1st Edition

Modeling Microbial Responses in Food

Edited By Robin C. McKellar, Xuewen Lu Copyright 2003
    360 Pages
    by CRC Press

    358 Pages
    by CRC Press

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    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.

    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.


    Robin C. McKellar, Xuewen Lu