Mathematical and statistical methods in food science and technology / edited by Daniel Granato, Food Science and Technology Graduate Programme, State University of Ponta Grossa, Ponta Grossa, Brazil; Gaston Ares, Department of Food Science and Technology, Universidad de le Republica, Montevideo, Uruguay.Material type: TextLanguage: English Publisher: Chichester, West Sussex, UK ; Hoboken, NJ : Wiley Blackwell, 2014Description: xv, 513 pages : illustrations ; 26 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9781118433683 (cloth)Subject(s): Food | Food contamination | Food supply -- MathematicsAdditional physical formats: Online version:: Mathematical and statistical methods in food science and technologyDDC classification: 664/.07 LOC classification: TX541 | M42 2014
|Item type||Current library||Collection||Call number||Materials specified||Status||Notes||Date due||Barcode|
|Books||Ladislao N. Diwa Memorial Library Reserve Section||Non-fiction||RUS TX541 M42 2014 (Browse shelf (Opens below))||Room use only||77406||00077606|
Includes bibliographical references and index.
The use and importance of design of experiments (DOE) in process modelling in food science and technology -- The use of correlation, association and regression to analyze processes and products -- Case study: Optimization of enzyme-aided extraction of polyphenols from unripe apples by response surface methodology -- Case study: Statistical analysis of eurycomananone yield using a full factorial design -- Applications of principal component analysis (PCA) in food science and technology -- Multiple factor analysis: presentation of the method using sensory data -- Cluster analysis: application in food science and technology -- Principal component regression (PCR) and partial least squares regression (PLSR) -- Multiway methods in food science -- Multidimensional scaling (MDS) -- Application of multivariate statistical methods during new product development -- Multivariate image analysis -- Case study: Quality control of Camellia sinesis and Ilex paraguariensis teas marketed in Brazil based on total phenolics, flavanoids and free-radical scavenging activity using chemometrics -- Statistical appproaches to develop and validate microbiological analytical methods -- Statistical approaches to the analysis of microbiological data -- Statistical modelling of anthropometric characteristics evaluated on nutritional status -- Effects of paediatric obesity : a multivariate analysis of laboratory parameters -- Development and application of predictive microbiology models in foods -- Statistical approaches for the design of sampling plans for microbiological monitoring of foods - Infrared spectroscopy detection coupled to chemometrics to characterize foodborne pathogens at a subspecies level -- Multivariate statistical quality control -- Application of neural-based algorithms as statistical tools for quality control of manufacturing processes -- An integrated approach to validation of analytical fingerprinting methods in combination with chemometric modelling for food quality assurance -- Translating randomly fluctuating QC records into the probabilities of future mishaps -- Application of statistical approaches for analysing the reliability and maintainability of food production lines: a case study of mozzarella cheese.
This book offers accessible and practical information, suitable for readers across a range of knowledge levels and food-related disciplines, for applying statistical and mathematical technologies in food science. Its focus is on the application of complex methodologies which have been recently introduced in the field (managing physicochemical, chemical, rheological, nutritional, and sensory data) and have proven to be extremely useful in characterizing new products and processes in the food industries. Theoretical explanations, practical examples and case studies ensure that this is an easy-to-follow and comprehensive text, not just a theoretical guide for non-statisticians. It will therefore be of value to all food science professionals with varying degrees o9f statistical skill, as well as researchers, undergraduate and graduate students. – Back cover.