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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 #325394

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: Evaluating the performance of a new model for predicting the growth of Clostridium perfringens in cooked, uncured meat and poultry products under isothermal, heating, and dynamically cooling conditions

Author
item Huang, Lihan

Submitted to: Journal of Food Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/3/2016
Publication Date: 7/6/2016
Citation: Huang, L. 2016. Evaluating the performance of a new model for predicting the growth of Clostridium perfringens in cooked, uncured meat and poultry products under isothermal, heating, and dynamically cooling conditions. Journal of Food Science. 81(7):M1754-1765. doi: 10.1111/1750-3841.13356.

Interpretive Summary: Clostridium perfringens Type A is a significant public health threat and may germinate, outgrow, and multiply during cooling of cooked meats. Rapid cooling after cooking is necessary to prevent food poisoning caused by this pathogen. This study evaluates the performance and accuracy of a new predictive model that can be used to evaluate the safety of cooked meat products in the event of cooling deviation. The results of this study show the new model is more accurate and fail-safe than currently available predictive models. This model should be used by the food industry and regulatory agencies to ensure the safety of cooked and partially meat and poultry products.

Technical Abstract: Clostridium perfringens Type A is a significant public health threat and may germinate, outgrow, and multiply during cooling of cooked meats. This study evaluates a new C. perfringens growth model in IPMP Dynamic Prediction using the same criteria and cooling data in Mohr and others (2015), but including isothermal and dynamic heating temperature profiles. The residual errors of predictions (observation-prediction) are analyzed, and the root-mean-square errors (RMSE) calculated. For isothermal and heating profiles, each data point in growth curves is compared. The mean residual errors of predictions (MRE) range from -0.40 to 0.02 Log CFU/g, with a RMSE of about 0.6 Log CFU/g. For cooling, the end-point predictions are conservative in nature, with an MRE of -1.16 Log CFU/g for single rate cooling and -0.66 Log CFU/g for dual rate cooling. The RMSE is between 0.6 and 0.7 Log CFU/g. Compared with other models reported in Mohr and others (2015), this model makes more accurate and fail-safe predictions. For cooling, the percentage for accurate and fail-safe predictions is between 97.6% and 100%. Under criterion 1, the percentage of accurate predictions is 47.5% for single rate and 66.7% for dual rate cooling, while the fail-dangerous predictions are between 0 and 2.4%. This study demonstrates that IPMP Dynamic Prediction can be used by food processors and regulatory agencies as a tool to evaluate the safety of cooked or heat-treated uncured meat and poultry products exposed to cooling deviations or to develop customized cooling schedules. This study also demonstrates the need for more accurate data collection during cooling.