摘要
食品安全抽检数据中蕴含的掺杂物信息在食品安全早期预警、风险预测等方面有着重要应用.为深入研究食品类别中的高权重掺杂物,首先根据对比学习思想设计了基于特征选择技术的食品掺杂物特征权重计算模型,并获取模型中的样本分类结果、特征信息以及分类模型中常见的评估指标,在上述特征模型计算的基础上,设计并实现了一个食品掺杂物可视分析系统.该系统不仅包含多个联动视图帮助用户更直观地理解食品掺杂物的特征,并支持用户通过迭代交互不断更新最优特征组合.最后,将该可视分析系统用于2010—2020年全国范围内24种食品类别的89202条不合格样本的掺杂物特征分析,实验结果证明该系统可以通过自动化的方式更加方便、直接地获取食品的掺杂物权重,增强掺杂物特征组合,为专业人员提供了对食品掺杂物更全面的见解.
The study of adulterants in food sampling data is very important for food safety early warning and risk prediction.In order to better explore the high-weight adulterants in food categories,we first design the food adulterant feature weight calculation model based on feature selection technology according to the idea of contrastive learning,and obtain sample classification results,feature information and common evaluation indicators in the model.Based on the above model,the visual analysis system for food adulterants is designed and implemented,which contains multiple linked views to help users understand the characteristics of food adulterants more intuitively and supports users to update the optimal feature combination through iterative interaction.Finally,the visual analysis system is used for the adulterant feature analysis of 89202 unqualified samples of 24 food types nationwide from 2010 to 2020.The experimental results prove that our system can obtain the adulterant weights of food more conveniently and directly in an automated way,enhance the combination of adulterant features,and provide professionals with more comprehensive insight of food adulterants.
作者
汤颖
盛祎琛
潘晶
周伟华
Tang Ying;Sheng Yichen;Pan Jing;Zhou Weihua(College of Computer Science and Technology,College of Software,Zhejiang University of Technology,Hangzhou 310023;Zhejiang University Library,Hangzhou 310058;International Research Center for Data Analytics and Management,Zhejiang University,Hangzhou 310058)
出处
《计算机辅助设计与图形学学报》
北大核心
2025年第2期229-242,共14页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金面上项目(61972355)
国家自然科学基金重大项目(72192820)
浙江省自然科学基金重点项目(LZ23F020010)。
关键词
特征选择
食品抽检数据
掺杂物
可视分析
关联分析
feature selection
food inspection data
adulterant
visual analysis
correlation analysis
