摘要
为科学合理评价危害物在粮食供应链各环节中的综合风险,本研究在分析全国各省粮食供应链抽检数据及其他维度数据的基础上,结合粮食供应链中风险因素,构建多维层次风险指标体系,将大量多维异构数据转化为半定量风险指标。应用关联规则挖掘一级指标和二级指标的内在关联确定权重分配,结合自组织映射算法将各指标变量映射到风险等级,明确指标交叉关联,构建粮食供应链危害物综合风险等级评价方法。通过对粮食产品风险等级进行评价,得出风险较高的重点省份为山东省、河南省,典型区域为城市区域,关键环节为流通环节以及以铝残留为代表的一系列高风险危害物。该评价体系的确立可以为监管机构制定有针对性的抽检策略、确立优先监管领域,并为合理分配风险监管资源提供科学依据。
In order to scientifically and reasonably evaluate the comprehensive risks of hazardous materials in each link of the grain supply chain,sample survey data from the grain supply chains in many provinces across the country and data from other dimensions were analyzed in this paper.On this basis,a multidimensional hierarchical risk indicator system was built by using risk factors in the grain supply chain to convert a large number of multidimensional heterogeneous data into semi-quantitative risk indicators.The association rules were applied to excavate the intrinsic correlation between the first-level indicators and the second-level indicators for determining the weight distribution.Further,the self-organizing maps algorithm was used to map each indicator variable to a risk level for analysis of the cross-correlation.Finally,a comprehensive evaluation method for risk levels of hazards in the grain supply chain.By evaluating the risk level of grain products,it was concluded that the key provinces with higher risks were Shandong and Henan provinces,typically in urban areas,and the key link was circulation as well as a series of high-risk hazards,represented by aluminum residues.The evaluation system established in this paper provides a scientific basis for the regulatory agencies to develop target-oriented sample survey strategies,establish priority supervision areas and legitimately allocate supervision resources.
作者
王小艺
王珍妮
孔建磊
金学波
苏婷立
白玉廷
WANG Xiaoyi;WANG Zhenni;KONG Jianlei;JIN Xuebo;SU Tingli;BAI Yuting(Artificial Intelligence Academy,Beijing Technology and Business University,Beijing 100048,China;Beijing Key Laboratory of Big Data Technology for Food Safety,Beijing Technology and Business University,Beijing 100048,China)
出处
《食品科学》
EI
CAS
CSCD
北大核心
2020年第9期15-22,共8页
Food Science
基金
“十三五”国家重点研发计划重点专项(2017YFC1600605)
北京市教育委员会科技计划一般项目(PXM2019_014213_000007-KM201910011010)
科技创新服务能力建设-基本科研业务费-粮油食品供应链危害物识别与预警技术创新平台项目(PXM2018_014213_000033)。
关键词
多维层次指标体系
关联规则挖掘
自组织映射
综合风险分级评价
multidimensional hierarchical index system
association rule mining
self-organizing mapping
comprehensive risk assessment
