Several mathematical models have been developed to understand the interactions of microorganisms in foods and predict their growth. The resulting model equations for the growth of interacting cells include several parameters that must be determined for the specific conditions to be modeled. In this study, these parameters were determined by using inverse engineering and a multi-objective optimization procedure that allows fitting more than one experimental growth curve simultaneously. A genetic algorithm was applied to obtain the best parameter values of a model that permit the construction of the front of Pareto with 50 individuals or phenotypes. The method was applied to three experimental data sets of simultaneous growth of lactic acid bacteria (LAB) and Listeria monocytogenes (LM). Then, the proposed method was compared with a conventional mono-objective sequential fit. We concluded that the multi-objective fit by the genetic algorithm gives superior results with more parameter identifiability than the conventional sequential approach.

中文翻译：

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遗传算法应用于细菌生长中的同时参数估计

已经开发了几种数学模型来了解食品中微生物的相互作用并预测它们的生长。产生的相互作用细胞生长的模型方程包括几个必须为要建模的特定条件确定的参数。在本研究中，这些参数是通过使用逆向工程和允许同时拟合多个实验生长曲线的多目标优化程序确定的。应用遗传算法来获得模型的最佳参数值，该模型允许构建具有 50 个个体或表型的 Pareto 前沿。该方法应用于乳酸菌（LAB）和单核细胞增生李斯特菌（LM）同时生长的三个实验数据集。然后，所提出的方法与传统的单目标顺序拟合进行了比较。我们得出结论，遗传算法的多目标拟合比传统的顺序方法提供了更好的结果和更多的参数可识别性。