Submitted to: International Journal of Food Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/22/2014
Publication Date: 12/22/2014
Publication URL: http://handle.nal.usda.gov/10113/61114
Citation: Huang, L. 2014. Dynamic determination of kinetic parameters and computer simulation of growth of Clostridium perfringens in cooked beef. International Journal of Food Microbiology. DOI: 10.1016/j.ifoodmicro.2014.11.025.
Interpretive Summary: Clostridium perfringens is a spore-forming foodborne pathogen that can cause acute abdominal pain, diarrhea, and vomiting. This pathogen can be found in cooked meats regulated by the USDA FSIS. Cooling after cooking is critical to prevent the growth of this pathogen. This study was conducted to develop an innovative method and computer programs to predict the growth of this pathogen during cooling of cooked beef. The results showed the new method is very accurate. This study provides a new tool and a stochastic approach to the food industry and regulatory agencies to assess the microbial safety of cooked beef.
Technical Abstract: The objective of this research was to develop a new one-step methodology that uses a dynamic approach to directly construct a tertiary model for prediction of the growth of C. perfringens in cooked beef. This methodology was based on numerical analysis and optimization of both primary and secondary models using multiple dynamic growth curves obtained under dynamic conditions. Once the models were constructed, the bootstrap method was used to calculate the 95% confidence intervals of kinetic parameters, and a Monte Carlo simulation method was developed to validate the models using the growth curves not previously used in model development. The results showed the kinetic parameters obtained from this study accurately matched the common characteristics of C. perfringens, with the optimum temperature being 45.3C. The results also showed the predicted growth curves matched accurately with experimental observations used in validation. The mean of residuals of the predictions was -0.02 log CFU/g, with a standard deviation of only 0.23 log CFU/g. For relative growths less than 1 log CFU/g, the residuals of prediction are less than 0.4 log CFU/g. Overall, 74% of the residuals of predictions are less than 0.2 log CFU/g, 7.7% greater than 0.4 log CFU/g, while only 1.5% are greater than 0.8 log CFU/g. In addition, the dynamic model also accurately predicted isothermal growth curves arbitrarily chosen from the literature. Finally, the Monte Carlo simulation was used to provide the probability of greater than 1 and 2 log CFU/g relative growth at the end of cooling. The results of this study will provide a new and accurate tool to the food industry and regulatory agencies to assess the safety of cooked beef in the event of cooling deviation.