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首页> 外文期刊>Food microbiology >Growth of Clostridium perfringens in cooked chicken during cooling: One-step dynamic inverse analysis, sensitivity analysis, and Markov Chain Monte Carlo simulation
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Growth of Clostridium perfringens in cooked chicken during cooling: One-step dynamic inverse analysis, sensitivity analysis, and Markov Chain Monte Carlo simulation

机译：冷却过程中熟鸡中产气荚膜梭状芽胞杆菌的生长：一步动态逆分析，灵敏度分析和马尔可夫链蒙特卡洛模拟

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摘要

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, mu(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 degrees C, respectively, with a mu(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.
机译：这项研究的目的是确定动力学参数，并应用马尔可夫链蒙特卡洛（MCMC）模拟来预测动态冷却过程中熟鸡肉中孢子的产气荚膜梭状芽胞杆菌的生长。将接种的样品暴露于各种冷却条件下以观察动态生长。一站式反分析使用了4种冷却曲线的组合，其中Baranyi模型为主要模型，而基本参数模型为次要模型。估算了Baranyi模型和基本参数模型的六个动力学参数，包括Q（0），Y-max，mu（opt），T-min，T-opt和T-max。 T-opt和T-max分别为14.8、42.9和50.5摄氏度，mu（opt）为5.25 h（-1），最大细胞密度为8.4 log CFU / g。相关分析表明，Q（0）和Y-max与其他参数之间均弱相关，而其余参数大多彼此之间具有中等至强相关性。尽管可能难以使用单个温度曲线来估计高度相关的参数，但是通过使用多个不同温度曲线的一步分析可以成功地估计它们。估计的参数用作构造贝叶斯分析的后验分布的先验信息。 MCMC模拟用于使用不同的动态温度曲线预测细菌的生长，以验证预测模型的准确性。 MCMC模拟结果表明，与确定性方法相比，贝叶斯分析对冷却过程中细菌生长的预测更为准确。通过贝叶斯分析，预测的均方根误差（RMSE）仅为0.1 log CFU / g，所有残留误差在+/- 0.25 log CFU / g之内。因此，建议使用贝叶斯分析来预测冷却过程中熟肉中的产气荚膜梭菌的生长。

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