The Development of Machine Learning-Assisted Software for Predicting the Interaction Behaviours of Lactic Acid Bacteria and <i>Listeria monocytogenes</i>
Biopreservation technology has emerged as a promising approach to enhance food safety and extend shelf life by leveraging the antimicrobial properties of beneficial microorganisms. This study aims to develop precise predictive models to characterize the growth and interaction dynamics of lactic acid...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Life |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1729/15/2/244 |
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| Summary: | Biopreservation technology has emerged as a promising approach to enhance food safety and extend shelf life by leveraging the antimicrobial properties of beneficial microorganisms. This study aims to develop precise predictive models to characterize the growth and interaction dynamics of lactic acid bacteria (LAB) and <i>Listeria monocytogenes</i>, which serve as bioprotective agents in food systems. Using both traditional and machine learning modelling approaches, we analyzed data from previously published growth curves in broth (BHI) and milk under isothermal conditions (4, 10, and 30 °C). The models evaluated mono-culture conditions for <i>L. monocytogenes</i> and LAB, as well as their competitive interactions in co-culture scenarios. The modified Gompertz model demonstrated the best performance for mono-culture simulations, while a combination of the modified Gompertz and Lotka–Volterra models effectively described co-culture interactions, achieving high adjusted R-squared values (<i>adjusted R</i><sup>2</sup> = 0.978 and 0.962) and low root mean square errors (<i>RMSE</i> = 0.324 and 0.507) for BHI and milk, respectively. Machine learning approaches further validated these findings, with improved statistical indices (adjusted <i>R</i><sup>2</sup> = 0.988 and 0.966, <i>RMSE</i> = 0.242 and 0.475 for BHI and milk, respectively), suggesting their potential as robust alternatives to traditional methods. The integration of machine learning-assisted software developed in this work into predictive microbiology demonstrates significant advancements by bypassing the conventional primary and secondary modelling steps, enabling a streamlined, precise characterization of microbial interactions in food products. |
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| ISSN: | 2075-1729 |