Table of Contents
Introduction, 1
Chapter 1. A Description of Sample Datasets, 9
1.1. A Description of Example Data Sets, 9
1.1.1. White Corn Tortilla Chips, 9
1.1.2. Muscadine Grape Juices, 18
1.1.3. Fried Mozzarella Cheese Stick Appetizers, 21
1.1.4. Data Sets for Panelist and Panel Performance Evaluation, 25
1.2. References, 25
Chapter 2. Panelist and Panel Performance a Multivariate Experience, 27
2.1. The Multivariate Nature of Sensory Evaluation, 27
2.1.1. A Musical Analogy, 27
2.1.2. Understanding Attributes in Context, 28
2.2. Univariate Approaches to Panelist Assessment, 29
2.2.1. Visualizing Raw Data, 29
2.2.2. PanelCheck, 31
2.2.3. Feedback Calibration, 32
2.3. Multivariate Techniques for Panelist Performance, 32
2.3.1. MANOVA, 32
2.3.2. Normalized RV, 33
2.3.3. Principal Component Analysis, 34
2.3.4. Generalized Procrustes Analysis, 37
2.4. Panel Evaluation Through Multivariate Techniques, 43
2.4.1. GPA for Lexicon Reduction, 43
2.4.2. The ESN Multicountry Projects, 44
2.4.3. The INRA Approach, 45
2.5. Conclusions, 46
2.6. 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
3.5. References, 67
Chapter 4. Deterministic Extensions to Preference Mapping Techniques, 69
4.1. Introduction, 69
4.2. Application and Models Available, 69
4.2.1. Partial Least Squares Regression on Average Data, 70
4.2.2. Response Surface Model for External Mapping, 75
4.2.3. Euclidian Distance Approach, 87
4.3. Conclusions, 89
4.4. 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
5.4.1. Comparison of LSA to External Mapping Methods, 100
5.4.2. Comparison of LSA to Internal Mapping Methods, 104
5.5. References, 109
Chapter 6. Consumer Segmentation Techniques, 111
6.1. Introduction, 111
6.2. Methods Available, 111
6.2.1. Cluster Analysis, 111
6.2.2. Latent Class Models, 112
6.3. Segmentation Methods Using Hierarchical Cluster Analysis, 113
6.4. 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. POM, 144
7.7. Conclusions, 160
7.8. 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
8.3.1. Prediction of Overall Rejection Rate from Attribute
Acceptance Data, 166
8.3.2. Prediction of Overall Rejection Rate from Descriptive
Sensory Attributes, 173
8.3.3. Prediction of Overall Liking from Descriptive Sensory Attributes, 174
8.4. Conclusions, 176
8.5. References, 176
Chapter 9. Application of MARS to Preference Mapping, 179
9.1. Introduction, 179
9.2. MARS Basics, 179
9.2.1. Knots, 182
9.2.2. Basis Functions, 184
9.2.3. Piecewise Linear Regression Splines, 185
9.2.4. Generalized Cross-validation (GCV), 186
9.3. Setting Control Parameters and Refining Models, 187
9.4. Example of Application of MARS, 188
9.5. A Comparison with Partial Least Squares Regression, 201
9.6. 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
10.5.1. Proportional Odds Model (POM), 216
10.5.2. Proportional Hazards Model (PHM), 216
10.5.3. Comparison of Proportional Odds and Hazard Models, 216
10.5.4. Examples, 217
10.6. Use of Dummy Variables to Model JAR Data, 220
10.6.1. Analysis of Covariance with Dummy Variables, 221
10.6.2. Partial Least Squares Regression with Dummy Variables, 224
10.6.3. Analysis Example, 227
10.7. References, 233