Mathematical and Statistical Methods in Food Science and Technology -- Contents -- About the editors -- List of contributors -- Acknowledgements -- Section 1 -- 1 The use and importance of design of experiments (DOE) in process modelling in food science and technology -- ABSTRACT -- INTRODUCTION -- OVERVIEW OF EXPERIMENTAL DESIGNS -- Types of design -- Some Considerations -- RESPONSE SURFACE METHODOLOGY: A TOOL FOR ANALYSING AND OPTIMIZING PRODUCTS AND PROCESSES -- PROCESS OPTIMIZATION -- Simultaneous optimization of response variables -- RSM application to foods/process development/optimization -- STATISTICAL PACKAGES -- FINAL REMARKS AND PERSPECTIVES -- REFERENCES -- 2 The use of correlation, association and regression to analyse processes and products -- ABSTRACT -- INTRODUCTION -- PROCESS ANALYSIS -- MULTIVARIATE METHODS -- OUTLIER DETECTION -- MODEL ACCURACY AND VALIDATION -- OVERFITTING AND UNDERFITTING -- ROUTINE ANALYSES AND APPLICATIONS -- SUMMARY -- REFERENCES -- 3 Case study: Optimization of enzyme-aided extraction of polyphenols from unripe apples by response surface methodology -- ABSTRACT -- INTRODUCTION -- EXPERIMENTS -- Materials, chemicals and instruments -- Viscozyme L aided polyphenol extraction -- Experimental design for the RSM procedure -- Determination of the optimum conditions and evaluation of the model -- Determination of TPC and CAC -- Statistical analysis -- RESULTS AND DISCUSSION -- Modelling of the Viscozyme L aided polyphenol extraction reaction -- Effect of Viscozyme L aided hydrolysis variables on TPC -- Effect of Viscozyme L aided hydrolysis variables on CAC -- Estimation and validation of optimum hydrolysis condition -- CONCLUSION -- REFERENCES -- 4 Case study: Statistical analysis of eurycomanone yield using a full factorial design -- ABSTRACT -- INTRODUCTION -- MATERIALS AND METHODS -- Materials -- Methods.

RESULTS AND DISCUSSION -- CONCLUSIONS -- REFERENCES -- Section 2 -- 5 Applications of principal component analysis (PCA) in food science and technology -- ABSTRACT -- INTRODUCTION -- GOAL -- DEFINITION -- EFFECTIVE COMPUTATION -- SOME PROPERTIES -- REPRESENTATION OF THE INDIVIDUALS: A GEOMETRICAL INTERPRETATION -- DIMENSIONALITY REDUCTION -- COVARIANCE OR CORRELATION MATRIX? -- DETERMINING THE NUMBER OF COMPONENTS -- SOME PATTERNS IN R OR IN S AND THEIR INTERPRETATION -- RELATIONSHIP WITH THE ORTHOGONAL REGRESSION -- MULTIPLE REGRESSION ON PRINCIPAL COMPONENTS -- REFERENCES -- 6 Multiple factor analysis: Presentation of the method using sensory data -- ABSTRACT -- INTRODUCTION -- DATA -- Ten Touraine white wines -- Two panels -- Pre-processing -- Analysis of the table -- WEIGHTING TO BALANCE GROUPS OF VARIABLES -- Representing wines and descriptors -- SUPERIMPOSED REPRESENTATION OF THE WINES ANALYSED BY EACH PANEL -- SUPPLEMENTARY CATEGORICAL VARIABLES -- REPRESENTING THE DIMENSIONS OF SEPARATE ANALYSES -- REPRESENTING GROUPS OF VARIABLES -- Measuring the relationship between a variable and a group of variables -- Relationship square -- Relationship square and projection -- Measuring the overall resemblance of shape between two similar clouds: the RV coefficient -- CONSTRUCTING CONFIDENCE ELLIPSES AROUND THE WINES -- CONCLUSIONS AND IMPLICATIONS FOR OTHER APPLICATIONS -- APPENDIX 6.A SOFTWARE AND TECHNICAL POINT -- FactoMineR and SensoMineR -- Transition relations -- REFERENCES -- 7 Cluster analysis: Application in food science and technology -- ABSTRACT -- INTRODUCTION -- Types of cluster analysis -- HIERARCHICAL CLUSTER ANALYSIS -- Calculating the distance between objects and clusters -- Clustering procedure or amalgamation rule -- Construction of the dendrogram -- Examples -- Implementation in R -- K-MEANS CLUSTERING -- Examples.

