Modeling microbial responses in food
Author(s)
Bibliographic Information
Modeling microbial responses in food
(CRC series in contemporary food science / Fergus M. Clydesdale, Series editor)
CRC Press, 2019, c2004
- : pbk
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Note
Includes bibliographical references and index
"First issued in paperback 2019"--T.p. verso
Description and Table of Contents
Description
The first state-of-the-art review of this dynamic field in a decade, Modeling Microbial Responses in Foods provides the latest information on techniques in mathematical modeling of microbial growth and survival. The comprehensive coverage includes basic approaches such as improvements in the development of primary and secondary models, statistical fitting strategies, and novel data collection methods.
An international team of experts explore important developing areas, including specific applications, challenges in applying models to foods, variability and uncertainty, and new modeling strategies. The authors present detailed descriptions of non-linear regression fitting, methods, approaches relevant to 'real world' situations, and extensive applications of predictive models. They conclude by highlighting the strengths and weaknesses in the field and areas for future work, and attempt to resolve some of the outstanding conflicts.
The book includes strategies for combining databases, improving researcher networks, and standardization of applications packages. Providing the uninitiated with enough information to begin developing their own models, Modeling Microbial Responses in Foods covers all aspects of growth and survival modeling from the primary stage of gathering data to the implementation of final models in appropriate delivery systems.
Table of Contents
Experimental Design & Data Collection. Primary Models. Secondary Models. Model Fitting and Uncertainty. Challenge of Food and the Environment. Software Programs to Increase the Utility of Predictive Microbiology Information. Modeling Microbial Dynamics Under Time-Varying Conditions. Predictive Microbiology in Quantitative Risk Assessment. Modeling the History Effect on Microbial Growth and Survival. Models: What Comes After the Next Generation? Predictive Mycology. An Essay on the Unrealized Potential of Predictive Microbiology.
by "Nielsen BookData"