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.

Genetic Algorithms for Optimal Control of Lactic Fermentation: Modelling the Lactobacillus paracasei CBA L74 Growth on Rice Flour Substrate / Ponticelli, G. S.; Gallo, M.; Cacciotti, I.; Giannini, O.; Guarino, S.; Budelli, A.; Nigro, R.. - In: APPLIED SCIENCES. - ISSN 2076-3417. - 13:1(2023). [10.3390/app13010582]

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

Gallo M.
Investigation
;
Guarino S.;Budelli A.
Supervision
;
Nigro R.
Supervision
2023

Abstract

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.
2023
Genetic Algorithms for Optimal Control of Lactic Fermentation: Modelling the Lactobacillus paracasei CBA L74 Growth on Rice Flour Substrate / Ponticelli, G. S.; Gallo, M.; Cacciotti, I.; Giannini, O.; Guarino, S.; Budelli, A.; Nigro, R.. - In: APPLIED SCIENCES. - ISSN 2076-3417. - 13:1(2023). [10.3390/app13010582]
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/954505
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact