Modeling Microbial Responses in Food
1st Edition
Published on 29. December 2003
360 pages
978-1-135-51374-0 (ISBN)
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
More details
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Researchers in food science, microbiology, engineering, biotechnology, water quality: industry quality control managers; HACCP specialists; process engineers; agricultural engineers; students
Product notice
Reflowable
Illustrations
63 s/w Abbildungen, 32 s/w Tabellen, 2 s/w Photographien bzw. Rasterbilder
63 b/w images, 32 tables, 2 halftones and 173 equations
ISBN-13
978-1-135-51374-0 (9781135513740)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Robin C. McKellar | Xuewen Lu
Modeling Microbial Responses in Food
Book
09/2019
1st Edition
CRC Press
€96.50
Shipment within 15-20 days
Robin C. McKellar | Xuewen Lu
Modeling Microbial Responses in Food
Book
12/2003
1st Edition
CRC Press
€272.81
Shipment within 15-20 days
Content
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.
