Qian Chenhao, Lee Renee T, Weachock Rachel L, Wiedmann Martin, Martin Nicole H
Department of Food Science, Cornell University, Ithaca, New York, United States.
Department of Food Science, Cornell University, Ithaca, New York, United States.
J Food Prot. 2025 Apr 22;88(5):100477. doi: 10.1016/j.jfp.2025.100477. Epub 2025 Mar 7.
Bacterial spores in raw milk can lead to quality issues in milk and milk-derived products. As these spores originate from farm environments, it is important to understand the contributions of farm-level factors to spore levels. This study aimed to investigate the impact of farm management practices and meteorological factors on levels of different spore types in organic raw milk using machine learning models. Raw milk from certified organic dairy farms (n = 102) located across 11 states was collected 6 times over a year and tested for standard plate count, psychrotolerant spore count, mesophilic spore count, thermophilic spore count, and butyric acid bacteria. At each sampling date, a survey about farm management practices was collected and meteorological factors were obtained on the date of sampling as well as 1, 2, and 3 days prior. The dataset was stratified separately based on the use of a parlor for milking, number of years since organic certification, and pasture time into subdatasets to address confounders. We constructed random forest regression models to predict log mesophilic spore count, log thermophilic spore count, and log butyric acid bacteria's most probable number as well as a random forest classification model to classify the presence of psychrotolerant spores in each raw milk sample. The summary statistics showed that spore levels vary considerably between certified organic farms but were only slightly higher than those from conventional farms in previous longitudinal studies. The variable importance plots from the models suggest that herd size, certification year, employee-related variables, clipping and flaming udders are important for the spore levels in organic raw milk. The small effects of these variables as shown in partial dependence plots suggest a need for individualized risk-based approach to manage spore levels. Incorporating novel data streams has the potential to enhance the performance of the model as a real-time monitoring tool.
生牛奶中的细菌孢子会导致牛奶及奶制品出现质量问题。由于这些孢子源自农场环境，了解农场层面的因素对孢子水平的影响很重要。本研究旨在使用机器学习模型调查农场管理实践和气象因素对有机生牛奶中不同类型孢子水平的影响。在一年时间里，从11个州的102个经认证的有机奶牛场采集了6次生牛奶样本，并对其进行标准平板计数、耐冷孢子计数、嗜温孢子计数、嗜热孢子计数和丁酸菌检测。在每次采样日期，收集有关农场管理实践的调查信息，并获取采样当天以及前1天、2天和3天的气象因素数据。根据挤奶厅的使用情况、有机认证后的年数和放牧时间，将数据集分别分层为子数据集，以解决混杂因素。我们构建了随机森林回归模型来预测对数嗜温孢子计数、对数嗜热孢子计数和丁酸菌的最可能数，以及一个随机森林分类模型来对每个生牛奶样本中耐冷孢子的存在情况进行分类。汇总统计数据表明，经认证的有机农场之间的孢子水平差异很大，但仅略高于之前纵向研究中传统农场的孢子水平。模型的变量重要性图表明，畜群规模、认证年份、与员工相关的变量、修剪和灼烧乳房对有机生牛奶中的孢子水平很重要。局部依赖图中显示的这些变量的微小影响表明，需要采用基于个体风险的方法来管理孢子水平。纳入新的数据流有可能提高模型作为实时监测工具的性能。
