Submitted to: Meeting Abstract
Publication Type: Proceedings
Publication Acceptance Date: June 15, 2010
Publication Date: September 27, 2010
Citation: Juneja, V.K., Huang, L. 2010. Hazards associated with Clostridium perfringens in particular reference to predictive models applicable to cooling of cooked meat and poultry products [abstract]. Cold-Chain-Management. p. 93-99. Technical Abstract: The incidence of C. perfringens food-poisoning is quite common and costly. Although somewhat fastidious in growth characteristics using synthetic laboratory media, the microorganism is very prolific when found in food products. Inadequate cooling of foods in retail food operations is a major safety problem. Most C. perfringens illnesses arise primarily due to inadequate rate and extent of cooling of cooked products. We have developed series of dynamic growth models for C. perfringens spore germination and outgrowth at temperatures relevant to cooling of cooked products. The common methodology for developing growth models for dynamic environments is to first determine the growth kinetics under isothermal conditions. Subsequently, “secondary models” are derived to elucidate the effect of temperature and other environmental conditions on growth rates. Thereafter, a judiciously selected set of differential equations are developed to describe the growth of bacteria within a changing environment. These predictive models for C. perfringens are used in evaluating the safety of products after cooling and thus, with the disposition of products subject to cooling deviations. Food industry, including small and very small meat processors, use the models to determine compliance with the stabilization (cooling) performance standards. These models have been converted into an easy-to-use USDA- Pathogen Modeling Program (PMP) that is available on the USDA- Eastern Regional Research Center website. A Predictive Microbiology Information Portal (PMIP) has also been developed to assist small and very small processing companies in the use and interpretation of PMP models. In addition, PMIP can assist in locating and retrieving regulatory information, predictive models, research data and numerous food safety related links associated with the models.