Multivariate and Probabilistic Analyses of Sensory Science Problems

Introduction 3

Chapter 1 A Description of Sample Data Sets Used in Further Chapters 9

1.1 A Description of Example Data Sets 9

References 25

Chapter 2 Panelist and Panel Performance: A Multivariate Experience 27

2.1 The Multivariate Nature of Sensory Evaluation 27

2.2 Univariate Approaches to Panelist Assessment 29

2.3 Multivariate Techniques for Panelist Performance 32

2.4 Panel Evaluation through Multivariate Techniques 43

2.5 Conclusions 46

References 47

Chapter 3 A Nontechnical Description of Preference Mapping 49

3.1 Introduction 49

3.2 Internal Preference Mapping 49

3.3 External Preference Mapping (PREFMAP) 58

3.4 Conclusions 66

References 67

Chapter 4 Deterministic Extensions to Preference Mapping Techniques 69

4.1 Introduction 69

4.2 Application and Models Available 69

4.3 Conclusions 89

References 94

Chapter 5 Multidimensional Scaling and Unfolding and the Application of Probabilistic Unfolding to Model Preference Data 95

5.1 Introduction 95

5.2 Multidimensional Scaling (MDS) and Unfolding 96

5.3 Probabilistic Approach to Unfolding and Identifying the Drivers of Liking 98

5.4 Examples 100

References 109

Chapter 6 Consumer Segmentation Techniques 111

6.1 Introduction 111

6.2 Methods Available 111

6.3 Segmentation Methods Using Hierarchical Cluster Analysis 113

References 126

Chapter 7 Ordinal Logistic Regression Models in Consumer Research 129

7.1 Introduction 129

7.2 Limitations of Ordinary Least Square Regression 129

7.3 Odds Odds Ratio and Logit 130

7.4 Binary Logistic Regression 133

7.5 Ordinal Logistic Regression Models 144

7.6 Proportional Odds Model (POM) 144

7.7 Conclusions 160

References 160

Chapter 8 Risk Assessment in Sensory and Consumer Science 163

8.1 Introduction 163

8.2 Concepts of Quantitative Risk Assessment 164

8.3 A Case Study: Cheese Sticks Appetizers 166

8.4 Conclusions 176

References 176

Chapter 9 Application of MARS to Preference Mapping 179

9.1 Introduction 179

9.2 MARS Basics 179

9.3 Setting Control Parameters and Refining Models 187

9.4 Example of Application of MARS 188

9.5 A Comparison with PLS Regression 201

References 205

Chapter 10 Analysis of Just About Right Data 207

10.1 Introduction 207

10.2 Basics of Penalty Analysis 208

10.3 Boot Strapping Penalty Analysis 210

10.4 Use of MARS to Model JAR Data 212

10.5 A Proportional Odds/Hazards Approach to Diagnostic Data Analysis 215

10.6 Use of Dummy Variables to Model JAR Data 220

References 233

Index 237