An improved traceability system for food quality assurance and evaluation based on fuzzy classification and neural network

Abstract

Currently, the food safety incidents happened frequently in china and the customer confidence declined rapidly, then the problems related to food quality and safety have attracted more and more social attention. Considering the concern with regard to food quality assurance and consumer confidence improvement, many companies have developed a traceability system to visualize the supply chain and avoid food safety incidents. In this paper, we proposed an improved food traceability system which can not only achieve forward tracking and diverse tracing like the existing systems do, but also evaluate the food quality timely along the supply chain and provide consumers with these evaluating information, to mainly enhance the consumer experience and help firms gain the trust of consumers. For the food quality evaluation, the method of fuzzy classification was used to evaluate the food quality at each stages of supply chain while the artificial neural network was adopted to derive the final determination of the grade of food quality according to all the stage quality evaluations. A case study of a pork producer was conducted, and the results showed that the improved traceability system performed well in food quality assurance and evaluation. In addition, implications of the proposed approach were discussed, and suggestions for future work were outlined.