Submitted to: Innovative Food Science and Emerging Technologies
Publication Type: Peer reviewed journal
Publication Acceptance Date: 10/25/2009
Publication Date: 2/10/2010
Citation: Juneja, V.K., Marks, H.L., Thippareddi, H. 2010. Predictive model for growth of Clostridium perfringens during cooling of cooked ground pork. Innovative Food Science & Emerging Technologies. 11:146-154. Interpretive Summary: One of the most common types of food poisoning in the United States is caused by the bacterium, Clostridium perfringens. Illnesses have been traditionally associated with inadequate cooling practices in retail food service operations. Thus, there was a need to determine the time and temperature for cooked uncured meat products to remain pathogen-free and provide vital data for performing risk assessment on cooked meat. We developed a 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 aid with the disposition of products subject to cooling deviations and therefore, ensure the safety of the cooked foods.
Technical Abstract: A predictive dynamic model for C. perfringens spore germination and outgrowth in cooked pork products during cooling is presented. Cooked, ground pork was inoculated with C. perfringens spores and vacuum packaged. For the isothermal experiments, all samples were incubated in a water bath stabilized at selected temperatures between 10 – 51C and sampled periodically. For dynamic experiments, the samples were cooled from 54.4 to 27C and subsequently from 27 to 4 C for different time periods, designated as x and y hours, respectively. The growth models used incorporate a constant, referred to as the physiological state constant, q0. The value of this constant captures the cells’ history before the cooling begins. To estimate specific growth rates, data from isothermal experiments were used, from which a secondary model was developed. An optimal value of q0 (= 0.01375) was derived minimizing the mean square error of predictions. However, using this estimate, the model had a tendency to over-predict relative growth,when small amounts of relative growth were observed, and under-predict relative growth, when large relative growth was observed. To provide more fail-safe estimates, rather than using the derived value of q0, a value of 0.04 is recommended. The predictive model with this value of q0 would provide more fail-safe estimates of relative growth and could aid producers and regulatory agencies with determining disposition of products that were subjected to cooling deviations.