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As we move further into the 21st Century, sensory and consumer studies continue to develop, playing an important role in food science and industry. These studies are crucial for understanding the relation between food properties on one side and human liking and buying behaviour on the other. This book by a group of established scientists gives a comprehensive, up-to-date overview of the most common statistical methods for handling data from both trained sensory panels and consumer studies of food. It presents the topic in two distinct sections: problem-orientated (Part I) and method orientated…mehr
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- Produktdetails
- Verlag: John Wiley & Sons
- Seitenzahl: 294
- Erscheinungstermin: 20. Juni 2011
- Englisch
- ISBN-13: 9781119957249
- Artikelnr.: 37360512
- Verlag: John Wiley & Sons
- Seitenzahl: 294
- Erscheinungstermin: 20. Juni 2011
- Englisch
- ISBN-13: 9781119957249
- Artikelnr.: 37360512
Trained Sensory Panels and Consumer Panels. 1.2 The Need for Statistics in
Experimental Planning and Analysis. 1.3 Scales and Data Types. 1.4
Organisation of the Book. 2 Important Data Collection Techniques for
Sensory and Consumer Studies. 2.1 Sensory Panel Methodologies. 2.2 Consumer
Tests. PART I PROBLEM DRIVEN. 3 Quality Control of Sensory Profile Data.
3.1 General Introduction. 3.2 Visual Inspection of Raw Data. 3.3 Mixed
Model ANOVA for Assessing the Importance of the Sensory Attributes. 3.4
Overall Assessment of Assessor Differences Using All Variables
Simultaneously. 3.5 Methods for Detecting Differences in Use of the Scale.
3.6 Comparing the Assessors' Ability to Detect Differences between the
Products. 3.7 Relations between Individual Assessor Ratings and the Panel
Average. 3.8 Individual Line Plots for Detailed Inspection of Assessors.
3.9 Miscellaneous Methods.- 4 Correction Methods and Other Remedies for
Improving Sensory Profile Data. 4.1 Introduction. 4.2 Correcting for
Different Use of the Scale. 4.3 Computing Improved Panel Averages. 4.4
Pre-processing of Data for Three-Way Analysis. 5 Detecting and Studying
Sensory Differences and Similarities between Products. 5.1 Introduction.
5.2 Analysing Sensory Profile Data: Univariate Case. 5.3 Analysing Sensory
Profile Data: Multivariate Case. 6 Relating Sensory Data to Other
Measurements. 6.1 Introduction. 6.2 Estimating Relations between Consensus
Profiles and External Data. 6.3 Estimating Relations between Individual
Sensory Profiles and External Data. 7 Discrimination and Similarity
Testing. 7.1 Introduction. 7.2 Analysis of Data from Basic Sensory
Discrimination Tests. 7.3 Examples of Basic Discrimination Testing. 7.4
Power Calculations in Discrimination Tests. 7.5 Thurstonian Modelling: What
Is It Really? 7.6 Similarity versus Difference Testing. 7.7 Replications:
What to Do? 7.8 Designed Experiments, Extended Analysis and Other Test
Protocols. 8 Investigating Important Factors Influencing Food Acceptance
and Choice. 8.1 Introduction. 8.2 Preliminary Analysis of Consumer Data
Sets (Raw Data Overview). 8.3 Experimental Designs for Rating Based
Consumer Studies. 8.4 Analysis of Categorical Effect Variables. 8.5
Incorporating Additional Information about Consumers. 8.6 Modelling of
Factors as Continuous Variables. 8.7 Reliability/Validity Testing for
Rating Based Methods. 8.8 Rank Based Methodology. 8.9 Choice Based Conjoint
Analysis. 8.10 Market Share Simulation. 9 Preference Mapping for
Understanding Relations between Sensory Product Attributes and Consumer
Acceptance. 9.1 Introduction. 9.2 External and Internal Preference Mapping.
9.3 Examples of Linear Preference Mapping. 9.4 Ideal Point Preference
Mapping. 9.5 Selecting Samples for Preference Mapping. 9.6 Incorporating
Additional Consumer Attributes. 9.7 Combining Preference Mapping with
Additional Information about the Samples. 10 Segmentation of Consumer Data.
10.1 Introduction. 10.2 Segmentation of Rating Data. 10.3 Relating Segments
to Consumer Attributes. PART II METHOD ORIENTED. 11 Basic Statistics. 11.1
Basic Concepts and Principles. 11.2 Histogram, Frequency and Probability.
