A food safety targeted sampling decision-making method based on association rule mining and GNNs.

To solve the problem of subjectivity and low targeting of task-assigned food safety sampling, in this study, a targeted sampling decision-making method for food safety was proposed. First, a food decision-making factor reasoning module based on association analysis was constructed. An improved frequent pattern growth algorithm with constraints was used to mine food factor association rules based on decision-making factors. Second, a decision-making support module for targeted sampling to support food safety was constructed. A graph neural network was used to perform decision-making on the sampling frequency. The CRITIC-TOPSIS method was used to determine the sampling sequence of hazardous substances. In this study, experiments and analyses were conducted on sampling data of processed grain products in China (nationwide) and in Shandong Province from 2020 to 2022. Decision-making results of the sampling frequency and sampling order of hazardous substances were generated, thus demonstrating the wide applicability of the decision-making method.