Machine Learning and Advanced Statistical Modeling Can Identify Key Quality Management Practices That Affect...

Area of Science:

• Food Science
• Microbiology
• Data Science

Background:

• Post-pasteurization contamination (PPC) by gram-negative bacteria causes spoilage in pasteurized fluid milk, impacting the dairy industry.
• Current understanding of processing facility practices influencing PPC is insufficient to prioritize interventions for maximum impact and return on investment.

Purpose of the Study:

• To identify and rank the relative importance of factors associated with PPC in fluid milk processing facilities.
• To provide data-driven insights for dairy facilities to select effective interventions for reducing PPC.

Main Methods:

• Utilized a longitudinal dataset of bacterial spoilage indicators from 23 processing facilities (July 2015-November 2017).
• Collected data on fluid milk quality management practices via facility surveys.
• Employed multimodel inference and conditional random forest analyses to identify and rank factors associated with PPC, handling correlated and unbalanced data.

Main Results:

• Nearly all assessed factors showed individual associations with PPC.
• Cleaning and sanitation practices, good manufacturing practices, container type, in-house finished product testing, and quality department designation were identified as key drivers of PPC.
• Machine learning approaches proved effective for analyzing complex, real-world food safety data.

Conclusions:

• Specific processing practices, including sanitation and quality management systems, are critical targets for reducing post-pasteurization contamination in fluid milk.
• The study demonstrates the utility of advanced analytical methods for improving decision-making in food safety and quality control.
• Implementing targeted interventions based on identified factors can enhance milk quality and reduce economic losses due to spoilage.