Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: February 10, 2008
Publication Date: August 4, 2008
Citation: Juneja, V.K., Friedman, M., Thippareddi, H. 2008. Predictive model for growth of clostridium perfringens during cooling of cooked ground chicken. Meeting Abstract. P2-64. page 98. Technical Abstract: Traditional methodologies for development of microbial growth models under dynamic temperature conditions do not take adequate account for the organism’s history. Such models were shown to be inadequate in predicting growth of the organisms under dynamic conditions commonly encountered in the food industry. The objective of the current research was to develop a predictive model for C. perfringens spore germination and outgrowth in cooked chicken products during cooling by incorporating a function to describe the prior history of the microbial cell in the secondary model. Incorporating an assumption that growth kinetics depend in an explicit way on the cells’ history provide accurate estimates of growth or inactivation. Cooked, ground chicken was inoculated with C. perfringens spores and vacuum packaged. For the isothermal experiments, all samples were incubated in a constant temperature water bath stabilized at selected temperatures between 10 - 51C and sampled periodically. The samples were cooled from 54.4 to 27C and subsequently from 27 to 4C for different time periods (cooled rates) for dynamic cooling experiments. For a temperature decline from 54.4C to 27C in 1.5 h, the standard model predicted a log10 relative growth of about 1.15, with a standard error (SE) of about 0.065 log10, while the mean of observed results for two replicates was 0.47 log10. For the same temperature decline in 3 h, the predicted log10 relative growth was about. 3.3 log10 (with a SE of about 0.07), and the mean of the observed log10 relative growths were 2.7 log10. However, for a selected memory model, an estimate of log10 relative growth for the above cooling scenario was 0.76 log10, within 0.3 log10 of the observed mean. For other cooling scenarios this memory model provided predictions within 0.3 log10 of the mean observed log10 growth values. These findings point to an improvement of predictions obtained by memory models over those obtained by the standard model. 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.