{"product_id":"quantitative-sensory-analysis-9780470673461","title":"Quantitative Sensory Analysis","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eSensory evaluation is a scientific discipline used to evoke, measure, analyse and interpret responses to products perceived through the senses of sight, smell, touch, taste and hearing. It is used to reveal insights into the way in which sensory properties drive consumer acceptance and behaviour, and to design products that best deliver what the consumer wants. It is also used at a more fundamental level to provide a wider understanding of the mechanisms involved in sensory perception and consumer behaviour.  \u003cp\u003e\u003ci\u003eQuantitative Sensory Analysis\u003c\/i\u003e is an in-depth and unique treatment of the quantitative basis of sensory testing, enabling scientists in the food, cosmetics and personal care product industries to gain objective insights into consumer preference data  vital for informed new product development.\u003cbr\u003e Written by a globally-recognised learer in the field, this book is suitable for industrial sensory evaluation practitioners, sensory scientists, advanced undergraduate and gra\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface x  \u003c\/p\u003e\u003cp\u003e\u003cb\u003e1 Psychophysics I: Introduction and Thresholds 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction and Terminology 1\u003c\/p\u003e \u003cp\u003e1.2 Absolute Sensitivity 4\u003c\/p\u003e \u003cp\u003e1.3 Methods for Measuring Absolute Thresholds 8\u003c\/p\u003e \u003cp\u003e1.4 Differential Sensitivity 13\u003c\/p\u003e \u003cp\u003e1.5 A Look Ahead: Fechner’s Contribution 17\u003c\/p\u003e \u003cp\u003eAppendix 1.A: Relationship of Proportions, Areas Under the Normal\u003c\/p\u003e \u003cp\u003eDistribution, and Z-Scores 18\u003c\/p\u003e \u003cp\u003eAppendix 1.B: Worked Example: Fitting a Logistic Function to Threshold Data 20\u003c\/p\u003e \u003cp\u003eReferences 22\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Psychophysics II: Scaling and Psychophysical Functions 24\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 24\u003c\/p\u003e \u003cp\u003e2.2 History: Cramer, Bernoulli, Weber, and Fechner 26\u003c\/p\u003e \u003cp\u003e2.3 Partition Scales and Categories 27\u003c\/p\u003e \u003cp\u003e2.4 Magnitude Estimation and the Power Law 28\u003c\/p\u003e \u003cp\u003e2.5 Cross-Modality Matching; Attempts at Validation 32\u003c\/p\u003e \u003cp\u003e2.6 Two-Stage Models and Judgment Processes 35\u003c\/p\u003e \u003cp\u003e2.7 Empirical Versus Theory-Based Functions 39\u003c\/p\u003e \u003cp\u003e2.8 Hybrid Scales and Indirect Scales: A Look Ahead 40\u003c\/p\u003e \u003cp\u003e2.9 Summary and Conclusions 41\u003c\/p\u003e \u003cp\u003eAppendix 2.A: Decibels and Sones 42\u003c\/p\u003e \u003cp\u003eAppendix 2.B: Worked Example: Transformations Applied to Non-Modulus\u003c\/p\u003e \u003cp\u003eMagnitude Estimation Data 44\u003c\/p\u003e \u003cp\u003eReferences 45\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Basics of Signal Detection Theory 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 48\u003c\/p\u003e \u003cp\u003e3.2 The Yes\/No Experiment 49\u003c\/p\u003e \u003cp\u003e3.3 Connecting the Design to Theory 52\u003c\/p\u003e \u003cp\u003e3.4 The ROC Curve 57\u003c\/p\u003e \u003cp\u003e3.5 ROC Curves from Rating Scales; the R-Index 62\u003c\/p\u003e \u003cp\u003e3.6 Conclusions and Implications for Sensory Testing 67\u003c\/p\u003e \u003cp\u003eAppendix 3.A: Table of p and Z 68\u003c\/p\u003e \u003cp\u003eAppendix 3.B: Test for the Significance of Differences Between d′ Values 69\u003c\/p\u003e \u003cp\u003eReferences 69\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Thurstonian Models for Discrimination and Preference 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 The Simple Paired-Choice Model 71\u003c\/p\u003e \u003cp\u003e4.2 Extension into n-AFC: The Byer and Abrams “Paradox” 78\u003c\/p\u003e \u003cp\u003e4.3 A Breakthrough: Power Analysis and Sample Size Determination 80\u003c\/p\u003e \u003cp\u003e4.4 Tau Versus Beta Criteria: The Same–Different Test 84\u003c\/p\u003e \u003cp\u003e4.5 Extension to Preference and Nonforced Preference 89\u003c\/p\u003e \u003cp\u003e4.6 Limitations and Issues in Thurstonian Modeling 90\u003c\/p\u003e \u003cp\u003e4.7 Summary and Conclusions 94\u003c\/p\u003e \u003cp\u003eAppendix 4.A: The