Document Detail


Development of associations and kinetic models for microbiological data to be used in comprehensive food safety prediction software.
MedLine Citation:
PMID:  20722946     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The objective of this study was to use an existing database of food products and their associated processes, link it with a list of the foodborne pathogenic microorganisms associated with those products and finally identify growth and inactivation kinetic parameters associated with those pathogens. The database was to be used as a part of the development of comprehensive software which could predict food safety and quality for any food product. The main issues in building such a predictive system included selection of predictive models, associations of different food types with pathogens (as determined from outbreak histories), and variability in data from different experiments. More than 1000 data sets from published literature were analyzed and grouped according to microorganisms and food types. Final grouping of data consisted of the 8 most prevalent pathogens for 14 different food groups, covering all of the foods (>7000) listed in the USDA Natl. Nutrient Database. Data for each group were analyzed in terms of 1st-order inactivation, 1st-order growth, and sigmoidal growth models, and their kinetic response for growth and inactivation as a function of temperature were reported. Means and 95% confidence intervals were calculated for prediction equations. The primary advantage in obtaining group-specific kinetic data is the ability to extend microbiological growth and death simulation to a large array of product and process possibilities, while still being reasonably accurate. Such simulation capability could provide vital ''what if'' scenarios for industry, Extension, and academia in food safety.
Authors:
Amit Halder; D Glenn Black; P Michael Davidson; Ashim Datta
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Review; Validation Studies    
Journal Detail:
Title:  Journal of food science     Volume:  75     ISSN:  1750-3841     ISO Abbreviation:  J. Food Sci.     Publication Date:  2010 Aug 
Date Detail:
Created Date:  2010-08-20     Completed Date:  2011-01-18     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0014052     Medline TA:  J Food Sci     Country:  United States    
Other Details:
Languages:  eng     Pagination:  R107-20     Citation Subset:  IM    
Affiliation:
Biological and Environmental Engineering, Cornell Univ., Ithaca, NY 14853, USA.
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MeSH Terms
Descriptor/Qualifier:
Computer Simulation
Databases, Factual
Food / classification
Food Microbiology / statistics & numerical data
Food Safety*
Foodborne Diseases / microbiology,  prevention & control
Gram-Negative Bacteria / growth & development
Gram-Positive Bacteria / growth & development
Humans
Kinetics
Models, Biological*
Risk Assessment
Software
Temperature

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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