基于时空与风险物视角的茶叶风险分级模型的构建

Establishment of tea risk rank model based on the perspective of time, space and risk substances

中文关键词:  茶叶  风险分级  时空分析  模型

英文关键词:tea  risk rank  spatio-temporal analysis  model

中文摘要:

      目的 对茶叶中的风险物进行分级, 结合时空视角分析茶叶不合格风险物情况, 对茶叶监督监管提出建议。方法 采用熵值法计算茶叶产业链中关键环节风险物大小, 并利用二八原则对风险进行分级排序; 采用描述性统计方法对茶叶数据进行时空分析; 利用全局与局部自相关分析方法探索茶叶中风险等级高及较高风险物的全国空间分布规律。结果 生产环节和流通环节高风险物质是氟、砷、铜、镉、铬和铅, 餐饮环节高风险物质是氟、铅、联苯菊酯、多菌灵、啶虫脒和吡虫啉; 2016—2020年期间茶叶不合格率总体变化稳定, 被抽样省份中内蒙古茶叶不合格率最高, 抽样环节中流通环节不合格率最高, 抽样场所中生产环节茶叶不合格主要发生在成品库已检区、流通环节茶叶不合格主要发生在超市; 全国各省茶叶中高及较高风险物合格率只有柠檬黄存在全局空间相关性, 局部自相关分析时, 只有氟、铅和草甘膦有高值-高值集聚和低值-低值集聚的现象。结论 茶叶风险物在不同环节、空间风险级别不同, 研究结果可提高茶叶监管效率, 为企业自律提供参考。

英文摘要:

      Objective To classify the risk substances in tea, analyze the unqualified risk substances in tea from the perspective of time and space, and put forward suggestions for the supervision and regulation of tea. Methods The entropy method was used to calculate the value of the risk substances in the key links in the tea industry chain, and the two-eight principle was used to rank the risks; the descriptive statistical method was used to analyze the spatio-temprol situation of the tea data; the global and local autocorrelation analysis methods were used to explore the national spatial distribution of highest-risk and high-risk substances in tea. Results The highest-risk substances in the production and circulation link were both fluorine, arsenic, copper, cadmium, chromium and plumbum, and highest-risk substances in catering link were fluorine, plumbum, bifenthrin, carbendazim, acetamiprid and imidacloprid. During 2016 to 2020, the unqualified rates of tea were stable, and the unqualified rate of tea in Inner Mongolia, China, was the highest in sample provinces, the unqualified rate of the circulation link in the sampling link was the highest, the unqualified tea in the production link in the sampling site mainly occured in the inspection area of the finished product warehouse, and the unqualified tea in the circulation link mainly occurred in the supermarket; only lemon yellow had global spatial correlation in the pass rate of highest and high risk substances of tea in all provinces of China, when the local autocorrelation analysis, only fluorine, lead and glyphosate showed high-high cluster and low-low cluster. Conclusion Tea risk substances have different risk levels in different links and spaces, the results can improve the efficiency of tea regulation, and provide reference for enterprise self-regulation.