Bradley–Terry–Luce Model: An Alternative to Thurstone 95\u003c\/p\u003e \u003cp\u003eAppendix 4.B: Tables for delta Values from Proportion Correct 96\u003c\/p\u003e \u003cp\u003eReferences 97\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Progress in Discrimination Testing 99\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 99\u003c\/p\u003e \u003cp\u003e5.2 Metrics for Degree of Difference 104\u003c\/p\u003e \u003cp\u003e5.3 Replication in Choice Tests 108\u003c\/p\u003e \u003cp\u003e5.4 Current Variations 110\u003c\/p\u003e \u003cp\u003e5.5 Summary and Conclusions 118\u003c\/p\u003e \u003cp\u003eAppendix 5.A: Psychometric Function for the Dual Pair Test, Power\u003c\/p\u003e \u003cp\u003eEquations, and Sample Size 119\u003c\/p\u003e \u003cp\u003eAppendix 5.B: Fun with g 120\u003c\/p\u003e \u003cp\u003eReferences 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Similarity and Equivalence Testing 124\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction: Issues in Type II Error 124\u003c\/p\u003e \u003cp\u003e6.2 Commonsense Approaches to Equivalence 126\u003c\/p\u003e \u003cp\u003e6.3 Allowable Differences and Effect Size 133\u003c\/p\u003e \u003cp\u003e6.4 Further Significance Testing 138\u003c\/p\u003e \u003cp\u003e6.5 Summary and Conclusions 140\u003c\/p\u003e \u003cp\u003eReferences 141\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Progress in Scaling 143\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 143\u003c\/p\u003e \u003cp\u003e7.2 Labeled Magnitude Scales for Intensity 147\u003c\/p\u003e \u003cp\u003e7.3 Adjustable and Relative Scales 153\u003c\/p\u003e \u003cp\u003e7.4 Explicit Anchoring 155\u003c\/p\u003e \u003cp\u003e7.5 Post Hoc Adjustments 158\u003c\/p\u003e \u003cp\u003e7.6 Summary and Conclusions 161\u003c\/p\u003e \u003cp\u003eAppendix 7.A: Examples of Individual Rescaling for Magnitude Estimation 162\u003c\/p\u003e \u003cp\u003eReferences 164\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Progress in Affective Testing: Preference\/Choice and Hedonic Scaling 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 167\u003c\/p\u003e \u003cp\u003e8.2 Preference Testing Options 168\u003c\/p\u003e \u003cp\u003e8.3 Replication 173\u003c\/p\u003e \u003cp\u003e8.4 Alternative Models: Ferris k-visit, Dirichlet multinomial 176\u003c\/p\u003e \u003cp\u003e8.5 Affective Scales 181\u003c\/p\u003e \u003cp\u003e8.6 Ranking and Partial Ranking 185\u003c\/p\u003e \u003cp\u003e8.7 Conclusions 188\u003c\/p\u003e \u003cp\u003eAppendix 8.A: Proof that the McNemar Test is Equivalent to the Binomial\u003c\/p\u003e \u003cp\u003eApproximation Z-Test (AKA Sign Test) 188\u003c\/p\u003e \u003cp\u003eReferences 190\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Using Subjects as Their Own Controls 194\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePart I: Designs using Parametric Statistics 195\u003c\/p\u003e \u003cp\u003e9.1 Introduction to Part I 195\u003c\/p\u003e \u003cp\u003e9.2 Dependent Versus Independent t-Tests 198\u003c\/p\u003e \u003cp\u003e9.3 Within-Subjects ANOVA (“Repeated Measures”) 203\u003c\/p\u003e \u003cp\u003e9.4 Issues 206\u003c\/p\u003e \u003cp\u003ePart II: Nonparametric Statistics 208\u003c\/p\u003e \u003cp\u003e9.5 Introduction to Part II 208\u003c\/p\u003e \u003cp\u003e9.6 Applications of the McNemar Test: A–not-A and\u003c\/p\u003e \u003cp\u003eSame–Different Methods 209\u003c\/p\u003e \u003cp\u003e9.7 Examples of the Stuart–Maxwell 212\u003c\/p\u003e \u003cp\u003e9.8 Further Extensions of the Stuart Test Comparisons 218\u003c\/p\u003e \u003cp\u003e9.9 Summary and Conclusions 220\u003c\/p\u003e \u003cp\u003eAppendix 9.A: R code for the Stuart Test 221\u003c\/p\u003e \u003cp\u003eReferences 222\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Frequency Counts and Check-All-That-Apply (CATA) 224\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Frequency Count Data: Situations — Open Ends, CATA 224\u003c\/p\u003e \u003cp\u003e10.2 Simple Data Handling 227\u003c\/p\u003e \u003cp\u003e10.3 Repeated or Within-Subjects Designs 228\u003c\/p\u003e \u003cp\u003e10.4 Multivariate Analyses 230\u003c\/p\u003e \u003cp\u003e10.5 Difference from Ideal and Penalty Analysis 231\u003c\/p\u003e \u003cp\u003e10.6 Frequency Counts in Advertising Claims 235\u003c\/p\u003e \u003cp\u003e10.7 Conclusions 236\u003c\/p\u003e \u003cp\u003eAppendix 10.A: Proof Showing Equivalence of Binomial