Machine learning tools identify key practices impacting fluid milk contamination

Nicole Martin, PhD

A recent Milk Quality Improvement Program study published in the Journal of Food Protection leveraged advanced statistical tools and machine learning to identify quality management practices in processing facilities that are associated with post-pasteurization contamination in fluid milk. Post-pasteurization contamination (PPC) is a barrier to high quality fluid milk, often causing pre-mature spoilage of product which can impact consumer acceptance and willingness to purchase.

Many fluid milk processors struggle to control PPC, especially when multiple factors at an individual facility may be directly and indirectly causing the contamination. Our study identified that the most important drivers of PPC are; i) cleaning and sanitation practices; ii) activities related to good manufacturing practices; iii) container type (a proxy for different filling equipment); iv) in-house finished product testing, and; v) designation of a quality department. Fluid milk processors should use these results to prioritize implementation of intervention strategies to reduce PPC. Read the fully study (https://doi.org/10.4315/JFP-20-431) or contact Sarah Murphy or Nicole Martin with questions.

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