Genetic Algorithms for Optimal Control of Lactic Fermentation: Modelling the Lactobacillus paracasei CBA L74 Growth on Rice Flour Substrate

Modelling and predicting of the kinetics of microbial growth and metabolite production during the fermentation process for functional probiotics foods development play a key role in advancing and making such biotechnological processes suitable for large-scale production. Several mathematical models have been proposed to predict the bacterial growth rate, but they can replicate only the exponential phase and require an appropriate empirical data set to accurately estimate the kinetic parameters. On the other hand, computational methods as genetic algorithms can provide a valuable solution for modelling dynamic systems as the biological ones. In this context, the aim of this study is to propose a genetic algorithm able to model and predict the bacterial growth of the Lactobacillus paracasei CBA L74 strain fermented on rice flour substrate. The experimental results highlighted that the pH control does not influence the bacterial growth as much as it does with lactic acid, which is enhanced from 1987 +/- 90 mg/L without pH control to 5400 +/- 163 mg/L under pH control after 24 h fermentation. The Verhulst model was adopted to predict the biomass growth rate, confirming the ability of exclusively replicating the log phase. Finally, the genetic algorithm allowed the definition of an optimal empirical model able to extend the predictive capability also to the stationary and to the lag phases.

Automated summary

Modelling and predicting the kinetics of microbial growth and metabolite production during fermentation is important for the development of functional probiotics foods on large scale production. Various mathematical models have been proposed to predict bacterial growth rate, but they can only replicate the exponential phase and rely on empirical data for accurate estimation of kinetic parameters. Genetic algorithms offer a promising solution for modelling dynamic biological systems. This study aims to propose a genetic algorithm for modelling and predicting bacterial growth of Lactobacillus paracasei CBA L74 strain fermented on rice flour substrate. Experimental results show that pH control has less influence on bacterial growth compared to lactic acid, and the genetic algorithm allowed the definition of an optimal empirical model that extends predictive capability to stationary and lag phases.