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Predictive model for growth of Clostridium perfringens during cooling of cooked uncured beef

机译：未煮熟牛肉冷却过程中产气荚膜梭菌生长的预测模型

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This paper considers growth models including one based on Baranyi's equations for growth and the other based on the logistic function. Using a common approach for constructing dynamic models for predicting Clostridium perfringens growth in ready-to-eat uncured beef during cooling, there was no appreciable difference between the models' predictions when the population of cells was within the lag or exponential phases of growth. The developed models can be used for designing safe cooling processes; however, the discrepancies between predicted and observed growths obtained in this study, together with discrepancies reported in other papers using the same, or similar methodology as used in this paper, point to a possible inadequacy of the derived models. In particular, the appropriateness of the methodology depends on the appropriateness of using estimated growth kinetics obtained from experiments conducted in isothermal environments for determining coefficients of differential equations that are used for predicting growth in constantly changing (dynamic) environments. The coefficients are interpreted as instantaneous specific rates of change that are independent of prior history. However, there is no known scientific reason that would imply the truth of this assumption. Incorporating a different, less restrictive assumption, allowing for a dependency on the prior history of cells for these kinetic parameters, might lead to models that provide more accurate estimates of growth. For example, a cooling scenario of 54.4-27 ℃ in 1.5 h, the average predicted and observed log_(10) relative growths were 1.1 log_(10) and 0.66 log_(10), respectively, a difference of 0.44 log_(10), whereas, when assuming a particular dependency of history, the predicted value was 0.8 log_(10). More research is needed to characterize the behavior of growth kinetic parameters relative to prior history in dynamic environments.
机译：本文考虑的增长模型包括一个基于Baranyi增长方程的模型，另一个基于Logistic函数的模型。使用通用的方法构建动态模型来预测冷却后即食未腌制牛肉中产气荚膜梭状芽孢杆菌的生长，当细胞数量处于生长的滞后或指数阶段时，模型的预测之间没有明显的差异。所开发的模型可用于设计安全的冷却过程；但是，在这项研究中获得的预测增长与观察到的增长之间的差异，以及使用与本文相同或相似的方法在其他论文中报告的差异，都表明派生模型可能存在不足。特别地，该方法的适当性取决于使用从等温环境中进行的实验中获得的估计生长动力学来确定用于预测在不断变化的（动态）环境中的生长的微分方程系数的适当性。系数被解释为瞬时特定的变化率，与先前的历史记录无关。但是，没有已知的科学理由暗示此假设的真实性。纳入一个不同的，限制性较小的假设，允许依赖于这些动力学参数的细胞先前的历史，可能会导致模型提供更准确的生长估计。例如，在1.5 h的54.4-27℃降温情景下，预测和观察到的平均log_（10）相对增长分别为1.1 log_（10）和0.66 log_（10），相差0.44 log_（10），反之，当假设特定的历史依赖关系时，预测值为0.8 log_（10）。相对于动态环境中的先前历史，需要更多的研究来表征生长动力学参数的行为。

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