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Advanced Quantitative Microbiology for Foods and Biosystems (Models for Predicting Growth and Inactivation)

List Price: $89.99
SKU:
9780367390952
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  • Product Details

    Author:
    Micha Peleg
    Format:
    Paperback
    Pages:
    456
    Publisher:
    CRC Press (September 5, 2019)
    Language:
    English
    Audience:
    Professional and scholarly
    ISBN-13:
    9780367390952
    Weight:
    16oz
    Dimensions:
    6" x 9"
    File:
    TAYLORFRANCIS-TayFran_260403050835162-20260403.xml
    Folder:
    TAYLORFRANCIS
    List Price:
    $89.99
    As low as:
    $85.49
    Publisher Identifier:
    P-CRC
    Discount Code:
    H
    Pub Discount:
    30
    Country of Origin:
    United States
    Imprint:
    CRC Press
    Case Pack:
    1
  • Overview

    Presenting a novel view of the quantitative modeling of microbial growth and inactivation patterns in food, water, and biosystems, Advanced Quantitative Microbiology for Foods and Biosystems: Models for Predicting Growth and Inactivation describes new models for estimating microbial growth and survival. The author covers traditional and alternative models, thermal and non-thermal preservation, water disinfection, microbial dose response curves, interpretation of irregular count records, and how to estimate the frequencies of future outbursts. He focuses primarily on the mathematical forms of the proposed alternative models and on the rationale for their introduction as substitutes to those currently in use.

    The book provides examples of how some of the methods can be implemented to follow or predict microbial growth and inactivation patterns, in real time, with free programs posted on the web, written in MS ExcelÒ, and examples of how microbial survival parameters can be derived directly from non-isothermal inactivation data and then used to predict the efficacy of other non-isothermal heat treatments. Featuring numerous illustrations, equations, tables, and figures, the book elucidates a new approach that resolves several outstanding issues in microbial modeling and eliminates inconsistencies often found in current methods.