472 252 737 379 831 428 697 378 853 568 370 444

 

 

 

 

 

 

235

 

 

A.2.1.4. Design of triangle tests on 2% milk stored in various packaging materials

 

 

 

 

 

 

 

 

 

 

 

 

 

Set Sample Treatment Coding
A CONTROL 4L glassjugs 371 694 716
GLASS 237 ml glass bottles 425 565 511
B CONTROL 4L glass jugs ’ 873 732 641
HDPE 237 ml HDPE bottles 513 422 172
C CONTROL 4L glass jugs 356 434 116
PET 237 ml PET bottles 498 562 814
D CONTROL 4L glassjugs 212 701 924
CARTON 237 ml paper cartons 610 493 375
Panelist Serve A B C D
Order

1 DABC 425 565 716 513 422 641 356 562 814 610 701 375

2 DCAB 371 565 51 1 873 422 641 498 434 1 16 610 493 924

3 BDAC 425 694 716 873 422 172 356 562 814 212 701 375

4 BDCA 425 694 716 513 732 641 356 434 814 610 701 375

5 ADCB 425 694 511 513 732 172 498 434 116 610 701 375

6 CBAD 371 565 716 873 422 641 498 562 1 16 212 493 375

7 CDAB 371 565 511 513 732 641 498 562 116 212 701 375

8 DBAC 425 694 51 1 873 732 172 498 562 1 16 212 701 375

9 BCDA 371 694 51 1 513 732 641 356 562 116 610 493 924

10 BACD 371 694 511 873 732 172 498 434 814 212 701 375

1 1 CDBA 371 565 51 1 873 422 172 356 562 814 212 493 924

12 DCBA 425 694 716 513 422 641 498 434 814 212 493 375

13 CBDA 371 565 716 873 732 172 498 434 814 610 701 375

14 ACBD 425 565 716 513 732 172 356 562 116 212 493 924

15 ACDB 371 694 51 1 873 422 641 356 434 814 610 701 924

16 ADBC 371 565 716 513 422 641 356 562 116 212 493 375

17 ABCD 425 694 716 513 732 172 498 434 814 610 701 924

18 DACB 425 694 51 1 513 732 172 356 562 814 610 493 924

19 CADB 371 694 51 1 513 422 641 498 434 1 16 212 493 924

20 BADC 425 565 716 513 732 641 356 562 1 16 610 701 924

21 DBCA 425 565 716 873 732 172 498 562 116 212 493 924

22 ABDC 425 694 51 1 873 422 172 356 434 814 610 701 924

23 CABD 371 565 51 1 873 422 172 498 434 1 16 610 493 924

24 BCAD 371 565 716 873 422 641 356 434 814 212 493 375

 

 

 

 

 

 

 

 

236

A.2.1.5. Design of triangle tests on 2% milk stored in various packaging materials
and exposed to 12 hours of fluorescent light

 

 

 

 

 

 

 

 

 

 

Set Sample Treatment Coding
A CONTROL 237 ml glass bottles, without light exposure 950 606 891
HDPE 237 ml HDPE bottles, 12 hours of light exposure 231 485 762
B CONTROL 237 ml glass bottles, without light exposure 921 176 635
HDPE-Tl02 237 ml HDPE-Ti02 bottles, 12 hours of light exposure 738 405 916
C CONTROL 237 ml glass bottles, without light exposure 681 831 709
PET 237 ml PET bottles , 12 hours of light exposure 379 502 428
D CONTROL 237 ml glass bottles, without light exposure 304 193 202
CARTON 237 ml paper cartons, 12 hours of light exposure 189 682 541

 

 

 

