Mathematical and Statistical Methods in Food Science and Technology

Contents: Section I: 1. The use and importance of design of experiments (DOE) in process modelling in food science and technology. 2. The use of correlation, association and regression to analyze processes and products. 3. Case study: Optimization of enzyme-aided extraction of polyphenols from unripe apples by response surface methodology. 4. Case study: Statistical analysis of eurycomanone yield using a full factorial design. Section II: 5. Applications of principal component analysis (PCA) in food science and technology. 6. Multiple factor analysis: Presentation of the method using sensory data. 7. Cluster analysis: Application in food science and technology. 8. Principal component regression (PCR) and partial least squares regression (PLSR). 9. Multiway methods in food science. 10. Multidimensional scaling (MDS). 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. 12. Multivariate image analysis. 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. Section III: 14. Statistical approaches to develop and validate microbiological analytical methods. 15. Statistical approaches to the analysis of microbiological data. 16. Statistical modelling of anthropometric characteristics evaluated on nutritional status. 17. Effects of paediatric obesity: a multivariate analysis of laboratory parameters. 18. Development and application of predictive microbiology models in foods. 19. Statistical approaches for the design of sampling plans for microbiological monitoring of foods. 20. Infrared spectroscopy detection coupled to chemometrics to characterize foodborne pathogens at a subspecies level. Section IV: 21. Multivariate statistical quality control. 22. Application of neural-based algorithms as statistical tools for quality control of manufacturing processes. 23. An integral approach to validation of analytical fingerprinting methods in combination with chemometric modelling for food quality assurance. 24. Translating randomly fluctuating QC records into the probabilities of future mishaps. 25. Application of statistical approaches for analysing the reliability and maintainability of food production lines: a case study of mozzarella cheese. Index. Mathematical and Statistical Approaches in Food Science and Technology offers an accessible guide to applying statistical and mathematical technologies in the food science field whilst also addressing the theoretical foundations. Using clear examples and case-studies by way of practical illustration, the book is more than just a theoretical guide for non-statisticians and may therefore be used by scientists, students and food industry professionals at different levels and with varying degrees of statistical skill.…