|MOHR, TIM - Food Safety Inspection Service (FSIS)|
|THIPAREDDI, HARSHAVARDHAN - University Of Nebraska|
Submitted to: Journal of Food Processing and Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/24/2013
Publication Date: 10/31/2013
Publication URL: http://handle.nal.usda.gov/10113/59991
Citation: Juneja, V.K., Marks, H.L., Mohr, T., Thipareddi, H. 2013. Predictive model for growth of Clostridium perfringens during cooling of cooked beef supplemented with NaCl, sodium nitrite and sodium pyrophosphate. Journal of Food Processing and Technology. 4(10):1-12.
Interpretive Summary: Clostridium perfringens is one of the most commonly reported bacterial agents of foodborne illness in the United States. Illnesses have been traditionally associated with inadequate cooling practices in food service establishments. Thus, there was a need to determine the time and temperature for cooked meat products to remain pathogen-free and provide vital data for performing risk assessment on cooked meat. We developed a dynamic model that can be used to predict the growth of C. perfringens at temperatures relevant to the cooling of cooked products. The predictive model will be of immediate use to the retail food service operations and regulatory agencies to help determine the safety of beef products that have been cooled after thermal treatment.
Technical Abstract: This paper presents a model for predicting relative growth of C. perfringens in ground beef products at different percentages of salt (0%, 1%, 2% and 3%) and nitrite (0 and 200 ppm). Included in the experiments were different levels of sodium pyrophosphate (SPP). The results of the experiments indicates that salt was the primary variable in effecting the amount of growth seen, and that growth in general, was significantly affected by the presence of nitrite. The inclusion of SPP did not improve the model’s fit with observed results. The primary model is based on a common form of Baranyi’s growth curves and the secondary model is based on cardinal temperatures, relating maximum specific growth rates as a function of temperature. For predictions, the model employs 10 parameters: 9 for describing the secondary model for the specific growth rates and the 10th parameter, providing a value for the physiological constant of Baranyi’s growth curves. In comparison to the present USDA pathogen modeling program, the model provides similar predicted growths for uncured product (salt = 0% and nitrite = 0 ppm), and conservative (fail-safe) predictions for cured product (salt = 0%, nitrite = 200ppm). The model can be used by processors to evaluate the risk of C. perfringens spore germination and outgrowth during cooling (stabilization) deviations or in custom cooling schedules in case the processors cannot follow the USDA FSIS Compliance Guidelines (Appendix A) for Cooling of Heat-Treated Meat and Poultry Products (Stabilization).