Analysis And Aplication Of Food Safety Data Based On Data Mining

As the whole society pays more and more attention to food safety,the food safety supervision departments at all levels of the government are constantly increasing the human,financial and material resources investment in food safety supervision.The funds invested in food safety testing are also increasing,and a large amount of food safety inspection data can be generated every year.Data accumulates year by year,however,the use of data is mostly statistical analysis,and there are few applications for deep mining.Therefore,how to further analyze,excavate and utilize the food safety testing data accumulated year by year has become a subject worth studying.As a data mining technology that discovers the rules and knowledge hidden in the data from the data,it has been widely used in many fields and has achieved good results.There have been many successful application cases.If data mining technology is applied to food safety testing data,it can help regulatory authorities to effectively use these testing data,so as to obtain more valuable information and better guide food safety supervision.This paper studies the application of data mining technology in food safety data.The work done in this article mainly has the following aspects:First,it introduces food safety related knowledge,data mining related knowledge,and industry application of data mining.Collected the food safety data of unqualified sampling in 2015-2019 published by the official website of the Shandong Food and Drug Administration.Through a series of pretreatments including cleane,integrate,reduce,converte,obtained suitable data set for mining.Then the pre-processed food safety data set was analyzed for association rules,and some meaningful association rules were obtained,and the association rules were professionally interpreted.Then,a classification study on food safety data was carried out.The classification algorithm was used to model the pre-processed food safety data set,and a classification prediction model for non-conformance indicators was established.The subsequent 97 samples of unqualified food safety data were used for testing the established model,and the classification accuracy rate was close to accuracy rate of the classification model,and the classification rules derived from the established classification model were professionally interpreted.The effective use of these data mining results and data mining models can provide the basis for decision-making for food safety supervision and have important reference value for future food safety supervision.