我国47种中药材中重金属含量分析与数据挖掘
投稿时间:2023-10-16     点此下载全文
引用本文:杨乾巍,杨迪,张良,杜光映,张明星,何愿子,唐桐桐,赵雅秋.我国47种中药材中重金属含量分析与数据挖掘[J].中国现代中药,2024,26(4):625-634
DOI:10.13313/j.issn.1673-4890.20231016005
摘要点击次数: 268
全文下载次数: 0
                       
作者中文名作者英文名单位中文名单位英文名E-Mail
杨乾巍 YANG Qian-wei 贵州中医药大学,贵州 贵阳 550025 Guizhou University of Traditional Chinese Medicine, Guiyang 550025, China  
杨迪 YANG Di 贵州中医药大学,贵州 贵阳 550025 Guizhou University of Traditional Chinese Medicine, Guiyang 550025, China  
张良 ZHANG Liang 贵州中医药大学,贵州 贵阳 550025 Guizhou University of Traditional Chinese Medicine, Guiyang 550025, China  
杜光映* DU Guang-ying 贵州中医药大学,贵州 贵阳 550025 Guizhou University of Traditional Chinese Medicine, Guiyang 550025, China  
张明星 ZHANG Ming-xing 贵州中医药大学,贵州 贵阳 550025 Guizhou University of Traditional Chinese Medicine, Guiyang 550025, China  
何愿子 HE Yuan-zi 贵州中医药大学,贵州 贵阳 550025 Guizhou University of Traditional Chinese Medicine, Guiyang 550025, China  
唐桐桐 TANG Tong-tong 贵州中医药大学,贵州 贵阳 550025 Guizhou University of Traditional Chinese Medicine, Guiyang 550025, China  
赵雅秋* ZHAO Ya-qiu 中国中医科学院 中药资源中心,北京 100700 National Resource Center for Chinese Meteria Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China  
基金项目:国家自然科学基金项目(82160717);贵州省科技计划项目(黔科合支撑〔2020〕4Y073号);中央级公益性科研院所基本科研业务费专项(ZZXT202202)
中文摘要:目的 分析中药材中重金属的富集特征,预测不同中药材入药部位重金属的含量,为进一步解析中药材重金属富集机制及制定相关标准提供依据。方法 从1998—2023年发表的238篇文献中筛选出47种中药材,采用R语言和Python进行数据预处理、单变量和多变量分析,对5种重金属含量进行数据挖掘。依据国际标准化组织(ISO)国际标准《中医药-中药材重金属限量》评估铅、汞、砷、镉含量,并参考《药用植物及制剂外经贸绿色行业标准》对铜含量进行评估,采用随机森林回归模型预测不同入药部位的5种重金属含量。结果 47种中药材中重金属含量为铜>铅>汞>砷>镉,超标率为铜>镉>汞>砷>铅。入药部位为干燥地上部分、干燥菌核、干燥果穗和干燥树皮的中药材中重金属超标情况较为严重。除北京、重庆、广西、黑龙江、吉林、辽宁和新疆的中药材中未发现重金属超标外,全国其他地区均存在中药材中重金属超标现象。随机森林回归模型在预测不同入药部位镉、汞和砷含量时显示出较高的准确性。结论 中药材中重金属污染使中医药产业发展存在潜在风险,需要加强对中药材中重金属富集机制的研究。机器学习技术在中药材重金属的数据挖掘和分析领域具有较好的应用潜力,可为今后中药材重金属富集机制研究提供新思路。
中文关键词:中药材  重金属  统计分析  数据挖掘  机器学习
 
Heavy Metals in 47 Chinese Medicinal Materials: A Data Mining
Abstract:Objective To analyze the enrichment characteristics of heavy metals in Chinese medicinal materials and predict their contents in different parts of the medicinal plants, to provide a basis for further analyzing the enrichment mechanism of heavy metals in Chinese medicinal materials and formulating relevant standards.Methods Forty-seven Chinese medicinal materials were screened out from 238 articles published from 1998 to 2023. Data pre-processing, univariate analysis and multivariate analysis were carried out using R language and Python software to mine the data of five heavy metals. The contents of lead (Pb), mercury (Hg), arsenic (As) and cadmium (Cd) were assessed according to the Traditional Chinese Medicine Determination of Heavy Metals in Herbal Medicines Used in Traditional Chinese Medicine in the International Organization for Standardization (ISO), and the content of copper (Cu) was assessed with reference to the Green Standards of Medicinal Plants and Preparations for Foreign Trade and Economy. Random forest regression was used to predict the contents of the five heavy metals in different parts of the medicinal plants.Results The contents of heavy metals in the 47 Chinese medicinal materials were in the order of Cu>Pb>Hg>As>Cd, and the exceedance rate was sequenced as Cu>Cd>Hg>As>Pb. Heavy metals were more observed in herbs with dry aboveground part, sclerotium, fruit spike and bark. Except for Beijing, Chongqing, Guangxi, Heilongjiang, Jilin, Liaoning and Xinjiang, where no exceedance of heavy metals has been found, the phenomenon of exceedance of heavy metals in Chinese herbal medicines exists in all other regions of the country. The random forest regression model showed high accuracy in predicting the levels of cadmium, mercury and arsenic in different entry sites.Conclusion Heavy metal pollution in Chinese medicinal materials poses potential risks to the development of traditional Chinese medicine industry, and it is necessary to strengthen the research on the enrichment mechanism of heavy metals in Chinese medicinal materials. Machine learning showed good application potential in the analysis of heavy metals by data mining in Chinese medicinal materials, providing new ideas for future research on the enrichment mechanism of heavy metals in Chinese medicinal materials.
keywords:Chinese medicinal materials  heavy metals  statistic analysis  data mining  machine learning
查看全文   查看/发表评论  下载PDF阅读器