Growth of Clostridium perfringens in cooked chicken during cooling: One-step dynamic inverse analysis, sensitivity analysis, and Markov Chain Monte Carlo simulation. (February 2020)

• Record Type: Journal Article
• Authors: Huang, Lihan; Li, Changcheng
• Is Part Of: Food microbiology. Volume 85(2020)
• Publication Date: 2020-02
• Subjects: C. perfringens -- Dynamic analysis -- One-step modeling -- Bayesian analysis; Food Microbiology -- Periodicals; Aliments -- Microbiologie -- Périodiques; Food -- Microbiology; Periodicals; Food -- Microbiology -- Periodicals; Food contamination -- Periodicals; 664.001579
• DOI: 10.1016/j.fm.2019.103285
• Languages: English
• ISSNs: 0740-0020

Abstract

Abstract: The objective of this study was to determine the kinetic parameters and apply Markov Chain Monte Carlo (MCMC) simulation to predict the growth of Clostridium perfringens from spores in cooked ground chicken meat during dynamic cooling. Inoculated samples were exposed to various cooling conditions to observe dynamic growth. A combination of 4 cooling profiles was used in one-step inverse analysis with the Baranyi model as the primary model and the cardinal parameters model as the secondary model. Six kinetic parameters of the Baranyi model and the cardinal parameters model, including Q 0, Y max, μ opt, T min, T opt, and T max, were estimated. The estimated T min, T opt, and T max were 14.8, 42.9, and 50.5 °C, respectively, with a μ opt of 5.25 h −1 and maximum cell density of 8.4 log CFU/g. Correlation analysis showed that both Q 0 and Y max are weakly correlated to other parameters, while the remaining parameters are mostly mildly to strongly correlated with each other. Although it may be difficult to estimate highly correlated parameters using a single temperature profile, one-step analysis with multiple different temperature profiles helped estimate them successfully. The estimated parameters were used as the prior information to construct the posterior distribution for Bayesian analysis. MCMC simulation was used to predict the bacterial growth using different dynamic temperature profiles for validation of the accuracy of the predictive models. The MCMC simulation results showed that the Bayesian analysis produced more accurate predictions of bacterial growth during cooling than the deterministic method. With Bayesian analysis, the root-mean-square-error (RMSE) of prediction was only 0.1 log CFU/g with all residual errors within ±0.25 log CFU/g. Therefore, Bayesian analysis is recommended for predicting the growth of C. perfringens in cooked meat during cooling. Highlights: One-step dynamic method is used to study the growth of C. perfringens. Markov Chain Monte Carlo simulation was used for Bayesian stochastic analysis. Bayesian analysis was compared with deterministic modeling. Bayesian analysis was proven to produce more accurate predictions.