{"product_id":"mathematical-and-statistical-methods-in-food-science-and-technology-isbn-9781118433683","title":"Mathematical and Statistical Methods in Food Science and Technology","description":"\u003ci\u003eMathematical and Statistical Approaches in Food Science and Technology\u003c\/i\u003e 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.  \u003cp\u003e\u003ci\u003eAbout the editors xi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eList of contributors xiii\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAcknowledgements xvii\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 1 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1 The use and importance of design of experiments (DOE) in process modelling in food science and technology 3\u003cbr\u003e \u003ci\u003eDaniel Granato and Verônica Maria de Araújo Calado\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2 The use of correlation, association and regression to analyze processes and products 19\u003cbr\u003e \u003ci\u003eDaniel Cozzolino\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3 Case study: Optimization of enzyme-aided extraction of polyphenols from unripe apples by response surface methodology 31\u003cbr\u003e \u003ci\u003eHu-Zhe Zheng and Shin-Kyo Chung\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4 Case study: Statistical analysis of eurycomanone yield using a full factorial design 43\u003cbr\u003e \u003ci\u003eAzila 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\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 2 55\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5 Applications of principal component analysis (PCA) in food science and technology 57\u003cbr\u003e \u003ci\u003eAurea Grane and Agnieszka Jach\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6 Multiple factor analysis: Presentation of the method using sensory data 87\u003cbr\u003e \u003ci\u003eJerôme Pagès and François Husson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7 Cluster analysis: Application in food science and technology 103\u003cbr\u003e \u003ci\u003eGastón Ares\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8 Principal component regression (PCR) and partial least squares regression (PLSR) 121\u003cbr\u003e \u003ci\u003eRolf Ergon\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9 Multiway methods in food science 143\u003cbr\u003e \u003ci\u003eÅsmund Rinnan, José Manuel Amigo and Thomas Skov\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10 Multidimensional scaling (MDS) 175\u003cbr\u003e \u003ci\u003eEva Derndorfer and Andreas Baierl\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11 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\u003cbr\u003e \u003ci\u003ePaula Varela\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12 Multivariate image analysis 201\u003cbr\u003e \u003ci\u003eMarco S. Reis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13 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\u003cbr\u003e \u003ci\u003eDébora Cristiane Bassani, Domingos Sávio Nunes and Daniel Granato\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 3 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14 Statistical approaches to develop and validate microbiological analytical methods 233\u003cbr\u003e \u003ci\u003eAnthony D. Hitchins\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15 Statistical approaches to the analysis of microbiological data 249\u003cbr\u003e \u003ci\u003eBasil Jarvis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16 Statistical modelling of anthropometric characteristics evaluated on nutritional status 285\u003cbr\u003e \u003ci\u003eZelimir Kurtanjek and Jasenka Gajdos Kljusuric\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17 Effects of paediatric obesity: a multivariate analysis of laboratory parameters 303\u003cbr\u003e \u003ci\u003eTamas Ferenci and Levente Kovacs\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18 Development and application of predictive microbiology models in foods 321\u003cbr\u003e \u003ci\u003eFernando Pérez-Rodríguez\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19 Statistical approaches for the design of sampling plans for microbiological monitoring of foods 363\u003cbr\u003e \u003ci\u003eUrsula Andrea Gonzales-Barron, Vasco Augusto Piláo Cadavez and Francis Butler\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20 Infrared spectroscopy detection coupled to chemometrics to characterize foodborne pathogens at a subspecies level 385\u003cbr\u003e \u003ci\u003eClara C. Sousa and João A. Lopes\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 4 419\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21 Multivariate statistical quality control 421\u003cbr\u003e \u003ci\u003eJeffrey E. Jarrett\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22 Application of neural-based algorithms as statistical tools for quality control of manufacturing processes 431\u003cbr\u003e \u003ci\u003eMassimo Pacella and Quirico Semeraro\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23 An integral approach to validation of analytical fingerprinting methods in combination with chemometric modelling for food quality assurance 449\u003cbr\u003e \u003ci\u003eGrishja van der Veer, Saskia M. van Ruth and Jos A. Hageman\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24 Translating randomly fluctuating QC records into the probabilities of future mishaps 471\u003cbr\u003e \u003ci\u003eMicha Peleg, Mark D. Normand and Maria G. Corradini\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25 Application of statistical approaches for analysing the reliability and maintainability of food production lines: a case study of mozzarella cheese 491\u003cbr\u003e \u003ci\u003ePanagiotis H. Tsarouhas\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eIndex 511\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eDr Daniel Granato\u003c\/b\u003e, Research Fellow, Department of Food Engineering, State University of Ponta Grossa, Paraná, Brazil.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eDr Gastón Ares\u003c\/b\u003e, Assistant Professor, Department of Food Science and Technology, Facultad de Química, Universidad de la República, Uruguay\u003c\/p\u003e Statistical and mathematical methodologies are usually perceived by people working in food science as complex and difficult subjects, and most food scientists and professionals receive limited instruction in them. This hinders the implementation of statistical and mathematical methodologies in food science and limits the usability and application of these techniques for research that contains a large quantity of complex data. These statistical techniques are necessary for the development and evaluation of food products and processes. They are also important for studying mechanisms underlying different phenomena that may affect product quality or the unit operations in food development. \u003cp\u003e\u003ci\u003eMathematical and Statistical Approaches in Food Science and Technology\u003c\/i\u003e 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. The book's 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 of statistical skill, as well as researchers, undergraduate and graduate students.\u003c\/p\u003e","brand":"Wiley-Blackwell","offers":[{"title":"Default Title","offer_id":47989585903845,"sku":"NP9781118433683","price":238.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118433683.jpg?v=1761784703","url":"https:\/\/k12savings.com\/es\/products\/mathematical-and-statistical-methods-in-food-science-and-technology-isbn-9781118433683","provider":"K12savings","version":"1.0","type":"link"}