使用ComBase数据库预测Listeria monocytogenes的生长动态的机器学习建模:一个全面的特征工程方法
Ziwen Zhou1, Haoxin An1, Zhao Li1
1University of Shanghai for Science and Technology, School of Health Science and Engineering USST, Shanghai, 200093, China.
Food research international (Ottawa, Ont.)
|March 7, 2026
概括
一个新的机器学习模型通过结合压力生理原理,准确地预测Listeria monocytogenes的生长和无活化. 这种方法可以提高各种食品类型的食品安全预测.
科学领域:
- 食品微生物学 食品微生物学
- 计算生物学是一种计算生物学.
- 预测建模的预测建模.
背景情况:
- 对于Listeria monocytogenes的传统模型与复杂的环境相互作用和非线性反应作斗争.
- 准确预测病原体的行为对于确保食品安全至关重要.
研究的目的:
- 开发一个先进的机器学习框架,用于Listeria monocytogenes的预测.
- 整合压力生理学原理和可解释的机制,以提高准确性.
主要方法:
- 在各种条件下使用了来自ComBase的2632个观测的精选数据集.
- 开发了基于压力生理学的特征工程模块和基于SHAP的可解释性模块.
- 结合这些与XGBoost进行预测建模.
主要成果:
- 实现了0.90的增长和0.88的无活化R2值,超过了基线模型的表现.
- 确定了影响不同增长阶段的关键因素,如适应性得分和水活动 (Aw).
- 在各种食物矩阵中显著提高了预测准确性和概括性.
结论:
- 基于压力生理学的机器学习框架提高了对Listeria monocytogenes的预测准确性.
- 与以前的方法相比,该模型显示出优越的性能和概括能力.
- 这一框架为改善食品安全评估提供了一个强大的工具.
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