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ARS Home » Northeast Area » Wyndmoor, Pennsylvania » Eastern Regional Research Center » Residue Chemistry and Predictive Microbiology Research » Research » Publications at this Location » Publication #319271

Research Project: DEVELOPMENT OF PREDICTIVE MICROBIAL MODELS FOR FOOD SAFETY AND THEIR ASSOCIATED USE IN INTERNATIONAL MICROBIAL DATABASES

Location: Residue Chemistry and Predictive Microbiology Research

Title: Direct dynamic kinetic analysis and computer simulation of growth of Clostridium perfringens in cooked turkey during cooling

Author
item Huang, Lihan
item Vinyard, Bryan

Submitted to: Journal of Food Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/4/2015
Publication Date: 3/1/2016
Citation: Huang, L., Vinyard, B.T. 2016. Direct dynamic kinetic analysis and computer simulation of growth of Clostridium perfringens in cooked turkey during cooling. Journal of Food Science. 81(3):M692-701. doi: 10.1111/1750-3841.13202.

Interpretive Summary: As a spore-forming foodborne pathogen, Clostridium perfringens can cause acute abdominal pain, diarrhea, and vomiting. C. perfringens can be found in many cooked meats regulated by the USDA FSIS. Cooling after cooking is one of the most effective methods for controlling the growth of this pathogen in cooked meat. This study develops an innovative and accurate method to predict the growth of this pathogen during cooling of cooked turkey meat, and provides a new tool and a stochastic approach to the food industry and regulatory agencies to assess the microbial safety of cooked turkey meat.

Technical Abstract: This research applied a new one-step methodology to directly construct a tertiary model for describing the growth of C. perfringens in cooked turkey meat under dynamically cooling conditions. The kinetic parameters of the growth models were determined by numerical analysis and optimization using multiple dynamic growth curves. The models and kinetic parameters were validated using independent growth curves obtained under different cooling conditions. The results showed that the residual errors of the predictions followed a Laplace distribution that is symmetric with respect to residual error =0. For residual errors, 90.6% are within +/- 0.5 Log CFU/g, and 73.4% are +/- 0.25 for all growth curves used for validation. For relative growth less than 1.0 Log CFU/g, 88.9% of the relative errors are within +/- 0.5 Log CFU/g, and 63.0% are within +/- 0.25 Log CFU/g. For relative growth of less than 2.0 Log CFU/g, 92.7% of the relative errors are within +/- 0.5 Log CFU/g, and 70.3% are within +/- 0.25 Log CFU/g. The distribution of relative errors clearly suggests that the models and kinetic parameters are reasonably accurate in predicting the growth of C. perfringens. Monte Carlo simulation was used to estimate the probabilities of greater than 1.0 and 2.0 Log CFU/g relative growth of C. perfringens in the final products at the end of cooling. This probabilistic process risk assessment approach provides a new alternative for estimating and managing the risk of a product and can help the food industry and regulatory agencies to assess the safety of cooked meat in the event of cooling deviation.