Abstract
At present, China exists a problem that the cost of food sampling inspection is too high. This paper attempts to reduce the number of sampling inspection items in the same food category, reduce the cost of food sampling inspection, and improve the work efficiency through the association analysis of national sampling inspection data. And this paper applies Apriori algorithm to analyse the association rules, which is based on the unqualified pastry sampling inspection data in the 2019 national food sampling inspection database. Finally, we obtain 10 strong association rules through experiments. The results show that this association analysis can reduce the workload of food sampling inspection effectively.
Highlights
Food sampling inspection data is crucial for food safety
Association analysis method has been widely used in food sampling inspection, such as the association analysis of multiple attributes in food safety testing data, but it still has not solved the problem of how to scientifically sample inspection and determine the sampling inspection items of the samples
In this study, based on the unqualified sampling inspection data of pastry food in the 2019 national sampling inspection database, the Apriori algorithm is used to analyze the association of the preprocessed food sampling inspection data
Summary
Food sampling inspection data is crucial for food safety. China spends a large number of people and property on food sampling inspection every year. It has gradually accumulated a great number of data. Through making a deep analysis of the data, it can effectively reduce the cost of food sampling inspection, and improve the efficiency of food sampling inspection. We preprocess the data, and use the association rules mining method—Apriori algorithm to analyse the correlation of the sampling inspection items.
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