. Serve
Panelist Order A B C D
1 CBDA 231 606 891 738 176 635 681 831 428 189 193 202
2 DBCA 950 606 762 738 176 635 681 831 428 189 193 202
3 CDBA 231 606 891 921 405 635 681 502 428 304 682 541
4 DCBA 950 485 891 921 405 916 379 831 709 304 682 202
5 ACDB 950 485 762 921 405 916 379 502 709 304 682 541
6 ADBC 231 606 762 738 176 916 681 502 709 189 193 202
7 BCDA 231 606 891 738 405 635 681 831* 428 189 682 202
8 BADC 231 485 891 921 176 916 379 502 709 304 682 202
9 DCAB 950 485 891 921 176 916 379 502 709 189 682 202
10 ABDC 231 606 762 921 405 635 379 831 428 189 682 202
1 1 CADB 950 485 762 921 405 635 379 831 428 189 193 541
12 BDCA 231 606 762 738 176 635 681 502 428 189 193 541
13 DBAC 231 485 891 738 176 635 379 831 709 304 682 541
14 CABD 231 606 891 738 176 916 379 831 428 304 193 541
15 ADCB 950 606 762 738 176 916 681 502 709 304 682 202
16 DABC 950 485 762 921 176 916 379 502 709 189 193 541
17 BDAC 950 485 762 921 405 916 681 502 428 304 682 202
18 CDAB 231 485 891 738 405 635 379 831 709 304 682 541
19 CBDA 950 606 762 921 405 916 681 502 709 304 193 541
20 DBCA 950 485 891 738 176 916 681 502 709 189 193 541
21 CDBA 231 606 762 738 405 635 379 831 428 304 193 541
22 DCBA 950 485 891 921 176 916 681 502 428 189 193 202
23 ACDB 231 485 891 738 405 635 379 831 709 304 193 541
24 ADBC 950 606 762 921 405 635 681 831 428 189 682 202

 

 

 

 

 

 

 

 

237

A.2.2. Milk scoring card based on ADSA guidelines

 

 

 

SCORE SHEET FOR MILK (SET A)
NAME: GENDER: '3 F l M AGE: DATE:
DIRECTIONS: Rate milk samples (from left to right) for the criticisms. # 1
Specify the intensity of the off—flavors (S: Slight, D: Definite, P: Pronounced)
For example, a sample with slight cooked flavor is rated at ‘cooked' column as 0 S
SAMPLE 621 444 615 791 921 738 176
FLAVOR (1-10) . ..._ __ _
ACID 0 _ O _ O __ O _ O _ O _ O __
BITTER O__ O_ O__ O____ O_ O_ O____
COOKED O_ O_ O_ O__ O_ O_ O_
FEED O _ O __ O _ O __ O _ O _ O _
FERMENTED/FRUITY O __ O _ O _ O _ O _ O _ O _
FLAT 0 _ O __ O _ O _ O __ O _ O _
FOREIGN O_ O__ O__ O_ O_ O_ O_
GARLIC/ONION O __ O _ O __ O _ O _ O _ O _
LACKS FRESHNESS O _ O _ O _ O _ O __ O _ O _
MALTY O __ O _ O __ O _ O _ O _ O _
OXIDIZED-LIGHT O __ O _ O _ O _ O _ O _ O _
OXIDIZED—METAL O __ O _ O _ O _ O _ O _ O _
RANCID O_ O_ O_ O__ O_ O__ O_
SALTY O __ O __ O __ O __ O _ O _ O __
UNCLEAN O_ O_ O_ O_ O___ O_ 0—
COMMENT:

 

h
0
0|

l||||||l||l|||

OOOOOOOOOOOOOOO

 

 

238

A.2.3. Score cards for difference-from-control and ADSA rating of light-oxidized
Cheddar cheese

 

SCORE SHEET FOR CHEDDAR CHEESE

NAME: DATE:

GENDER: E F U M AGE: (#1)

DIRECTIONS: Rate cheese samples (coded in 3-digit numbers) and compare with the control. Specify the
intensity of the criticisms (S: Slight, D: Definite, P: Pronounced).

