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United States Department of Agriculture

Agricultural Research Service

Research Project: MICROBIAL MODELING AND BIOINFORMATICS FOR FOOD SAFETY AND SECURITY Title: General regression neural network and Monte Carlo simulation model for survival and growth of Salmonella on raw chicken skin as a function of serotype, temperature and time for use in risk assessment

Author
item Oscar, Thomas

Submitted to: Journal of Food Protection
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 14, 2009
Publication Date: October 1, 2009
Citation: Oscar, T.P. 2009. General regression neural network and Monte Carlo simulation model for survival and growth of Salmonella on raw chicken skin as a function of serotype, temperature and time for use in risk assessment. Journal of Food Protection. 72(10):2078-2087.

Interpretive Summary: Temperature abuse of food can result in rapid growth of pathogens to high and dangerous levels. Computer models can be used to forecast pathogen exposure from food that has been temperature abused. Accurate predictions of pathogen growth in food are needed to safeguard public health. Models that under-predict (‘fail-dangerous’) pathogen growth result in consumption of unsafe food, whereas models that over-predict (‘fail-safe’) pathogen growth result in destruction of safe food. Most models are developed using a mixture of pathogen strains. The idea is that this will result in a ‘fail-safe’ model. However, models developed with a cocktail of strains could be overly ‘fail-safe’. In the current study, a computer model for forecasting growth of Salmonella on raw chicken skin subjected to short-term (< 8 h) temperature abuse was developed with individual isolates of Salmonella Typhimurium, Kentucky and Hadar. If the model had been developed with a mixture of Salmonella Typhimurium, Kentucky and Hadar it would have over-predicted growth of Salmonella on raw chicken skin contaminated with Kentucky, which displayed much less growth on raw chicken skin than Typhimurium and Hadar. Thus, by developing a model that predicts Salmonella growth during temperature abuse as a function of prevalence of different types of Salmonella, better predictions of Salmonella growth and consumer exposure were obtained to the benefit of the chicken industry and consumers.

Technical Abstract: A general regression neural network and Monte Carlo simulation model for predicting survival and growth of Salmonella on raw chicken skin as a function of serotype (Typhimurium, Kentucky, Hadar), temperature (5 to 50C) and time (0 to 8 h) was developed. Poultry isolates of Salmonella with natural resistance to antibiotics were used to investigate and model survival and growth from a low initial dose (< 1 log) on raw chicken skin. Computer spreadsheet and spreadsheet add-in programs were used to develop and simulate a GRNN model. Model performance was evaluated by determining the percentage of residuals in an acceptable prediction zone from -1 log (‘fail-safe’) to 0.5 log (‘fail-dangerous’). The GRNN model had an acceptable prediction rate of 92% for dependent data (n = 464) and 89% for independent data (n = 116), which exceeded the performance criterion for model validation of 70% acceptable predictions. Relative contributions of independent variables were 16.8% for serotype, 48.3% for temperature and 34.9% for time. Differences among serotypes were observed with Kentucky exhibiting less growth than Typhimurium and Hadar, which had similar growth. Temperature abuse scenarios were simulated to demonstrate how the model can be integrated with risk assessment and the most common output distribution obtained was Pearson5. This study demonstrated that it is important to include serotype as an independent variable in predictive models for Salmonella. Had a cocktail of serotypes Typhimurium, Kentucky and Hadar been used for model development, the GRNN model would have provided overly ‘fail-safe’ predictions of Salmonella growth on raw chicken skin contaminated with serotype Kentucky. Thus, by developing the GRNN model with individual strains and then modeling growth as a function of serotype prevalence more accurate predictions were obtained.

Last Modified: 11/21/2014
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