Article(id=1314918293081784787, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314918247384834796, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740499200000, receivedDateStr=2025-02-26, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791430551070, onlineDateStr=2026-10-08, pubDate=1760889600000, pubDateStr=2025-10-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791430551070, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791430551070, creator=13701087609, updateTime=1791430551070, updator=13701087609, issue=Issue{id=1314918247384834796, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='10', pageStart='5329', pageEnd='5920', issueExtLink='null', onlineDate='null', pubDate='1760889600000', pubDateStr='2025-10-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791430540175, creator='13701087609', updateTime=1791431660009, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314922944367784036, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314918247384834796, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314922944367784037, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314918247384834796, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=5585, endPage=5595, ext={EN=ArticleExt(id=1314918293392163284, articleId=1314918293081784787, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Identification of XRF-XRD coupled spectral fingerprint characteristics of typical solid waste, columnId=1240689598498656298, journalTitle=China Environmental Science, columnName=Solid Waste, runingTitle=null, highlight=null, articleAbstract=

This study conducted X-ray fluorescence (XRF) and X-ray diffraction (XRD) experiments on 10 common types of solid waste. By employing non-parametric tests, the XRF and XRD fingerprint characteristics of solid waste were extracted. A normalized approach was adopted to combine the two fingerprint characteristics to form XRF-XRD coupled spectral fingerprint characteristics. A machine learning classification model was trained using the three fingerprint characteristics as a dataset. It was found that the random forest model exhibited the best classification recognition performance, with classification accuracy, recall rate, and precision rate in the validation set reaching 98.0%, 97.5%, and 97.6%, respectively. This confirmed that the classification performance of coupled spectral fingerprint characteristics was superior to that of single spectral fingerprint characteristics. A method for extracting multi-dimensional fingerprint characteristics of solid waste using XRF-XRD was developed, laying a methodological foundation for the identification of fingerprint characteristics of solid waste.

, authors=Rui-xiao HUANG, Yong-qi LU, Zhi-min ZHENG, Yu-fei YANG, Jin-zhong YANG^*, Qi-fei HUANG, authorsList=Rui-xiao HUANG, Yong-qi LU, Zhi-min ZHENG, Yu-fei YANG, Jin-zhong YANG, Qi-fei HUANG, authorCompany=null, correspAuthors=Jin-zhong YANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314918293803205077, articleId=1314918293081784787, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=典型固体废物的XRF-XRD耦合光谱指纹特征识别, columnId=1240689598737731645, journalTitle=中国环境科学, columnName=固体废物, runingTitle=null, highlight=null, articleAbstract=

针对常见的10类固体废物开展X射线荧光（XRF）与X射线衍射（XRD）实验，通过非参数检验提取固体废物的XRF与XRD指纹特征，并采用归一化的方式结合两种指纹特征形成XRF-XRD耦合光谱指纹特征，以三种指纹特征为数据集训练机器学习分类模型，发现随机森林模型分类识别效果最好，验证集分类精确度、召回率、准确率分别达98.0%、97.5%、97.6%，证实耦合光谱指纹特征分类效果优于单一光谱指纹特征．形成了固体废物XRF-XRD多维指纹特征提取方法，为固体废物指纹特征识别奠定方法基础．

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黄瑞潇（2000-），男，山东济南人，中国环境科学研究院硕士研究生，主要研究方向为固体废物指纹特征时别．发表论文1篇．huangruixiao22@mails.ucas.ac.cn.

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黄瑞潇（2000-），男，山东济南人，中国环境科学研究院硕士研究生，主要研究方向为固体废物指纹特征时别．发表论文1篇．huangruixiao22@mails.ucas.ac.cn.

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