PRIAS: An Intelligent Analysis System for Pesticide Residue Detection Data and Its Application in Food Safety Supervision.

Yi Chen,Qiaoying Chang,Chunlin Fan,Haifeng Dou

doi:10.3390/foods11060780

Abstract

Pesticide residue is a prominent factor that leads to food safety problems. For this reason, many countries sample and detect pesticide residues in food every year, which generates a large amount of pesticide residue data. However, the way to deeply analyze and mine these data to quickly identify food safety risks is still an unresolved issue. In this study, we present an intelligent analysis system that supports the collection, processing, and analysis of detection data of pesticide residues. The system is first based on a number of databases such as maximum residue limit standards for the fusion of pesticide residue detection results; then, it applies a series of statistical methods to analyze pesticide residue data from multiple dimensions for quickly identifying potential risks; it uses the Apriori algorithm to mine the implicit association in the data to form pre-warning rules; finally, it applies Word document automatic generation technology to automatically generate pesticide residue analysis and pre-warning reports. The system was applied to analyze the pesticide residue detection results of 42 cities in mainland China from 2012 to 2015. Application results show that the system proposed in this study can greatly improve the depth, accuracy and efficiency of pesticide residue detection data analysis, and it can provide better decision support for food safety supervision.

Highlights

Pesticides, which are a class of agrochemicals for preventing and controlling pests and regulating plant growth, are widely used in the growth of edible agricultural products
Pesticide residues are detected in all of the agricultural products that people frequently purchase for use, with detection rates (DRap) ranging from 43.68% to 99.67%, most of which are above 80%, and these findings are of concern
The application results show that PRIAS can provide decision support for pesticide residue supervision and pre-warning from three aspects: data accumulation, distribution characteristics and potential risk discovery, and automatic generation of analysis reports

Summary

Introduction

Pesticides, which are a class of agrochemicals for preventing and controlling pests and regulating plant growth, are widely used in the growth of edible agricultural products. The excessive use of pesticides will threaten human life and cause damage to human health [1–3]. For this reason, many countries have formulated monitoring programs for pesticide residues in food and regularly conduct random inspections on the pesticide residue content in locally marketed food/agricultural products to assess the levels of pesticide residues in foods and safety risk [4]. The European Food Safety Authority (EFSA) organizes its member states to monitor pesticide residues in food and publishes reports every year [6]. China’s State Administration for Market Regulation (SAMR) conducts random inspections on marketed foods and releases inspection results every month. Pesticide residues are an important aspect of food safety supervision [8]

Methods

Discussion

Conclusion