Statistical Learning for Food Safety Assessment Under Privacy and Resource Constraints

BACKGROUND

The food processing industry has a long-standing need for food-safety data analytics to aid rapid and reliable decision making. A major food safety concern is Listeria monocytogenes, a foodborne pathogen with a case fatality rate of 20% and more than $4 billion in annual costs to American consumers and food companies. Science-based Listeria environmental monitoring programs in food processing facilities are a key tool to reduce the risk of food contamination. The key challenge is the high cost and risk involved in Listeria testing and experimentation. Decision making in existing Listeria control programs are typically based on sparse data and human intuitive judgment, resulting in highly suboptimal solutions, especially in complex situations and under stress. While there is considerable interest in the food industry for agent-based simulation models to aid decision making, there are challenges that prevent their full utilization.

GOALS

• Build digital-twin models of processing facilities, utilizing pre-existing industry datasets and developing the ability of integrating diverse streams of data for improved model parameterization.
• Develop statistical learning and inference algorithms for rapid and reliable pathogen detection and risk-averse prediction of effective corrective measures, by integrating synthesized data from the digital-twin models with the facility-owned data.
• Address practical constraints, in particular, privacy constraints to incentivize data sharing and improve human trust, as well as resource constraints in terms of computation and memory complexity.

IMPACT

• Allow operators to receive streamlined information (e.g., in real-time) with the capacity to rapidly identify key factors or sites at risk, ultimately improving food-safety related decision-making and resulting in more cost- effective pathogen control programs.
• Extend the data analytics proposed here using Listeria monocytogenes models to decision support tools for other pathogens affecting the food production, including the spread of COVID-19 among workers in the food industry.