Mathematical and Statistical Methods in Food Science and Technology

Dr Daniel Granato, Research Fellow, Department of Food Engineering, State University of Ponta Grossa, Paraná, Brazil. Dr Gastón Ares, Assistant Professor, Department of Food Science and Technology, Facultad de Química, Universidad de la República, Uruguay

About the editors xi

List of contributors xiii

Acknowledgements xvii

Section 1 1

1 The use and importance of design of experiments (DOE) in process modelling in food science and technology 3
Daniel Granato and Verônica Maria de Araújo Calado

2 The use of correlation, association and regression to analyze processes and products 19
Daniel Cozzolino

3 Case study: Optimization of enzyme-aided extraction of polyphenols from unripe apples by response surface methodology 31
Hu-Zhe Zheng and Shin-Kyo Chung

4 Case study: Statistical analysis of eurycomanone yield using a full factorial design 43
Azila Abdul-Aziz, Harisun Yaakob, Ramlan Aziz, Roshanida Abdul Rahman, Sulaiman Ngadiran, Mohd Faizal Muhammad, Noor Hafiza Harun, Wan Mastura Wan Zamri and Ernie Surianiy Rosly

Section 2 55

5 Applications of principal component analysis (PCA) in food science and technology 57
Aurea Grane and Agnieszka Jach

6 Multiple factor analysis: Presentation of the method using sensory data 87
Jerôme Pagès and François Husson

7 Cluster analysis: Application in food science and technology 103
Gastón Ares

8 Principal component regression (PCR) and partial least squares regression (PLSR) 121
Rolf Ergon

9 Multiway methods in food science 143
Åsmund Rinnan, José Manuel Amigo and Thomas Skov

10 Multidimensional scaling (MDS) 175
Eva Derndorfer and Andreas Baierl

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 187
Paula Varela

12 Multivariate image analysis 201
Marco S. Reis

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 219
Débora Cristiane Bassani, Domingos Sávio Nunes and Daniel Granato

Section 3 231

14 Statistical approaches to develop and validate microbiological analytical methods 233
Anthony D. Hitchins

15 Statistical approaches to the analysis of microbiological data 249
Basil Jarvis

16 Statistical modelling of anthropometric characteristics evaluated on nutritional status 285
Zelimir Kurtanjek and Jasenka Gajdos Kljusuric

17 Effects of paediatric obesity: a multivariate analysis of laboratory parameters 303
Tamas Ferenci and Levente Kovacs

18 Development and application of predictive microbiology models in foods 321
Fernando Pérez-Rodríguez

19 Statistical approaches for the design of sampling plans for microbiological monitoring of foods 363
Ursula Andrea Gonzales-Barron, Vasco Augusto Piláo Cadavez and Francis Butler

20 Infrared spectroscopy detection coupled to chemometrics to characterize foodborne pathogens at a subspecies level 385
Clara C. Sousa and João A. Lopes

Section 4 419

21 Multivariate statistical quality control 421
Jeffrey E. Jarrett

22 Application of neural-based algorithms as statistical tools for quality control of manufacturing processes 431
Massimo Pacella and Quirico Semeraro

23 An integral approach to validation of analytical fingerprinting methods in combination with chemometric modelling for food quality assurance 449
Grishja van der Veer, Saskia M. van Ruth and Jos A. Hageman

24 Translating randomly fluctuating QC records into the probabilities of future mishaps 471
Micha Peleg, Mark D. Normand and Maria G. Corradini

25 Application of statistical approaches for analysing the reliability and maintainability of food production lines: a case study of mozzarella cheese 491
Panagiotis H. Tsarouhas

Index 511