Food risk assessment based on NSGA-II algorithm: a case study of alcoholic beverages

Alcoholic beverages have been a significant industry. However, they present food safety risks, necessitating heightened regulation and monitoring. The safety risk assessment of alcoholic beverages encompasses a variety of factors, including microorganisms, excessive methanol content, chemical adulteration, and food additives. The data used in this paper is sourced from the National Food Safety Sampling Inspection Results Query System in China. The primary conclusions are as follows: (1) A deviation reconstruction-based multi-weight decision model is proposed, which incorporates three distinct weight acquisition methods to perform optimization calculations. (2) The comparative investigations serve as evidence of the risk assessment model’s effectiveness. (3) The multi-weight decision model based on deviation reconstruction and the NSGA-II (non-dominated sorting genetic algorithm-II) exhibit excellent adaptability. The results of the risk assessment are analyzed, and recommendations are offered based on the categories of alcoholic beverages and the detection indicators. This paper investigates the regulation of food safety and the identification of risks in intoxicating beverages. It also transitions the response to food safety risks from a passive to an active protection strategy. This method has the potential to improve the public’s perception of safety and satisfaction with food-related concerns, as well as to provide the industry with practical solutions for sustainable growth. Simultaneously, this document establishes new risk assessment regulations for alcoholic beverages, offering recommendations for enhancing regulatory efficiency.