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机器学习辅助软件的开发,用于预测乳酸细菌和Listeria monocytogenes的相互作用行为

Fatih Tarlak¹, Jean Carlos Correia Peres Costa², Ozgun Yucel³

¹Department of Bioengineering, Faculty of Engineering, Gebze Technical University, Gebze 41400, Kocaeli, Turkey.

Life (Basel, Switzerland)

|February 26, 2025

概括

使用机器学习和传统方法的精确预测模型准确地描述了食品中的微生物相互作用. 这种生物保存方法通过研究乳酸细菌和Listeria monocytogenes的动态来提高食品安全性和延长保质期.

科学领域:

食品科学食品科学食品科学
微生物学微生物学
计算生物学计算生物学

背景情况:

生物保存利用有益的微生物来提高食品安全和保质期.
了解微生物动态对于有效的生物保存策略至关重要.
乳酸细菌 (LAB) 和Listeria monocytogenes的相互作用是食品系统的关键.

研究的目的:

为LAB和L. monocytogenes的生长和相互作用开发精确的预测模型.
为了比较微生物动态的传统和机器学习建模方法.
将先进的建模纳入用于食品应用的预测微生物学.

主要方法:

在异热条件下 (4,10,30°C) 分析BHI和牛奶中发表的生长曲线.
修改后的Gompertz和Lotka-Volterra模型用于单种和共同种植模拟的应用.
利用机器学习方法来提高预测准确度.

主要成果:

修改后的Gompertz模型最好地描述了单一培养; 组合的Gompertz-Lotka-Volterra在共同培养中表现出色.
通过传统模型实现了高调整的R平方 (高达0.978) 和低的RMSE (低至0.324).
机器学习模型显示出优异的性能 (R2调整至0.988,RMSE降至0.242),验证了研究结果.

结论:

机器学习为传统的预测微生物学建模提供了强大的,精简的替代方案.
开发的模型准确地描述了微生物相互作用,推进了生物保存技术.
集成ML辅助软件提高了预测食品中的微生物行为的准确性.

关键词:

李斯特里亚单细胞原生菌 (Listeria monocytogenes) 是一种生物保存生物保存交互模型的交互模型.乳酸细菌是一种乳酸细菌.机器学习是机器学习.

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