BioTeC, Chemical and Biochemical Process Technology and Control, Department of Chemical Engineering, KU Leuven, Ghent, Belgium; OPTEC, Optimization in Engineering Center-of-Excellence, KU Leuven, Belgium; CPMF(2), Flemish Cluster Predictive Microbiology in Foods, Belgium(1).
BioTeC, Chemical and Biochemical Process Technology and Control, Department of Chemical Engineering, KU Leuven, Ghent, Belgium; OPTEC, Optimization in Engineering Center-of-Excellence, KU Leuven, Belgium; CPMF(2), Flemish Cluster Predictive Microbiology in Foods, Belgium(1).
Food Res Int. 2018 Apr;106:1123-1131. doi: 10.1016/j.foodres.2017.11.026. Epub 2017 Nov 21.
Previous research has indicated that more complex model structures than the commonly used gamma model are needed to obtain an accurate prediction of the effect of multiple environmental conditions on the microbial growth rate. Due to the complexity associated with the development of such model structures, it is recommended that the model structure is compatible with a modular model building method. In this research, a gamma-interaction model was built to describe the combined effect of temperature, pH and water activity on the microbial growth rate of E. coli K12 based on a dataset of 68 bioreactor experiments. This novel interaction model was compared with the standard gamma model. The model structures were tested separately for the combined effects of (i) temperature and pH, (ii) pH and water activity, (iii) temperature and water activity and (iv) temperature, pH and water activity. Based on the results of this research, it was concluded that models for the combined effect of environmental conditions need to allow for sufficient flexibility for the description of combined effects of environmental conditions to obtain accurate model predictions. In the current study, this flexibility was successfully introduced by using the gamma-interaction model. A cross-validation study also demonstrated that the predictions of the interaction model are more robust with respect to the specific data used than the gamma model. As such, the gamma-interaction model provides food producers and food safety authorities with a more accurate and reliable tool for the prediction of the microbial growth rate as a function of multiple environmental conditions.
先前的研究表明，为了准确预测多种环境条件对微生物生长速率的影响，需要使用比常用的伽马模型更复杂的模型结构。由于开发这种模型结构的复杂性，建议模型结构与模块化模型构建方法兼容。在这项研究中，基于 68 个生物反应器实验的数据集，构建了一个伽马交互模型来描述温度、pH 值和水活度对大肠杆菌 K12 微生物生长速率的综合影响。将这个新的交互模型与标准的伽马模型进行了比较。模型结构分别针对（i）温度和 pH 值、（ii）pH 值和水活度、（iii）温度和水活度以及（iv）温度、pH 值和水活度的联合效应进行了测试。根据这项研究的结果，得出结论认为，用于描述环境条件综合效应的模型需要具有足够的灵活性，才能获得准确的模型预测。在当前的研究中，通过使用伽马交互模型成功地引入了这种灵活性。交叉验证研究还表明，与伽马模型相比，交互模型的预测对于特定数据的使用具有更强的稳健性。因此，伽马交互模型为食品生产者和食品安全当局提供了一个更准确、更可靠的工具，可用于预测微生物生长速率作为多种环境条件的函数。
