Developing Predictive Models for Food Contamination and Nutrition Risk Assessment

Abstract

Food safety and nutritional integrity are a major concern in public health. The paper investigates the creation of predictive analytics of food contamination and nutrition risks assessment through the combination of data science, artificial intelligence, and epidemiological studies. The updated methods of Hazard Analysis and Critical Control Points (HACCP) are explored, as well as AI controlled contamination systems that may react to supply chain real-time monitoring. The importance of the collaboration of agencies in providing better predictability and guiding proactive actions is highlighted. The results indicate that predictive modeling helps to enhance earlier identification of microbial hazards and conform food safety practices with nutritional health standards. These models would offer a framework in reducing foodborne illness outbreaks, regulatory compliance, and boost national biosecurity by using advanced analytics. The paper identifies the transformational nature of data-based methods to the development of future food safety policies.