Implementation in R -- FUZZY CLUSTERING ALGORITHMS -- Example of application: Classification of strawberry cultivars -- Implementation in R -- CONCLUSIONS -- REFERENCE S -- 8 Principal component regression (PCR) and partial least squares regression (PLSR) -- ABSTRACT -- INTRODUCTION -- THEORY -- Classical linear regression -- Optimal estimation -- Principal component regression -- Partial least squares regression -- THE PLSR RESIDUAL ISSUE IN PROCESS MONITORING -- Problem formulation -- Covariance structures -- Combination of SPE and T2 plots -- Conclusion on the PLSR residual issue -- PLSR SCORE LOADING CORRESPONDENCE -- MODEL REDUCTION METHODS -- OPLS -- PLS + ST -- 2PLS -- PCP -- Model reduction in multiresponse cases -- ESTABLISHED PCR AND PLSR PRACTICES -- Pre-treatment of data -- Uncertainty estimation -- Calibration transfer -- Three-way methods -- SOME EMERGING METHODS IN FOOD SCIENCE -- REFERENCES -- 9 Multiway methods in food science -- ABSTRACT -- INTRODUCTION -- METHODS AND CONCEPTS -- Structure of the data -- Second-order advantage -- Tucker3 -- Parallel factor analysis - PARAFAC model -- Parallel factor analysis 2 - PARAFAC2 model -- PARAFAC and PARAFAC2 versus Beer's law -- Multilinear partial least squares regression. N-PLS -- SOURCES FOR MULTIWAY DATA -- Fluorescence -- Chromatographic data -- LF-NMR analysis of food -- Sensory data -- FUTURE PERSPECTIVES -- Direct measurements in the raw samples - Process data -- Automation of three-way methods -- How many samples are really needed? -- REFERENCES -- 10 Multidimensional scaling (MDS) -- ABSTRACT -- INTRODUCTION - WHAT IS MDS? -- APPLICATION OF MDS IN FOOD SCIENCE -- QUALITY OF RESULTS -- MDS PROCEDURE -- Data -- Model selection and goodness of fit -- MDS methods and software -- Interpretation of MDS maps -- EXAMPLE - A SORTING TASK OF WINE GLASS SHAPES -- REFERENCES.

11 Application of multivariate statistical methods during new product development - Case study: Application of principal component analysis and hierarchical cluster analysis on consumer liking data of orange juices -- ABSTRACT -- INTRODUCTION -- CASE STUDY: CONSUMER RESEARCH TO GUIDE THE DEVELOPMENT PROCESS OF A 'MORE NATURAL' TASTING PROCESSED ORANGE JUICE -- Background -- Materials and methods -- Results and discussion -- General discussion and conclusions -- ACKNOWLEDGEMENTS -- REFERENCES -- 12 Multivariate image analysis -- ABSTRACT -- INTRODUCTION -- METHODS -- Digital images basics -- A classification of MIA approaches -- Principal components analysis (PCA) applied in MIA -- Partial least squares (PLS) applied in MIA: Multivariate image regression (MIR) -- Incorporating spatial information in MIA -- Integration of spectral and spatial information in MIA -- APPLICATIONS -- Exploratory and diagnostic analysis -- Process monitoring and grade classification -- Process control -- Predicting product quality and process variables -- CONCLUSIONS -- ACKNOWLEDGEMENTS -- REFERENCES -- 13 Case Study: Quality control of Camellia sinensis and Ilex paraguariensis teas marketed in Brazil based on total phenolics, flavonoids and free-radical scavenging activity using chemometrics -- ABSTRACT -- INTRODUCTION -- MATERIAL AND METHODS -- Reagents -- Tea samples and extraction procedure -- Determination of total phenolic content -- Determination of total flavonoid content -- Free-radical scavenging assay (DPPH) -- Statistical evaluation -- RESULTS AND DISCUSSION -- CONCLUSIONS -- REFERENCES -- Section 3 -- 14 Statistical approaches to develop and validate microbiological analytical methods -- ABSTRACT -- INTRODUCTION -- Variability -- Validation -- SAMPLE PREPARATION -- QUANTITATIVE MICROBIOLOGY, ENUMERATION -- Most probable number enumeration.

QUALITATIVE MICROBIOLOGY METHODS -- Reproducibility -- LIMITS OF DETECTION OF QUALITATIVE MICROBIOLOGY METHODS -- Determining LOD50 values by the Spearman-Kärber method -- Determining LOD50 values by the complementary log-log model -- CONCLUSIONS -- ACKNOWL EDGEMENTS -- REFERENCE S -- 15 Statistical approaches to the analysis of microbiological data -- ABSTRACT -- INTRODUCTION -- MICROBIAL POPULATION DISTRIBUTIONS -- Spatial distributions -- Statistical distributions -- EXPERIMENTAL DESIGN AND PLANNING -- Sampling for surveillance -- Sampling and testing in the laboratory -- Setting up laboratory experiments -- STATISTICAL METHODS FOR ANALYSIS OF DATA -- Descriptive statistics -- Comparing independent data sets -- Analysis of variance (ANOVA) -- USE OF MODELLING TECHNIQUES -- Approaches to mathematical modelling of processes for food safety -- Modelling procedures -- CONCLUSIONS -- ACKNOWLEDGEMENT -- REFERENCES -- 16 Statistical modelling of anthropometric characteristics evaluated on nutritional status -- ABSTRACT -- INTRODUCTION -- Methodology -- Applications in food sciences -- Applications in anthropometry -- EXPERIMENTAL DATA -- PRINCIPAL COMPONENT ANALYSIS (PCA) -- LINEAR DISCRIMINATE ANALYSIS (LDA) -- PARTIAL LEAST SQUARES PLS -- CONCLUSIONS -- REFERENCE S -- 17 Effects of paediatric obesity: a multivariate analysis of laboratory parameters -- ABSTRACT -- INTRODUCTION -- MATERIALS -- Data acquisition -- Collected data -- METHODS -- Statistical analysis -- Univariate analysis -- Investigation of the multivariate structure -- RESULTS -- Univariate descriptive statistics and association analysis -- Investigation of the multivariate structure -- DISCUSSION -- Univariate descriptive statistics and association analysis -- Investigation of the multivariate structure -- CONCLUSION -- ACKNOWLEDGEMENTS -- REFERENCES.

18 Development and application of predictive microbiology models in foods.