Dynamic Analysis, Prediction, and Monte Carlo Simulation of Growth of Clostridium perfringens in Cooked Beef

Introduction:  Clostridium perfringens is a spore-forming anaerobic pathogen that can cause acute abdominal pain, stomach cramps, and diarrhea in consumers.  This pathogen is one of the most common causes of foodborne illness in the United States.  Improper cooling and handling of meat products after cooking is a major cause of outbreaks of C. perfringens.  Therefore, rapid cooling is critical to limit the germination of the spores and prevent the growth of this microorganism.

Purpose: This study was conducted to develop and validate a new dynamic method to directly construct a tertiary model for prediction of the growth of C. perfringens in cooked beef. 

Methods:  This method was based on a numerical algorithm to simultaneously solve the primary and secondary equations using multiple dynamic growth curves obtained under different conditions.  A bootstrap method was used to calculate the 95% confidence intervals of kinetic parameters. Monte Carlo simulation was used to validate the models. 

Results: The results demonstrated a close agreement between the predictions and experimental observations.  The mean residual of predictions (RP) was -0.02 ± 0.23 log CFU/g.  The RPs were < 0.4 log CFU/g for relative growths < 1 log CFU/g.  Overall, 74% of the RPs were < 0.2 log CFU/g, 7.7% > 0.4 log CFU/g, while only 1.5% > 0.8 log CFU/g.  In addition, the dynamic model also accurately predicted four isothermal growth curves arbitrarily chosen from the literature.  Finally, the probability of  > 1 and 2 log CFU/g relative growth at the end of cooling was provided by the Monte Carlo simulation. 

Significance: This study provides a new probabilistic approach to estimate of the growth of C. perfringens in cooked meat during cooling.  The results of this study can be used by the food industry and regulatory agencies to assess the safety of cooked beef in the event of cooling deviation.