A Machine–Learning Approach Reveals That Bacterial Spore Levels in Organic Bulk Tank Milk are Dependent on Farm Characteristics and Meteorological Factors
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...
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Elsevier
2025-04-01
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| Series: | Journal of Food Protection |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0362028X25000298 |
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| author | Chenhao Qian Renee T. Lee Rachel L. Weachock Martin Wiedmann Nicole H. Martin |
| author_facet | Chenhao Qian Renee T. Lee Rachel L. Weachock Martin Wiedmann Nicole H. Martin |
| author_sort | Chenhao Qian |
| collection | DOAJ |
| description | 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 log10 mesophilic spore count, log10 thermophilic spore count, and log10 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. |
| format | Article |
| id | doaj-art-7216f0db166542e29de0add95ef35408 |
| institution | OA Journals |
| issn | 0362-028X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
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| series | Journal of Food Protection |
| spelling | doaj-art-7216f0db166542e29de0add95ef354082025-08-20T02:27:07ZengElsevierJournal of Food Protection0362-028X2025-04-0188510047710.1016/j.jfp.2025.100477A Machine–Learning Approach Reveals That Bacterial Spore Levels in Organic Bulk Tank Milk are Dependent on Farm Characteristics and Meteorological FactorsChenhao Qian0Renee T. Lee1Rachel L. Weachock2Martin Wiedmann3Nicole H. Martin4Department of Food Science, Cornell University, Ithaca, New York, United StatesDepartment of Food Science, Cornell University, Ithaca, New York, United StatesDepartment of Food Science, Cornell University, Ithaca, New York, United StatesDepartment of Food Science, Cornell University, Ithaca, New York, United StatesCorresponding author.; Department of Food Science, Cornell University, Ithaca, New York, United StatesBacterial 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 log10 mesophilic spore count, log10 thermophilic spore count, and log10 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.http://www.sciencedirect.com/science/article/pii/S0362028X25000298Dairy farm managementDairy spoilageDecision support toolOrganic milkPredictive model |
| spellingShingle | Chenhao Qian Renee T. Lee Rachel L. Weachock Martin Wiedmann Nicole H. Martin A Machine–Learning Approach Reveals That Bacterial Spore Levels in Organic Bulk Tank Milk are Dependent on Farm Characteristics and Meteorological Factors Journal of Food Protection Dairy farm management Dairy spoilage Decision support tool Organic milk Predictive model |
| title | A Machine–Learning Approach Reveals That Bacterial Spore Levels in Organic Bulk Tank Milk are Dependent on Farm Characteristics and Meteorological Factors |
| title_full | A Machine–Learning Approach Reveals That Bacterial Spore Levels in Organic Bulk Tank Milk are Dependent on Farm Characteristics and Meteorological Factors |
| title_fullStr | A Machine–Learning Approach Reveals That Bacterial Spore Levels in Organic Bulk Tank Milk are Dependent on Farm Characteristics and Meteorological Factors |
| title_full_unstemmed | A Machine–Learning Approach Reveals That Bacterial Spore Levels in Organic Bulk Tank Milk are Dependent on Farm Characteristics and Meteorological Factors |
| title_short | A Machine–Learning Approach Reveals That Bacterial Spore Levels in Organic Bulk Tank Milk are Dependent on Farm Characteristics and Meteorological Factors |
| title_sort | machine learning approach reveals that bacterial spore levels in organic bulk tank milk are dependent on farm characteristics and meteorological factors |
| topic | Dairy farm management Dairy spoilage Decision support tool Organic milk Predictive model |
| url | http://www.sciencedirect.com/science/article/pii/S0362028X25000298 |
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