预测 Pseudomonas 的种类. 人口在食品产品和文化媒体使用基于机器学习的回归方法
1Department of Nutrition and Dietetics, Istanbul Gedik University, Kartal, Istanbul 34876, Turkey.
Life (Basel, Switzerland)
|July 29, 2023
概括
机器学习模型可以准确预测食物中的细菌种群. 随机森林回归显示出最好的预测性能,为微生物生长和生存分析提供了传统方法的替代方案.
科学领域:
- 预测性微生物学 预测性微生物学
- 食品安全 食品安全
- 计算生物学是一种计算生物学.
背景情况:
- 传统的建模方程在预测性食品微生物学中使用.
- 机器学习 (ML) 提供了基于数据的替代方法.
- 机器学习算法分析大型数据集,以获得微生物生长和生存的洞察力.
研究的目的:
- 应用基于ML的回归方法来估计细菌种群.
- 评估支持向量回归 (SVR),高斯过程回归 (GPR),决策树回归 (DTR) 和随机森林回归 (RFR).
- 为了预测 * 伪omonas * spp. 的生长或生存. 在食品和文化媒体中.
主要方法:
- 收集了5618个数据点,这些数据点是关于*Pseudomonas* spp. 的. 从ComBase数据库中获取这些数据.
- 使用的预测变量:温度,盐度,水活性和酸度.
- 使用R2,RMSE,Bf和Af的统计指标评估算法适用性.
主要成果:
- 所有测试的回归算法都显示出适当的估计能力.
- R2值在0.886到0.913之间;RMSE在0.724到0.899.之间.
- 随机森林回归 (RFR) 显示出最佳的预测性能,并与外部数据验证.
结论:
- 机器学习方法是预测微生物学中传统建模的可行替代方案.
- RFR有效预测食品中的微生物生存和生长.
- ML模型的预测能力取决于特定食品矩阵的大数据集的可用性.
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