Article(id=1304388223640298140, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304388108988997783, articleNumber=null, orderNo=null, doi=10.7501/j.issn.0253-2670.2026.12.011, pmid=null, cstr=null, oa=null, hot=0, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1767283200000, receivedDateStr=2026-01-02, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1788919986877, onlineDateStr=2026-09-09, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1788919986877, onlineIssueDateStr=2026-09-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1788919986877, creator=13701087609, updateTime=1788919986877, updator=13701087609, issue=Issue{id=1304388108988997783, tenantId=1146029695717560320, journalId=1302319053441957962, year='2026', volume='57', issue='12', pageStart='4509', pageEnd='4948', issueExtLink='null', onlineDate='null', pubDate='1782576000000', pubDateStr='2026-06-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1788919959542, creator='13701087609', updateTime=1788923461082, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1304402795579330582, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304388108988997783, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1304402795579330583, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304388108988997783, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=4631, endPage=4643, ext={EN=ArticleExt(id=1304388223967453854, articleId=1304388223640298140, tenantId=1146029695717560320, journalId=1302319053441957962, language=EN, title=Research on waterjet processing mechanism of Orpiment zirconium spheres based on multimodal spectral fusion, columnId=null, journalTitle=Chinese Traditional and Herbal Drugs, columnName=null, runingTitle=null, highlight=null, articleAbstract=Objective To address the challenges of insufficient standardization in the processing of Orpiment (arsenic disulfide, As₂S₂) and the difficulty in controlling its toxicity, this study elucidated the interactions among “particle size-morphology-porosity-crystal defects-arsenic (As) release”. It analyzed the effects of different processing methods on elemental composition and microstructure, and established a rapid identification model based on multimodal spectral fusion. Methods A multi-dimensional “element-morphology-spectrum” data matrix was constructed. Inductively coupled plasma optical emission spectrometry (ICP-OES) was used to determine elemental content. Scanning electron microscopy (SEM) was employed to observe morphology and analyze surface porosity, combined with dissolution experiments to quantify As release. Furthermore, Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy (RS) data were integrated. Partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM) discrimination models were built using both low-level and mid-level data fusion strategies. Results The zirconia-ball water-grinding method reduced As content and increased sulfur (S) content in Orpiment. SEM analysis revealed that particles processed by this method exhibited a more concentrated size distribution, their morphology became more rounded and blunt, and surface porosity significantly increased, forming a hierarchical pore structure. In simulated gastric and intestinal fluids, As dissolution decreased by 72.8% and 81.4%, respectively, compared to dry crushing (P < 0.001). The SVM model based on mid-level spectral fusion achieved 100% classification accuracy. Conclusion The water-grinding method achieves detoxification by removing As³⁺ from Orpiment, regulating the particle morphology and pore structure of Orpiment particles, and enhancing the stability of Orpiment crystals. It establishes a multidimensional data matrix integrating elemental composition, microscopic morphology, pore structure, and spectral characteristics, providing efficient and reliable technical support for quality control of the mineral drug Orpiment., authors=XU Tianyi, HUANG Zijun, KANG Shuang, GE Songqi, MENG Lingbin, YANG Xinxin, YU Peng, authorsList=XU Tianyi, HUANG Zijun, KANG Shuang, GE Songqi, MENG Lingbin, YANG Xinxin, YU Peng, authorCompany=null, correspAuthors=null, 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=1304388223875179165, articleId=1304388223640298140, tenantId=1146029695717560320, journalId=1302319053441957962, language=CN, title=基于多模态数据融合的雌黄锆球水飞炮制机制研究, columnId=1304140189132149234, journalTitle=中草药, columnName=药剂与工艺, runingTitle=null, highlight=null, articleAbstract=目的 为解决雌黄Orpiment炮制工艺标准化不足及毒性控制难题，研究阐释“粒径-形貌-孔隙-晶体缺陷-As释放”的交互关系，解析不同炮制方法对雌黄元素组成、微观结构的影响，并建立多模态光谱融合快速鉴别模型。方法 构建雌黄“元素-形态-光谱”多维数据矩阵，采用电感耦合等离子体发射光谱仪（inductively coupled plasma optical emission spectrometer，ICP-OES）测定雌黄中元素含量，扫描电子显微镜（scanning electron microscope，SEM）观察雌黄形貌并分析其表面孔隙率，结合溶出实验量化其As释放量；同时集成傅里叶变换红外光谱（Fourier transform infrared spectroscopy，FTIR）与拉曼光谱（Raman spectroscopy，RS）数据，通过初级与中级融合策略，构建偏最小二乘法-判别分析（partial least squares-discrimination analysis，PLS-DA）与支持向量机（support victor machines，SVM）判别模型。结果 锆球水飞法使雌黄中As含量降低，S含量升高；SEM结果显示，锆球水飞后雌黄颗粒的粒径更加集中，其形貌经水飞后更为圆钝且表面孔隙率明显升高，形成多级孔结构；在人工胃液与肠液中，As溶出量较干法粉碎组分别降低72.8%与81.4%（P＜0.001）。基于光谱中级融合的SVM模型，分类准确率达100%。结论 水飞法通过去除雌黄中As³⁺、调控雌黄颗粒形貌与孔隙结构、增强雌黄晶体稳定性，从而实现减毒；构建了整合元素组成、微观形貌、孔隙结构及光谱特征的多维数据矩阵，为矿物药雌黄质控提供高效可靠的技术支持。, authors=徐天艺¹, 黄梓骏¹, 康爽¹, 葛松奇¹, 孟玲彬², 杨辛欣¹, 于澎¹, authorsList=徐天艺, 黄梓骏, 康爽, 葛松奇, 孟玲彬, 杨辛欣, 于澎, authorCompany=1 长春中医药大学,吉林 长春 130117;
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