Machine Learning and Advanced Statistical Modeling Can Identify Key Quality Management Practices That Affect Postpasteurization Contamination of Fluid Milk

What is it about?

Post-pasteurization contamination (PPC) of fluid milk is a challenge for the dairy industry. While processing facility level practices are known to impact the frequency of PPC, the relative importance of different practices is not well defined. Using two different analytical approaches, we identified important quality management practices associated with fluid milk spoilage due to PPC. Factors identified as most important based on multimodel inference and conditional random forest analyses included cleaning and sanitation practices, activities related to good manufacturing practices, container type (which is a proxy for different filling equipment), in-house finished product testing, and designation of a quality department, indicating potential targets for reducing PPC.

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