摘要
食品检测数据作为食品风险分析的重要工具,针对同类食品所做检测项目不同而导致最终的数据矩阵部分缺失,且已有的食品检测数据大部分为未检出等问题,通过引入词频-逆文档频率(term frequency-inverse document frequency,TF-IDF)的权重确定办法,构建一种新型的食品风险分析模型。本文以2019-2020年为时间段,收集某市食用农产品的蔬菜样本抽检信息作为分析数据,通过模型计算得到蔬菜中各样品的风险指数。结果显示:2019-2020年间检测的蔬菜产品中,风险指数高的为韭菜和芹菜,超标指数为毒死蜱,在监管中需加强关注,而其余蔬菜大多呈现低风险情况。本分析模型相较于其它传统分析方法,能给出具体的风险指数,在评价上具有直观性,且当数据样本越大,评价效果越好。同时,本模型基于信息理论来设置权重,消除了主观因素在评价中的影响,在应对多样化食品数据时更具有实用性。模型的建立在大数据的时代背景下,对于深入研究食品安全风险及其评价方法新路径提供一个新思路。
Food testing data is an important tool for food risk analysis.The final data matrix is missing due to different testing items for similar foods,and most of the existing food testing data is undetected.Through the introduction of TF-IDF(The term frequency-inverse document frequency)weight determination method has constructed a new type of food risk analysis model.This paper uses the sampling information of vegetable samples of edible agricultural products in a city from 2019 to 2020 as the research data,and calculates the risk index of each sample in the vegetable through the model.The results show that among the vegetable products tested from 2019 to 2020,the high-risk index is leeks and celery,and the over-standard index is chlorpyrifos,which needs to be paid more attention in supervision,while most of the remaining vegetables are low-risk.Compared with other traditional analysis methods,this analysis model can give a specific risk index,is intuitive in evaluation,and shows better evaluation performance in big data analysis.At the same time,this model sets weights in an objective and universal mode,which eliminates the influence of subjective factors in the evaluation and further enhances the practicability in diversified data analysis.The model is established in the context of the era of big data,and provides a new way of thinking for further in-depth research and exploration of new paths for food safety risks and evaluation methods.
作者
姚振民
邢家溧
承海
郑睿行
毛玲燕
徐晓蓉
张书芬
沈坚
Yao Zhenmin;Xing Jiali;Cheng Hai;Zheng Ruihang;Mao Lingyan;Xu Xiaorong;Zhang Shufen;Shen Jian(Ningbo Academy of Product and Food Quality Inspection(Ningbo Fibre Inspection Institute),Ningbo 315048,Zhejiang)
出处
《中国食品学报》
EI
CAS
CSCD
北大核心
2022年第12期324-331,共8页
Journal of Chinese Institute Of Food Science and Technology
基金
国家市场监管总局科技计划项目(2019MK080,2020MK117)
浙江省基础公益研究计划项目(LGC20C200013)
宁波市自然科学基金项目(202003N4196,2019A610438,2019A610437)
宁波市泛3315创新团队(2018B-18-C)
宁波市高新精英创新团队(甬高科[2018]63号)。
