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 Microbiol. 2018 Jun;72:214-219. doi: 10.1016/j.fm.2017.12.007. Epub 2017 Dec 18.
Modelling and simulation of microbial dynamics as a function of processing, transportation and storage conditions is a useful tool to improve microbial food safety and quality. The goal of this research is to improve an existing methodology for building mechanistic predictive models based on the environmental conditions. The effect of environmental conditions on microbial dynamics is often described by combining the separate effects in a multiplicative way (gamma concept). This idea was extended further in this work by including the effects of the lag and stationary growth phases on microbial growth rate as independent gamma factors. A mechanistic description of the stationary phase as a function of pH was included, based on a novel class of models that consider product inhibition. Experimental results on Escherichia coli growth dynamics indicated that also the parameters of the product inhibition equations can be modelled with the gamma approach. This work has extended a modelling methodology, resulting in predictive models that are (i) mechanistically inspired, (ii) easily identifiable with a limited work load and (iii) easily extended to additional environmental conditions.
作为一种改进加工、运输和储存条件下微生物动态的方法，对微生物食品安全和质量的建模和模拟是一种有用的工具。本研究的目的是改进现有的基于环境条件建立机械预测模型的方法。通常通过以乘法方式（伽马概念）组合各个环境条件的单独影响来描述环境条件对微生物动态的影响。通过将滞后和稳定生长阶段对微生物生长速率的影响作为独立的伽马因子包含在内，这个想法在这项工作中得到了进一步扩展。基于考虑产物抑制的新型模型，将稳定期的机理描述为 pH 的函数。对大肠杆菌生长动态的实验结果表明，产物抑制方程的参数也可以用伽马方法进行建模。这项工作扩展了一种建模方法，得到了可以预测模型，该模型具有以下特点：（i）基于机理的启发，（ii）工作量有限，易于识别，（iii）易于扩展到其他环境条件。
