Computational shelf-life dating : complex systems approaches to food quality and safety

Shelf-life is defined as the time that a product is acceptable and meets the consumers expectations regarding food quality. It is the result of the conjunction of all services in production, distribution, and consumption. Shelf-life dating is one of the most difficult tasks in food engineering. Market pressure has lead to the implementation of shelf-life by sensory analyses, which may not reflect the full quality spectra. Moreover, traditional methods for shelf-life dating and small-scale distribution chain tests cannot reproduce in a laboratory the real conditions of storage, distribution, and consumption on food quality. Today, food engineers are facing the challenges to monitor, diagnose, and control the quality and safety of food products. The advent of nanotechnology, multivariate sensors, information systems, and complex systems will revolutionize the way we manage, distribute, and consume foods. The informed consumer demands foods, under the legal standards, at low cost, high standards of nutritional, sensory, and health benefits. To accommodate the new paradigms, we herein present a critical review of shelf-life dating approaches with special emphasis in computational systems and future trends on complex systems methodologies applied to the prediction of food quality and safety.Fundo Europeu de Desenvolvimento Regional (FEDER) - Programa POS-ConhecimentoFundação para a Ciência e a Tecnologia (FCT) - SFRH/BPD/26133/2005, SFRH/ BPD/20735/200

Synthetic approaches to artificial photosynthesis: general discussion

Metals in Catalysis, Biomimetics & Inorganic Material

FUZZY DATA AND STATISTICAL MODELING

Data are frequently not precise numbers but more or less non-precise, also called fuzzy. Before analyzing such data the mathematical description of fuzzy data is necessary. This is possible using fuzzy models. Based on this, descriptive data analysis as well as statistical modeling have to be adapted. Basic methods for this are described in this contribution.

Statistical methods for fuzzy data

xii, 256 p. : ill. ; 24 cm

Fuzzy Data and Statistical Modeling

Data are frequently not precise numbers but more or less non-precise, also called fuzzy. Before analyzing such data the mathematical description of fuzzy data is necessary. This is possible using fuzzy models. Based on this, descriptive data analysis as well as statistical modeling have to be adapted. Basic methods for this are described in this contribution

Probability and Bayesian statistics

This book contains selected and refereed contributions to the "Inter­ national Symposium on Probability and Bayesian Statistics" which was orga­ nized to celebrate the 80th birthday of Professor Bruno de Finetti at his birthplace Innsbruck in Austria. Since Professor de Finetti died in 1985 the symposium was dedicated to the memory of Bruno de Finetti and took place at Igls near Innsbruck from 23 to 26 September 1986. Some of the pa­ pers are published especially by the relationship to Bruno de Finetti's scientific work. The evolution of stochastics shows growing importance of probability as coherent assessment of numerical values as degrees of believe in certain events. This is the basis for Bayesian inference in the sense of modern statistics. The contributions in this volume cover a broad spectrum ranging from foundations of probability across psychological aspects of formulating sub­ jective probability statements, abstract measure theoretical considerations, contributions to theoretical statistics and stochastic processes, to real applications in economics, reliability and hydrology. Also the question is raised if it is necessary to develop new techniques to model and analyze fuzzy observations in samples. The articles are arranged in alphabetical order according to the family name of the first author of each paper to avoid a hierarchical ordering of importance of the different topics. Readers interested in special topics can use the index at the end of the book as guide