Document Detail


Predicting survival of Escherichia coli O157:H7 in dry fermented sausage using artificial neural networks.
MedLine Citation:
PMID:  18236656     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The Canadian Food Inspection Agency required the meat industry to ensure Escherichia coli O157:H7 does not survive (experiences > or = 5 log CFU/g reduction) in dry fermented sausage (salami) during processing after a series of foodborne illness outbreaks resulting from this pathogenic bacterium occurred. The industry is in need of an effective technique like predictive modeling for estimating bacterial viability, because traditional microbiological enumeration is a time-consuming and laborious method. The accuracy and speed of artificial neural networks (ANNs) for this purpose is an attractive alternative (developed from predictive microbiology), especially for on-line processing in industry. Data from a study of interactive effects of different levels of pH, water activity, and the concentrations of allyl isothiocyanate at various times during sausage manufacture in reducing numbers of E. coli O157:H7 were collected. Data were used to develop predictive models using a general regression neural network (GRNN), a form of ANN, and a statistical linear polynomial regression technique. Both models were compared for their predictive error, using various statistical indices. GRNN predictions for training and test data sets had less serious errors when compared with the statistical model predictions. GRNN models were better and slightly better for training and test sets, respectively, than was the statistical model. Also, GRNN accurately predicted the level of allyl isothiocyanate required, ensuring a 5-log reduction, when an appropriate production set was created by interpolation. Because they are simple to generate, fast, and accurate, ANN models may be of value for industrial use in dry fermented sausage manufacture to reduce the hazard associated with E. coli O157:H7 in fresh beef and permit production of consistently safe products from this raw material.
Authors:
A Palanichamy; D S Jayas; R A Holley
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Journal of food protection     Volume:  71     ISSN:  0362-028X     ISO Abbreviation:  J. Food Prot.     Publication Date:  2008 Jan 
Date Detail:
Created Date:  2008-02-01     Completed Date:  2008-03-04     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7703944     Medline TA:  J Food Prot     Country:  United States    
Other Details:
Languages:  eng     Pagination:  6-12     Citation Subset:  IM    
Affiliation:
Biosystems Engineering Department, University of Manitoba, Winnipeg, Manitoba, Canada R3T 5V6.
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MeSH Terms
Descriptor/Qualifier:
Animals
Colony Count, Microbial
Consumer Product Safety
Escherichia coli O157 / growth & development*
Fermentation
Food Contamination / analysis*
Food Handling / methods*
Food Microbiology
Humans
Hydrogen-Ion Concentration
Meat Products / microbiology*
Models, Biological
Neural Networks (Computer)*
Predictive Value of Tests
Reproducibility of Results
Swine
Time Factors
Water / metabolism
Chemical
Reg. No./Substance:
7732-18-5/Water

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


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