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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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Produktbeschreibung
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 (Part II), making it to appropriate for people at different levels with respect to their statistical skills. This book succesfully: Makes a clear distinction between studies using a trained sensory panel and studies using consumers. Concentrates on experimental studies with focus on how sensory assessors or consumers perceive and assess various product properties. Focuses on relationships between methods and techniques and on considering all of them as special cases of more general statistical methodologies It is assumed that the reader has a basic knowledge of statistics and the most important data collection methods within sensory and consumer science. This text is aimed at food scientists and food engineers working in research and industry, as well as food science students at master and PhD level. In addition, applied statisticians with special interest in food science will also find relevant information within the book.

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  • Produktdetails
  • Verlag: John Wiley & Sons
  • Seitenzahl: 294
  • Erscheinungstermin: 20. Juni 2011
  • Englisch
  • ISBN-13: 9781119957249
  • Artikelnr.: 37360512
Autorenporträt
Professor Tormod Naes is a Principal Research Scientistbased at Matforsk, a government food research laboratory, inNorway. He received his PhD in statistics from University of Osloin 1984. He is also currently employed as a Professor at theInstitute of Mathematics at the University of Oslo. He serves onthe editorial boards of Journal of Chemometrics, Journal of NearInfrared Spectroscopy and Food Quality and Preference. His main area of research is the development and use ofmultivariate statistical methods in food science. In particular inapplications within the areas of sensory analysis, spectroscopy,process optimisation and bioinformatics. He has published 108refereed papers and co-authored and co-edited 5 books inmultivariate analysis and analysis of variance, including thehighly cited "Multivariate Calibration" co-authored with ProfessorHarald Martens (Wiley 1988). He has received the Tomas Hirschfeldaward in NIR analysis. (1997), EAS award for achievements inchemometrics (1997), Kowalski award in Chemometrics (J. Wiley andSons) (2006) and is an Honorary member of the Chemometric Societyof Norway (2006).
Inhaltsangabe
Preface. Acknowledgements. 1 Introduction. 1.1 The Distinction between 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.