Dynamic determination of kinetic parameters, computer simulation, and probabilistic analysis of growth of Clostridium perfringens in cooked beef during cooling

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 Clostridium perfringens in cooked beef. This methodology was based on simultaneous numerical analysis and optimization of both primary and secondary models using multiple dynamic growth curves obtained under different 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 that the kinetic parameters obtained from this study accurately matched the common characteristics of C. perfringens, with the optimum temperature being 45.3°C. The results also showed that the predicted growth curves matched accurately with experimental observations used in validation. The mean of residuals of the predictions is −0.02logCFU/g, with a standard deviation of only 0.23logCFU/g. For relative growths <1logCFU/g, the residuals of predictions are <0.4logCFU/g. Overall, 74% of the residuals of predictions are <0.2logCFU/g, 7.7% are >0.4logCFU/g, while only 1.5% are >0.8logCFU/g. In addition, the dynamic model also accurately predicted four isothermal growth curves arbitrarily chosen from the literature. Finally, the Monte Carlo simulation was used to provide the probability of >1 and 2logCFU/g relative growths 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.