1. coLoR DIFFERENCE FROM cONTROL

SAMPLE 405 391 738 821 544 921 615 176
NODIFFERENCE - O O O 1 O. 0;; 0 . 0 , ;.0
VERY SLIGHT DIFFERENCE O O O O O O O O
SLIGHTDIFFERENCE O O 0 ‘ ' O; ' O 70;"; TO” 10""
MODERATE DIFFERENCE O O O O O O O O
LARGEDIFFERENCE ‘0 O 30: " :0 i 0 [00 £250
EXTREME DIFFERENCE O O O O O O O O

2. BODY AND TEXTURE: (1-5)- please neglect “gassy” and “open" criticisms

.‘SAMPLE. .405 3391.31,. 788 -..1.szi'7- 544 921 £615 175:

BODYAND
TEXTURE(1-5) —— — — — -——— —— — —

CORKY
CRUMBLY

    
 
 

 

 

  
 

PASTY
SHORT
WEAK

COMMENT:

*BODY AND TEXTURE DIFFERENCE FROM CONTROL

SAMPLE 405 391 738 821 544 921 6‘1 5 176
NO DIFFERENCE O O O O O O O O
VERY SLIGHT DIFFERENCE O O O , O 0 O O O
SLIGHT DIFFERENCE 0 O O O O O O O
MODERATE DIFFERENCE O O O O O O O 0
LARGE DIFFERENCE O O O O O O O O
EXTREME DIFFERENCE O O O O O O O O

 

 

 

 

 

3. FLAVOR (1 -1 O)

SAMPLE 405 391 738 821 544 921 615 176

FLAVOR
(1 -1 0) —
BITTER O
FEED O

FERMENTED O

F LAT/LOW
FLAVOR

FRUITY
HEATED
HIGH ACID
OXIDIZED
RANCID
SULFIDE
UNCLEAN
WHEY TAINT

YEASTY
COMMENT:

 

0000000000000

0000000000000

I
OOOOOOOOOOOOIO
|

0000000000000

0000000000000

|
|
l
I
I
OOOOOOOOOOOIOO
I
I

O

I
I
I
I
I
I
I
I

OO:OOOOOOOOOOO
I
l
I
I
|
|

000000000

*FLAVOR DIFFERENCE FROM CONTROL (0-5)

SAMPLE 405 391 738 821 544 921 615 176

No DIFFERENCE , O 0 O O O 0 O O
VERY SLIGHT DIFFERENCE O O O O O O O O
SLIGHT DIFFERENCE 0 O 0 O 0 0 0 O
MODERATE DIFFERENCE O O O O O O 0 0
LARGE DIFFERENCE O O 0 O 0 0 0 O
EXTREME DIFFERENCE O O O O O O O O

(#1)

 

240

 

 

A.2.4 Designs of difference-from-control and ADSA rating of light-oxidized
Cheddar cheese

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Specimen Position Light Exposure (wk) Coding

Interior 0 821

Interior 2 391

Interior 4 738

Interior 6 405

Surface 0 544

Surface 2 615

Surface 4 921

Surface 6 176

. Specimen
Panel's“ 1 2 3 4 5 6 7 8

1 405 391 738 821 544 921 615 176
2 738 405 391 821 921 544 176 615
3 405 391 821 738 921 176 544 615
4 391 405 821 738 615 176 544 921
5 738 405 821 391 921 544 615 176
6 405 821 391 738 921 615 176 544
7 405 738 391 821 615 176 921 544
8 821 738 405 391 615 921 176 544
9 738 821 405 391 615 544 921 176

 

241

 

A.3 Boxplot Interpretation

A box plot (or box-and-whisker plot, [195]) displays a statistical summary

of a variable: median, quartiles, range and, possibly and extreme values.

........................

nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn

......................

 

 

 

 

* .................
* .................

Data point(s) larger than
Q(.75)+1.5 IQR

Largest data point
less than or equal
to Q(.75)+1.5 iQR

C(75)

Q(.5) = Median

- Mean

C(25)

Smallest data point ,
bigger than or equal
to Q(.25)-1.5 [QR

Data point(s) less than
Q(.25)-1.5 [QR

Figure 102 Boxplot Interpretation. IQR: interquartile range. Q(.25): 25th
percentile; Q(.5): 50th percentile; Q(.75): 75th percentile.

