Abstract
Accurately detecting risk points is crucial to food safety risk assessment and prewarning in food safety risk management because it helps solve food safety problems at their source. With the advancement of informationization in the food industry, a vast quantity of food safety data generated throughout sample inspection, transportation, storage, food processing, and raw material production has become urgently necessary to develop and use. Nevertheless, the existing food safety risk warning system has several flaws, including a high personnel cost, a low data utilization rate, and a crude risk measurement system. As a result, we described the data attributes for further analysis and sorted the food safety data in this study. In the meantime, to fully exploit the high dimension and the data's large amount, a mixture of fuzzy hierarchy partition and prior risk probability could be used to calculate fuzzy comprehensive risk values depending on multiple traits as the predicted outcome of a predictive model which can forecast and confirm risk levels, created with the use of a light gradient boosting machine (LightGBM) and skilled adjustment procedures. Finally, the outcomes of the multiple methods are compared using the same training and test data in order to verify the efficiency of LightGBM. The risk analysis results presented in this study, including attribute importance distribution and the risk values, can be useful to decision-makers.
Keywords:
risk points; predictive model; risk management; fuzzy comprehensive risk values
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