机械模型和高斯过程模型的应用,用于预测婴儿菜在冷藏期间的细菌生长
Sriya Sunil1, Sarah I Murphy2, Ruixi Chen1
1Department of Food Science, Cornell University, Ithaca, NY 14853, United States.
Journal of food protection
|November 28, 2024
概括
机械学和机器学习模型在预测菜上的细菌生长方面表现出类似的准确性. 这项研究有助于食品行业管理微生物腐烂并确保食品安全.
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:
- 细菌生长的预测模型对于食品行业关于微生物腐烂的决策至关重要.
- 机器学习 (ML) 模型越来越多地使用,与传统的机械 (M) 方法形成鲜明对比.
- 将这些建模策略进行比较对于优化食品安全协议至关重要.
研究的目的:
- 为了比较机械 (M) 模型和高斯过程 (GP) ML模型对菜细菌生长的预测性能.
- 用美国和中国供应链的数据评估模型准确性,包括新收集的数据.
- 评估模型适用于菌株级和种群级细菌生长的适用性.
主要方法:
- 开发并安装了使用菌株级和种群级细菌生长数据的机械 (M) 模型.
- 使用种群级细菌生长数据开发和装配高斯过程 (GP) 模型.
- 使用根平均平方误差 (RMSE) 值进行模型性能比较.
主要成果:
- 在中国供应链中,菜上的细菌生长在本地和电子商务分销道之间没有显著差异.
- 机械模型显示RMSE值在0.72到1.09 log10 CFU/g之间.
- 高斯过程模型显示RMSE值为0.68和0.81 log10 CFU/g.
- 两种M和GP模型都在预测菜上的细菌生长方面表现出可比的准确性.
结论:
- 机械和高斯过程模型为菜上的细菌生长提供了类似的预测准确性.
- 这些发现支持使用两种建模方法来提高食品安全和管理食品工业中的微生物腐烂.
- 该研究为优化供应链管理和确保绿叶农产品安全提供了宝贵的见解.
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