下一代预测微生物学:一个软件平台,结合两步,一步和机器学习建模.
Fatih Tarlak1, Büşra Betül Şimşek1, Melissa Şahin1
1Department of Bioengineering, Gebze Technical University, 41400 Gebze, Turkey.
Foods (Basel, Switzerland)
|September 27, 2025
概括
一个新的软件平台将预测微生物学模型与机器学习 (ML) 集成在一起,以准确地预测微生物生长和抑制. 机器学习模型,特别是高斯过程回归和随机森林回归,在这种数据驱动的决策支持工具中表现出卓越的表现.
科学领域:
- 预测性微生物学 预测性微生物学
- 计算生物学 计算生物学
- 食品科学 食品科学 食品科学
背景情况:
- 微生物生长和抑制是复杂的过程,由于食品系统中的非线性相互作用,这些过程挑战了传统的机械模型.
- 准确的预测对于食品安全,保质期估计和抗菌素测试至关重要.
研究的目的:
- 开发一个集成经典预测微生物学模型与机器学习 (ML) 方法的动态软件平台.
- 能够直接比较各种增长和抑制模型中的一步式和两步式建模方法.
- 扩大使用ML用于微生物生长和抑制的预测能力,超出传统的参数框架.
主要方法:
- 集成经典的预测微生物学模型 (一步和两步) 与ML算法 (支持向量回归,随机森林回归,高斯过程回归).
- 实施四种增长模型 (修改的Gompertz,Logistic,Baranyi,Huang) 和三种抑制模型 (Log-Linear,Log-Linear + Tail,Weibull).
- 使用实验和文献数据集进行验证,以评估预测准确性和稳定性.
主要成果:
- 开发的平台在模拟微生物生长和抑制方面表现出高的预测准确性和稳定性.
- 机器学习模型,特别是高斯过程回归和随机森林回归,超过了经典的预测模型.
- 该平台为各种应用程序提供灵活的模型评估和选择.
结论:
- 这种新的软件平台为研究人员,行业和监管机构提供了一个强大的数据驱动的决策支持工具.
- 它增强了食品安全管理,保质期估计和抗菌素测试方面的能力.
- 未来的工作包括平台优化,数据库集成和扩展到新兴的预测微生物学领域.
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