Statistics for Sensory and Consumer Science

Statistics for Sensory
and Consumer Science......Page 5
Contents......Page 7
Preface......Page 11
Acknowledgements......Page 13
1.1 The Distinction between Trained Sensory Panels and Consumer Panels......Page 15
1.2 The Need for Statistics in Experimental Planning and Analysis......Page 16
1.4 Organisation of the Book......Page 17
2.1 Sensory Panel Methodologies......Page 19
2.2 Consumer Tests......Page 21
3.1 General Introduction......Page 25
3.2 Visual Inspection of Raw Data......Page 29
3.3 Mixed Model ANOVA for Assessing the Importance of the Sensory Attributes......Page 32
3.4 Overall Assessment of Assessor Differences Using All Variables Simultaneously......Page 33
3.5 Methods for Detecting Differences in Use of the Scale......Page 38
3.6 Comparing the Assessors’ Ability to Detect Differences between the Products......Page 41
3.7 Relations between Individual Assessor Ratings and the Panel Average......Page 43
3.8 Individual Line Plots for Detailed Inspection of Assessors......Page 47
3.9 Miscellaneous Methods......Page 48
4.1 Introduction......Page 53
4.2 Correcting for Different Use of the Scale......Page 54
4.3 Computing Improved Panel Averages......Page 57
4.4 Pre-processing of Data for Three-Way Analysis......Page 59
5.1 Introduction......Page 61
5.2 Analysing Sensory Profile Data: Univariate Case......Page 62
5.3 Analysing Sensory Profile Data: Multivariate Case......Page 73
6.1 Introduction......Page 81
6.2 Estimating Relations between Consensus Profiles and External Data......Page 82
6.3 Estimating Relations between Individual Sensory Profiles and External Data......Page 88
7.1 Introduction......Page 93
7.2 Analysis of Data from Basic Sensory Discrimination Tests......Page 94
7.3 Examples of Basic Discrimination Testing......Page 95
7.4 Power Calculations in Discrimination Tests......Page 99
7.5 Thurstonian Modelling: What Is It Really?......Page 100
7.6 Similarity versus Difference Testing......Page 101
7.7 Replications: What to Do?......Page 103
7.8 Designed Experiments, Extended Analysis and Other Test Protocols......Page 107
8.1 Introduction......Page 109
8.2 Preliminary Analysis of Consumer Data Sets (Raw Data Overview)......Page 113
8.3 Experimental Designs for Rating Based Consumer Studies......Page 116
8.4 Analysis of Categorical Effect Variables......Page 120
8.5 Incorporating Additional Information about Consumers......Page 127
8.6 Modelling of Factors as Continuous Variables......Page 131
8.7 Reliability/Validity Testing for Rating Based Methods......Page 132
8.8 Rank Based Methodology......Page 133
8.9 Choice Based Conjoint Analysis......Page 134
8.10 Market Share Simulation......Page 137
9 Preference Mapping for Understanding Relations between Sensory Product Attributes and Consumer Acceptance......Page 141
9.1 Introduction......Page 142
9.2 External and Internal Preference Mapping......Page 143
9.3 Examples of Linear Preference Mapping......Page 150
9.4 Ideal Point Preference Mapping......Page 155
9.5 Selecting Samples for Preference Mapping......Page 160
9.6 Incorporating Additional Consumer Attributes......Page 161
9.7 Combining Preference Mapping with Additional Information about the Samples......Page 163
10.1 Introduction......Page 169
10.2 Segmentation of Rating Data......Page 170
10.3 Relating Segments to Consumer Attributes......Page 177
11.1 Basic Concepts and Principles......Page 179
11.2 Histogram, Frequency and Probability......Page 180
11.3 Some Basic Properties of a Distribution (Mean, Variance and Standard Deviation)......Page 182
11.4 Hypothesis Testing and Confidence Intervals for the Mean µ......Page 183
11.5 Statistical Process Control......Page 186
11.6 Relationships between Two or More Variables......Page 187
11.7 Simple Linear Regression......Page 189
11.8 Binomial Distribution and Tests......Page 191
11.9 Contingency Tables and Homogeneity Testing......Page 192
12.1 Introduction......Page 195
12.2 Important Concepts and Distinctions......Page 196
12.3 Full Factorial Designs......Page 199
12.4 Fractional Factorial Designs: Screening Designs......Page 201
12.5 Randomised Blocks and Incomplete Block Designs......Page 202
12.6 Split-Plot and Nested Designs......Page 204
12.7 Power of Experiments......Page 205
13.1 Introduction......Page 207
13.2 One-Way ANOVA......Page 208
13.3 Single Replicate Two-Way ANOVA......Page 210
13.4 Two-Way ANOVA with Randomised Replications......Page 212
13.5 Multi-Way ANOVA......Page 214
13.6 ANOVA for Fractional Factorial Designs......Page 215
13.7 Fixed and Random Effects in ANOVA: Mixed Models......Page 217
13.8 Nested and Split-Plot Models......Page 219
13.9 Post Hoc Testing......Page 220
14.1 Interpretation of Complex Data Sets by PCA......Page 223
14.2 Data Structures for the PCA......Page 224
14.3 PCA: Description of the Method......Page 225
14.4 Projections and Linear Combinations......Page 227
14.5 The Scores and Loadings Plots......Page 228
14.6 Correlation Loadings Plot......Page 231
14.7 Standardisation......Page 233
14.9 Validation......Page 234
14.10 Outlier Diagnostics......Page 235
14.11 Tucker-1......Page 237
14.12 The Relation between PCA and Factor Analysis (FA)......Page 238
15.1 Introduction......Page 241
15.2 Multivariate Linear Regression......Page 243
15.3 The Relation between ANOVA and Regression Analysis......Page 246
15.4 Linear Regression Used for Estimating Polynomial Models......Page 247
15.5 Combining Continuous and Categorical Variables......Page 248
15.6 Variable Selection for Multiple Linear Regression......Page 249
15.7 Principal Components Regression (PCR)......Page 250
15.8 Partial Least Squares (PLS) Regression......Page 251
15.9 Model Validation: Prediction Performance......Page 252
15.10 Model Diagnostics and Outlier Detection......Page 255
15.11 Discriminant Analysis......Page 258
15.12 Generalised Linear Models, Logistic Regression and Multinomial Regression......Page 259
16.1 Introduction......Page 263
16.2 Hierarchical Clustering......Page 265
16.3 Partitioning Methods......Page 268
16.4 Cluster Analysis for Matrices......Page 273
17.1 Three-Way Analysis of Sensory Data......Page 277
17.3 Path Modelling......Page 283
17.5 Analysing Rank Data......Page 285
17.7 Missing Value Estimation......Page 287
Nomenclature, Symbols and Abbreviations......Page 291
Index......Page 297