11.3 Some Basic Properties of a Distribution (Mean, Variance and Standard
Deviation). 11.4 Hypothesis Testing and Confidence Intervals for the Mean
1/4. 11.5 Statistical Process Control. 11.6 Relationships between Two or
More Variables. 11.7 Simple Linear Regression. 11.8 Binomial Distribution
and Tests. 11.9 Contingency Tables and Homogeneity Testing. 12 Design of
Experiments for Sensory and Consumer Data. 12.1 Introduction. 12.2
Important Concepts and Distinctions. 12.3 Full Factorial Designs. 12.4
Fractional Factorial Designs: Screening Designs. 12.5 Randomised Blocks and
Incomplete Block Designs. 12.6 Split-Plot and Nested Designs. 12.7 Power of
Experiments. 13 ANOVA for Sensory and Consumer Data. 13.1 Introduction.
13.2 One-Way ANOVA. 13.3 Single Replicate Two-Way ANOVA. 13.4 Two-Way ANOVA
with Randomised Replications. 13.5 Multi-Way ANOVA. 13.6 ANOVA for
Fractional Factorial Designs. 13.7 Fixed and Random Effects in ANOVA: Mixed
Models. 13.8 Nested and Split-Plot Models. 13.9 Post Hoc Testing. 14
Principal Component Analysis. 14.1 Interpretation of Complex Data Sets by
PCA. 14.2 Data Structures for the PCA. 14.3 PCA: Description of the Method.
14.4 Projections and Linear Combinations. 14.5 The Scores and Loadings
Plots. 14.6 Correlation Loadings Plot. 14.7 Standardisation. 14.8
Calculations and Missing Values. 14.9 Validation. 14.10 Outlier
Diagnostics. 14.11 Tucker-1. 14.12 The Relation between PCA and Factor
Analysis (FA). 15 Multiple Regression, Principal Components Regression and
Partial Least Squares Regression. 15.1 Introduction. 15.2 Multivariate
Linear Regression. 15.3 The Relation between ANOVA and Regression Analysis.
15.4 Linear Regression Used for Estimating Polynomial Models. 15.5
Combining Continuous and Categorical Variables. 15.6 Variable Selection for
Multiple Linear Regression. 15.7 Principal Components Regression (PCR).
15.8 Partial Least Squares (PLS) Regression. 15.9 Model Validation:
Prediction Performance. 15.10 Model Diagnostics and Outlier Detection.
15.11 Discriminant Analysis. 15.12 Generalised Linear Models, Logistic
Regression and Multinomial Regression. 16 Cluster Analysis: Unsupervised
Classification. 16.1 Introduction. 16.2 Hierarchical Clustering. 16.3
Partitioning Methods. 16.4 Cluster Analysis for Matrices. 17 Miscellaneous
Methodologies. 17.1 Three-Way Analysis of Sensory Data. 17.2 Relating
Three-Way Data to Two-Way Data. 17.3 Path Modelling. 17.4
MDS-Multidimensional Scaling. 17.5 Analysing Rank Data. 17.6 The L-PLS
Method. 17.7 Missing Value Estimation. Nomenclature, Symbols and
Abbreviations. Index.
Trained Sensory Panels and Consumer Panels. 1.2 The Need for Statistics in
Experimental Planning and Analysis. 1.3 Scales and Data Types. 1.4
Organisation of the Book. 2 Important Data Collection Techniques for
Sensory and Consumer Studies. 2.1 Sensory Panel Methodologies. 2.2 Consumer
Tests. PART I PROBLEM DRIVEN. 3 Quality Control of Sensory Profile Data.
3.1 General Introduction. 3.2 Visual Inspection of Raw Data. 3.3 Mixed
Model ANOVA for Assessing the Importance of the Sensory Attributes. 3.4
Overall Assessment of Assessor Differences Using All Variables
Simultaneously. 3.5 Methods for Detecting Differences in Use of the Scale.
3.6 Comparing the Assessors' Ability to Detect Differences between the
Products. 3.7 Relations between Individual Assessor Ratings and the Panel
Average. 3.8 Individual Line Plots for Detailed Inspection of Assessors.
3.9 Miscellaneous Methods.- 4 Correction Methods and Other Remedies for
Improving Sensory Profile Data. 4.1 Introduction. 4.2 Correcting for
Different Use of the Scale. 4.3 Computing Improved Panel Averages. 4.4
Pre-processing of Data for Three-Way Analysis. 5 Detecting and Studying
Sensory Differences and Similarities between Products. 5.1 Introduction.
