Genetic Algorithms for Optimal Control of Lactic Fermentation: Modelling the <i>Lactobacillus paracasei</i> 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...
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| author | Gennaro Salvatore Ponticelli Marianna Gallo Ilaria Cacciotti Oliviero Giannini Stefano Guarino Andrea Budelli Roberto Nigro |
| author_facet | Gennaro Salvatore Ponticelli Marianna Gallo Ilaria Cacciotti Oliviero Giannini Stefano Guarino Andrea Budelli Roberto Nigro |
| author_sort | Gennaro Salvatore Ponticelli |
| collection | DOAJ |
| description | 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 <i>Lactobacillus paracasei</i> 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. |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T10:07:26Z |
| publishDate | 2022-12-01 |
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| series | Applied Sciences |
| spelling | doaj.art-fb6127c9e32540d5a7dcc5d865dc5e162023-11-16T14:58:58ZengMDPI AGApplied Sciences2076-34172022-12-0113158210.3390/app13010582Genetic Algorithms for Optimal Control of Lactic Fermentation: Modelling the <i>Lactobacillus paracasei</i> CBA L74 Growth on Rice Flour SubstrateGennaro Salvatore Ponticelli0Marianna Gallo1Ilaria Cacciotti2Oliviero Giannini3Stefano Guarino4Andrea Budelli5Roberto Nigro6Department of Engineering, University of Rome Niccolò Cusano, Via don Carlo Gnocchi 3, 00166 Rome, ItalyDepartment of Engineering, University of Rome Niccolò Cusano, Via don Carlo Gnocchi 3, 00166 Rome, ItalyDepartment of Engineering, University of Rome Niccolò Cusano, Via don Carlo Gnocchi 3, 00166 Rome, ItalyDepartment of Engineering, University of Rome Niccolò Cusano, Via don Carlo Gnocchi 3, 00166 Rome, ItalyDepartment of Engineering, University of Rome Niccolò Cusano, Via don Carlo Gnocchi 3, 00166 Rome, ItalyHeinz Innovation Center, Nieuwe Dukenburgseweg 19, 6534 AD Nijmegen Postbus 57, The NetherlandsDepartment of Chemical Engineering, Material and Industrial Production, University of Naples Federico II, P. Tecchio 80, 80125 Naples, ItalyModelling 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 <i>Lactobacillus paracasei</i> 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.https://www.mdpi.com/2076-3417/13/1/582genetic algorithmempirical modellingbacterial growthfermentation process<i>Lactobacillus paracasei</i> |
| spellingShingle | Gennaro Salvatore Ponticelli Marianna Gallo Ilaria Cacciotti Oliviero Giannini Stefano Guarino Andrea Budelli Roberto Nigro Genetic Algorithms for Optimal Control of Lactic Fermentation: Modelling the <i>Lactobacillus paracasei</i> CBA L74 Growth on Rice Flour Substrate Applied Sciences genetic algorithm empirical modelling bacterial growth fermentation process <i>Lactobacillus paracasei</i> |
| title | Genetic Algorithms for Optimal Control of Lactic Fermentation: Modelling the <i>Lactobacillus paracasei</i> CBA L74 Growth on Rice Flour Substrate |
| title_full | Genetic Algorithms for Optimal Control of Lactic Fermentation: Modelling the <i>Lactobacillus paracasei</i> CBA L74 Growth on Rice Flour Substrate |
| title_fullStr | Genetic Algorithms for Optimal Control of Lactic Fermentation: Modelling the <i>Lactobacillus paracasei</i> CBA L74 Growth on Rice Flour Substrate |
| title_full_unstemmed | Genetic Algorithms for Optimal Control of Lactic Fermentation: Modelling the <i>Lactobacillus paracasei</i> CBA L74 Growth on Rice Flour Substrate |
| title_short | Genetic Algorithms for Optimal Control of Lactic Fermentation: Modelling the <i>Lactobacillus paracasei</i> CBA L74 Growth on Rice Flour Substrate |
| title_sort | genetic algorithms for optimal control of lactic fermentation modelling the i lactobacillus paracasei i cba l74 growth on rice flour substrate |
| topic | genetic algorithm empirical modelling bacterial growth fermentation process <i>Lactobacillus paracasei</i> |
| url | https://www.mdpi.com/2076-3417/13/1/582 |
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