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ARS Home » Northeast Area » Wyndmoor, Pennsylvania » Eastern Regional Research Center » Residue Chemistry and Predictive Microbiology Research » Research » Publications at this Location » Publication #313033

Research Project: DEVELOPMENT OF PREDICTIVE MICROBIAL MODELS FOR FOOD SAFETY AND THEIR ASSOCIATED USE IN INTERNATIONAL MICROBIAL DATABASES

Location: Residue Chemistry and Predictive Microbiology Research

Title: Assessing the performance of Clostridium perfringens cooling models for cooked, uncured meat and poultry products

Author
item MOHR, T. - Food Safety Inspection Service (FSIS)
item Juneja, Vijay
item THIPPAREDDI, H. - University Of Nebraska
item SCHAFFNER, D. - Rutgers University
item BRONSTEIN, P. - Food Safety Inspection Service (FSIS)
item SILVERMAN, M. - Food Safety Inspection Service (FSIS)
item COOK, JR., L. - Safetytaste Solutions

Submitted to: Journal of Food Protection
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/20/2015
Publication Date: 8/1/2015
Citation: Mohr, T.B., Juneja, V.K., Thippareddi, H.H., Schaffner, D.W., Bronstein, P.A., Silverman, M., Cook, Jr., L.V. 2015. Assessing the performance of Clostridium perfringens cooling models for cooked, uncured meat and poultry products. Journal of Food Protection. 8:1512-1526.

Interpretive Summary: Clostridium perfringens is the second leading cause of bacterial foodborne illness in the United States. Based on the CDC statistics, the leading food vehicles for C. Perfringens type A foodborne illness in the United States are meats, notably beef and poultry. Therefore, the United States Department of Agriculture requires that the relative growth of C. perfringens should not exceed 1.0 log during cooling of certain meat and poultry products. Processors use easy-to-use computer models to evaluate the safety of cooked product after cooling and thus, the disposition of products subject to cooling deviations. We evaluated the relative performance of the existing cooling models so that processors can determine whether the cooling model is reliable (i.e., validated) for evaluating cooling deviations or developing customized cooling schedules for cooked/heat-treated, uncured meat or poultry products.

Technical Abstract: Heat-resistant spores of C. perfringens may germinate and multiply in cooked meat and poultry products if the rate and extent of cooling does not occur in a timely manner. Therefore, six cooling models (PMP 7.0 broth model; PMIP Uncured Beef, Chicken, and Pork Models; Smith-Schaffner (version 3); and UK, IFR ComBase Perfringens Predictor) were evaluated for relative performance in predicting growth of C. perfringens under dynamic temperature conditions encountered during cooling of cooked, uncured meat and poultry products. This was done by extensively comparing the predicted growth responses from the models with those observed in food. Data from 188 time/temperature cooling profiles (176 for single-rate exponential cooling and 12 for dual-rate exponential cooling) were collected from 17 independent sources (16 peer-reviewed publications and 1 report) for model evaluation. Data were obtained from a variety of cooked products including meat and poultry slurries, ground meat and poultry products with and without added ingredients (e.g., potato starch, sodium triphosphate, and potassium tetrapyrophosphate), and processed products such as ham and roast beef. Performance of the models was evaluated using three sets of criteria where accuracy was defined within a 1-2 log range. The first criterion (an accurate prediction is when the residual (observed value minus predicted value) is -1 log to +0.5 log; a fail-safe prediction is when the residual is less than -1.0 log; and a fail-dangerous prediction is when the residual is greater than +0.5 log) used to evaluate model performance is based upon the acceptable prediction zone (APZ) method. The boundaries for the second criterion (an accurate prediction is when the residual is -1 log to +1.0 log; a fail-safe prediction is when the residual is less than -1.0 log; and a fail-dangerous prediction is when the residual is greater than +1.0 log) are based on the level of microbial growth that would not be considered significant by an expert food microbiologist. The boundaries for the third criterion (an accurate prediction is when the residual is -0.5 log to +0.5 log; a fail-safe prediction is when the residual is less than -0.5 log; and a fail-dangerous prediction is when the residual is greater than +0.5 log) are as indicated because 0.5-log is generally accepted as the resolution limit in food microbiology. The percentages of accurate, fail-safe, or fail-dangerous predictions for each cooling model varied based on which criterion was used to evaluate the data set. Nevertheless, the combined percentage of accurate and fail-safe predictions based on the three performance criteria was 34.66 to 42.61% for the PMP 7.0 beef broth model, and 100% for the PMIP cooling models for uncured beef, uncured pork and uncured chicken; the corresponding values ranged from 80.11 to 93.18% for the Smith-Schaffner cooling model and from 74.43 to 85.23% for the ComBase Perfringens Predictor during single-rate exponential chilling. Performance of the available cooling models reported in this study will assist food processors and regulatory agencies in selecting reliable models for evaluating the safety of cooked meat and poultry products involved in cooling deviations.