242

A.4. Preliminary results
Several sets of preliminary experiments were performed. To access the
repeatability and reproducibility of the sample preparation, sensory evaluation

and instrumental analysis, the results may be compared to those in Chapter 4.

A41. Light—oxidized off-flavors in 2% milk

A.4.1.1. Sensory evaluation

Table 28 Sensory scores of light-oxidized 2% milk based on ADSA guidelines.
Scores were given by an 8-member trained panel.

 

Duration of light

Senso score
exposure (hours) ry

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

4 7.38 :l:1.60
8 7.00 12.07
12 5.75 i255
24 5.63 12.92
10—
9- l : I
E 8 “ .
O 7 - °
8 6 _ j 0 0
tr 5 —
g 4 _ * l .
LI. 3 fl
1 _
0 _ I I I I
4 8 12 24
TIME (HOURS)

Figure 103 Flavor sensory scores based on ADSA guidelines of 2% milk in
glass bottles exposed to 4, 8, 12 and 24 hours of fluorescent light (1000 Ix, 5°C).
0: mean values of each treatment; *: possible outliers.

243

 

A.4.1.2. Headspace analysis using SPME-GC

Area Response Area Response

Area Response

Figure 104 Calibration curves of (a) pentanal (b) hexanal and (c) dimethyl

   
  
   
 

 

 

16000 ~
14000 _ (a) PEN y =1027.7x + 1070.1
12000 _ R’=0.8664 :
10000 ~ 0
e
8000 .
6000 - e
4000 - 9
2000 .
0 T F l l
0 2 4 6 8 10
Concentration (nglml)
12000 ‘ (b) HEX y = 829.45x + 1759.7
10000 g R2 = 0.8976

 

 

 

Concentration (nglml)

(c) DMDS y = 653.26x + 334.44
8000 - R2 = 0.8327

00

 

 

 

Concentration (nglml)

disulfide in 2% milk.

244

 

A.4.1.2. Headspace analysis using the electronic nose

 

 

 

 

0.02:
. 09 4
2 4 2
0.01- 8 2 O
as 12 24
8i It
8 0.00: 36
N 123624 0
8 48
-0.01- 43 36
-002- 1224 36
00810.06 -004 -0.02 0.00 0.02 0.04 0.06 0.08

PC1: 74.66%

Figure 105 PCA based on 45°C electronic nose sensor responses of 2% milk
samples exposed to light (1000 Ix) for 0, 2, 4, 8, 12, 24, 36 and 48 hours.

 

CAN2: 31.84%

 

12

12

12

12

 

 

-2 0

2 4

CAN1 : 57.05%

10

Figure 106 LDA based on 95°C electronic nose sensor responses of 2% milk
samples exposed to light (1000 Ix) for 0, 2, 4, 8 and 12 hours at 5°C.

245

 

A.4.2. Light oxidation of Cheddar cheese

A.4.2.1. Discoloration of Cheddar cheese

Table 29 CIE L* a* b* values of Cheddar cheese samples exposed to
fluorescent light (2000 Ix) for 0 and 2 weeks. '

 

 

 

Duration of light ,, ,, ,,
exposure (wks) L a b
0 7485:0421 12.64i0.27 52.06i1.06
2 7404:029 1259:0237 49.50:3.64

 

 

 

 

 

 

1 Standard deviation of the sample

A.4.2.2. Light-induced flavor changes of cheese

 

 

 

 

 

003
450C
0.02
a?
g 0.01
O
N l
8 0.00
-0.01 0
O
_0.02 . 2 45c
-030 -0.20 -0.10 0.00 0.10 0.20 0.30 0.40
PC1:98.70%

Figure 107 PCA based on 45°C and 60°C electronic nose sensor responses of
Cheddar cheese samples exposed to fluorescent light (2000 Ix) for 0 and 2
weeks.

246

 

BIBLIOGRAPHY

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