5.2 Analysing Sensory Profile Data: Univariate Case. 5.3 Analysing Sensory
Profile Data: Multivariate Case. 6 Relating Sensory Data to Other
Measurements. 6.1 Introduction. 6.2 Estimating Relations between Consensus
Profiles and External Data. 6.3 Estimating Relations between Individual
Sensory Profiles and External Data. 7 Discrimination and Similarity
Testing. 7.1 Introduction. 7.2 Analysis of Data from Basic Sensory
Discrimination Tests. 7.3 Examples of Basic Discrimination Testing. 7.4
Power Calculations in Discrimination Tests. 7.5 Thurstonian Modelling: What
Is It Really? 7.6 Similarity versus Difference Testing. 7.7 Replications:
What to Do? 7.8 Designed Experiments, Extended Analysis and Other Test
Protocols. 8 Investigating Important Factors Influencing Food Acceptance
and Choice. 8.1 Introduction. 8.2 Preliminary Analysis of Consumer Data
Sets (Raw Data Overview). 8.3 Experimental Designs for Rating Based
Consumer Studies. 8.4 Analysis of Categorical Effect Variables. 8.5
Incorporating Additional Information about Consumers. 8.6 Modelling of
Factors as Continuous Variables. 8.7 Reliability/Validity Testing for
Rating Based Methods. 8.8 Rank Based Methodology. 8.9 Choice Based Conjoint
Analysis. 8.10 Market Share Simulation. 9 Preference Mapping for
Understanding Relations between Sensory Product Attributes and Consumer
Acceptance. 9.1 Introduction. 9.2 External and Internal Preference Mapping.
9.3 Examples of Linear Preference Mapping. 9.4 Ideal Point Preference
Mapping. 9.5 Selecting Samples for Preference Mapping. 9.6 Incorporating
Additional Consumer Attributes. 9.7 Combining Preference Mapping with
Additional Information about the Samples. 10 Segmentation of Consumer Data.
10.1 Introduction. 10.2 Segmentation of Rating Data. 10.3 Relating Segments
to Consumer Attributes. PART II METHOD ORIENTED. 11 Basic Statistics. 11.1
Basic Concepts and Principles. 11.2 Histogram, Frequency and Probability.
11.3 Some Basic Properties of a Distribution (Mean, Variance and Standard
Deviation). 11.4 Hypothesis Testing and Confidence Intervals for the Mean
1/4. 11.5 Statistical Process Control. 11.6 Relationships between Two or
More Variables. 11.7 Simple Linear Regression. 11.8 Binomial Distribution
and Tests. 11.9 Contingency Tables and Homogeneity Testing. 12 Design of
Experiments for Sensory and Consumer Data. 12.1 Introduction. 12.2
Important Concepts and Distinctions. 12.3 Full Factorial Designs. 12.4
Fractional Factorial Designs: Screening Designs. 12.5 Randomised Blocks and
Incomplete Block Designs. 12.6 Split-Plot and Nested Designs. 12.7 Power of
Experiments. 13 ANOVA for Sensory and Consumer Data. 13.1 Introduction.
13.2 One-Way ANOVA. 13.3 Single Replicate Two-Way ANOVA. 13.4 Two-Way ANOVA
with Randomised Replications. 13.5 Multi-Way ANOVA. 13.6 ANOVA for
Fractional Factorial Designs. 13.7 Fixed and Random Effects in ANOVA: Mixed
Models. 13.8 Nested and Split-Plot Models. 13.9 Post Hoc Testing. 14
Principal Component Analysis. 14.1 Interpretation of Complex Data Sets by
PCA. 14.2 Data Structures for the PCA. 14.3 PCA: Description of the Method.
14.4 Projections and Linear Combinations. 14.5 The Scores and Loadings
Plots. 14.6 Correlation Loadings Plot. 14.7 Standardisation. 14.8
Calculations and Missing Values. 14.9 Validation. 14.10 Outlier
Diagnostics. 14.11 Tucker-1. 14.12 The Relation between PCA and Factor
Analysis (FA). 15 Multiple Regression, Principal Components Regression and
Partial Least Squares Regression. 15.1 Introduction. 15.2 Multivariate
Linear Regression. 15.3 The Relation between ANOVA and Regression Analysis.
15.4 Linear Regression Used for Estimating Polynomial Models. 15.5
Combining Continuous and Categorical Variables. 15.6 Variable Selection for
Multiple Linear Regression. 15.7 Principal Components Regression (PCR).
15.8 Partial Least Squares (PLS) Regression. 15.9 Model Validation:
Prediction Performance. 15.10 Model Diagnostics and Outlier Detection.
15.11 Discriminant Analysis. 15.12 Generalised Linear Models, Logistic
Regression and Multinomial Regression. 16 Cluster Analysis: Unsupervised
Classification. 16.1 Introduction. 16.2 Hierarchical Clustering. 16.3
Partitioning Methods. 16.4 Cluster Analysis for Matrices. 17 Miscellaneous
Methodologies. 17.1 Three-Way Analysis of Sensory Data. 17.2 Relating
Three-Way Data to Two-Way Data. 17.3 Path Modelling. 17.4
MDS-Multidimensional Scaling. 17.5 Analysing Rank Data. 17.6 The L-PLS
Method. 17.7 Missing Value Estimation. Nomenclature, Symbols and
Abbreviations. Index.