Approximation\u003c\/p\u003e \u003cp\u003eZ-Test and c2 Test for Differences of Proportions 237\u003c\/p\u003e \u003cp\u003eReferences 239\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Time–Intensity Modeling 240\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction: Goals and Applications 240\u003c\/p\u003e \u003cp\u003e11.2 Parameters Versus Average Curves 245\u003c\/p\u003e \u003cp\u003e11.3 Other Methods and Analyses 250\u003c\/p\u003e \u003cp\u003e11.4 Summary and Conclusions 254\u003c\/p\u003e \u003cp\u003eReferences 254\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Product Stability and Shelf-Life Measurement 257\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 257\u003c\/p\u003e \u003cp\u003e12.2 Strategies, Measurements, and Choices 258\u003c\/p\u003e \u003cp\u003e12.3 Study Designs 261\u003c\/p\u003e \u003cp\u003e12.4 Hazard Functions and Failure Distributions 261\u003c\/p\u003e \u003cp\u003e12.5 Reaction Rates and Kinetic Modeling 267\u003c\/p\u003e \u003cp\u003e12.6 Summary and Conclusions 271\u003c\/p\u003e \u003cp\u003eReferences 272\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Product Optimization, Just-About-Right (Jar ) Scales, and\u003c\/b\u003e \u003cb\u003eIdeal Profiling 273\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 273\u003c\/p\u003e \u003cp\u003e13.2 Basic Equations, Designed Experiments, and Response Surfaces 276\u003c\/p\u003e \u003cp\u003e13.3 Just-About-Right Scales 279\u003c\/p\u003e \u003cp\u003e13.4 Ideal Profiling 285\u003c\/p\u003e \u003cp\u003e13.5 Summary and Conclusions 292\u003c\/p\u003e \u003cp\u003eReferences 294\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Perceptual Mapping, Multivariate Tools, and Graph Theory 297\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 297\u003c\/p\u003e \u003cp\u003e14.2 Common Multivariate Methods 299\u003c\/p\u003e \u003cp\u003e14.3 Shortcuts for Data Collection: Sorting and Projective Mapping 308\u003c\/p\u003e \u003cp\u003e14.4 Preference Mapping Revisited 309\u003c\/p\u003e \u003cp\u003e14.5 Cautions and Concerns 311\u003c\/p\u003e \u003cp\u003e14.6 Introduction to Graph Theory 314\u003c\/p\u003e \u003cp\u003eReferences 319\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Segmentation 323\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 323\u003c\/p\u003e \u003cp\u003e15.2 Case Studies 326\u003c\/p\u003e \u003cp\u003e15.3 Cluster Analysis 330\u003c\/p\u003e \u003cp\u003e15.4 Other Analyses and Methods 336\u003c\/p\u003e \u003cp\u003e15.5 Women, Fire, and Dangerous Things 337\u003c\/p\u003e \u003cp\u003eReferences 338\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 An Introduction to Bayesian Analysis 340\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Some Binomial-Based Examples 340\u003c\/p\u003e \u003cp\u003e16.2 General Bayesian Models 347\u003c\/p\u003e \u003cp\u003e16.3 Bayesian Inference Using Beta Distributions for Preference Tests 349\u003c\/p\u003e \u003cp\u003e16.4 Proportions of Discriminators 352\u003c\/p\u003e \u003cp\u003e16.5 Modeling Forced-Choice Discrimination Tests 353\u003c\/p\u003e \u003cp\u003e16.6 Replicated Discrimination Tests 355\u003c\/p\u003e \u003cp\u003e16.7 Bayesian Networks 356\u003c\/p\u003e \u003cp\u003e16.8 Conclusions 359\u003c\/p\u003e \u003cp\u003eReferences 360\u003c\/p\u003e \u003cp\u003eAppendix A: Overview of Sensory Evaluation 361\u003c\/p\u003e \u003cp\u003eA.1 Introduction 361\u003c\/p\u003e \u003cp\u003eA.2 Discrimination and Simple Difference Tests 363\u003c\/p\u003e \u003cp\u003eA.3 Descriptive Analysis 367\u003c\/p\u003e \u003cp\u003eA.4 Affective Tests 372\u003c\/p\u003e \u003cp\u003eA.5 Summary and Conclusions 375\u003c\/p\u003e \u003cp\u003eReferences 375\u003c\/p\u003e \u003cp\u003eAppendix B: Overview of Experimental Design 377\u003c\/p\u003e \u003cp\u003eB.1 General Considerations 377\u003c\/p\u003e \u003cp\u003eB.2 Factorial Designs 379\u003c\/p\u003e \u003cp\u003eB.3 Fractional Factorials and Screening 380\u003c\/p\u003e \u003cp\u003eB.4 Central Composite and Box–Behnken Designs 383\u003c\/p\u003e \u003cp\u003eB.5 Mixture Designs 385\u003c\/p\u003e \u003cp\u003eB.6 Summary and Conclusions 385\u003c\/p\u003e \u003cp\u003eReferences 386\u003c\/p\u003e \u003cp\u003eAppendix C: Glossary 387\u003c\/p\u003e \u003cp\u003eIndex 398\u003c\/p\u003e","brand":"John Wiley and Sons Ltd","offers":[{"title":"Default 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