基于大数据的茶叶安全风险预警分析  

Early Warning Analysis of Tea Safety Risk Based on Big Data

作　　者：王永真[1] 王钰 卢作焜 王德国[1] WANG Yongzhen;WANG Yu;LU Zuokun;WANG Deguo(Key Laboratory of Biomarker Based Rapid-detection Technology for Food Safety of Henan Province, Xuchang University, Xuchang 461000, China)

机构地区：[1]许昌学院河南省食品安全生物标识快检技术重点实验室,河南许昌461000

出　　处：《许昌学院学报》2018年第12期61-63,共3页Journal of Xuchang University

基　　金：国家重点研发计划项目(2016YFD0500704-4);河南省科技计划项目(182102110285);河南省高等学校科技创新团队支持计划(15IRTSTHN016)

摘　　要：以国家食品药品监督管理局发布的截止至2018年3月底之前的所有茶叶检测结果数据为基础,利用基于大数据的方法进行统计分析,得到茶叶总体不合格率及不合格项目的频次,同时分析每个省份抽查的不合格率较高的项目.结果显示,在所检测的茶叶中,出现频次较高的不合格项目有三氯杀螨醇、氰戊菊酯、日落黄等.有助于为茶叶安全市场预警提供方向依据,把控茶叶监管的重点,提高监管力度.In this paper, based on the data of all tea test results released by the China Food and Drug Administration by the end of March 2018, using the method based on big data for statistical analysis, the total unqualified rate of tea and the frequency of unqualified items were obtained. At the same time, the items with high nonconformity rate in each province were analyzed. The results in the test tea showed that the unqualified items which had high frequency were dicofol, fenvalerate, sunset yellow and so on, which was helpful to provide the direction basis for the warning of tea safety market, and to control the focus of tea supervision and strengthen supervision.

关 键 词：茶叶 不合格率 预警 日落黄 

分 类 号：TS221[轻工技术与工程—粮食、油脂及植物蛋白工程]