Article(id=1304388067020796532, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304388047747969563, articleNumber=null, orderNo=null, doi=10.7501/j.issn.0253-2670.2026.10.007, pmid=null, cstr=null, oa=null, hot=0, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1766073600000, receivedDateStr=2025-12-19, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1788919949535, onlineDateStr=2026-09-09, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1788919949535, onlineIssueDateStr=2026-09-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1788919949535, creator=13701087609, updateTime=1788919949535, updator=13701087609, issue=Issue{id=1304388047747969563, tenantId=1146029695717560320, journalId=1302319053441957962, year='2026', volume='57', issue='10', pageStart='3685', pageEnd='4088', issueExtLink='null', onlineDate='null', pubDate='1779897600000', pubDateStr='2026-05-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1788919944940, creator='13701087609', updateTime=1788923403989, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1304402556332037104, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304388047747969563, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1304402556332037105, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304388047747969563, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3741, endPage=3752, ext={EN=ArticleExt(id=1304388067339563638, articleId=1304388067020796532, tenantId=1146029695717560320, journalId=1302319053441957962, language=EN, title=Optimization of ginger-steamed Amomi Fructus kernels process based on AHP-entropy weight method combined with Box-Behnken response surface methodology and fingerprint study before and after ginger processing, columnId=null, journalTitle=Chinese Traditional and Herbal Drugs, columnName=null, runingTitle=null, highlight=null, articleAbstract=Objective To optimize the processing technology for ginger-steamed Sharen (Amomi Fructus, AF) kernels and establish UPLC fingerprints before and after ginger-steaming. Methods Using the contents of volatile oil, bornyl acetate, extracts, 6-gingerol, vanillic acid, epicatechin, and quercitrin as evaluation indicators, the combined AHP-entropy weight method composite weighting method was employed to determine the weight coefficients of each indicator. Combined with the Box-Behnken design-response surface methodology (BBD-RSM), the effects of ginger juice dosage, moistening duration, steaming duration, and drying temperature on the ginger-steaming process of AF kernels were investigated to determine the optimal process parameters. UPLC fingerprints of AF kernels and ginger-steamed AF kernels were established and imported into the “Traditional Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System” (2012 edition) for pattern analysis. Chemometrics was used to compare the differences before and after processing. Results The optimal processing conditions for ginger-steamed AF kernels were determined as follows: ginger juice dosage 15%, moistening duration 3 h, steaming duration 38 min, drying temperature 60 ℃. Comparative analysis of the fingerprints before and after processing identified two differential components, which were ranked by variable importance projection (VIP) value as peak 6 (vanillic acid) > peak 8. Conclusion The optimized processing technology of ginger-steamed AF kernels is stable, reliable and reproducible. The method of UPLC fingerprint combined with chemometrics to screen quality difference markers before and after processing is simple and convenient, which can provide a scientific reference for the quality control and evaluation of ginger-steamed AF kernels., authors=CHEN Wenyi, MA Enyao, ZHOU Jinsong, YANG Shihui, WANG Jinyu, authorsList=CHEN Wenyi, MA Enyao, ZHOU Jinsong, YANG Shihui, WANG Jinyu, 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=1304388067264066165, articleId=1304388067020796532, tenantId=1146029695717560320, journalId=1302319053441957962, language=CN, title=基于层次分析-熵权法与Box-Behnken设计-响应面法优化姜蒸砂仁米工艺及姜制前后指纹图谱研究, columnId=1304140189132149234, journalTitle=中草药, columnName=药剂与工艺, runingTitle=null, highlight=null, articleAbstract=目的 优化姜蒸砂仁Amomi Fructus米炮制工艺，建立砂仁米姜蒸前后的指纹图谱。方法 以挥发油、乙酸龙脑酯、浸出物、6-姜辣素、香草酸、表儿茶素、槲皮苷含量为评价指标，采用层次分析（analytic hierarchy process，AHP）-熵权法复合加权法确定各指标权重系数，再结合Box-Behnken设计-响应面法（Box-Behnken design-response surface methodology，BBD-RSM）考察姜汁用量、闷润时长、蒸制时长、干燥温度对姜蒸砂仁米工艺的影响，以确定最佳工艺参数；建立砂仁米与姜蒸砂仁米UPLC指纹图谱，导入《中药色谱指纹图谱相似度评价系统》（2012年版）进行图谱分析，结合化学计量学对比砂仁米炮制前后的差异性。结果 姜蒸砂仁米最优炮制工艺条件为姜汁用量15%，闷润时长3 h，蒸制时长38 min，干燥温度60 ℃；砂仁米炮制前后指纹图谱对比分析共筛选出2个差异成分，根据变量重要性投影（variable importance projection，VIP）值排序为峰6（香草酸）＞峰8。结论 优化得到的姜蒸砂仁米炮制工艺稳定可靠，重复性好；UPLC指纹图谱结合化学计量学分析，筛选砂仁米炮制前后质量差异标志物，方法简便，可为姜蒸砂仁米质量控制和品质评价提供科学参考。, authors=陈文意¹, 马恩耀^2,3, 周劲松², 杨诗慧², 汪金玉¹, authorsList=陈文意, 马恩耀, 周劲松, 杨诗慧, 汪金玉, authorCompany=1 广州中医药大学中药学院, 广东广州 510000;
2 广东汉潮中药科技有限公司, 广东广州 510360;
3 广药采芝林(梅州)药业有限公司, 广东梅州 514000, correspAuthors=马恩耀, authorNote=陈文意: 陈文意(2001—),女,硕士研究生,研究方向为中药炮制机制与饮片质量控制研究。E-mail:1310763360@qq.com, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=RmKrIbt+07huOcvur8Jybg==, pdfFileSize=1511004, 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=国家重点研发计划“乡村产业共性关键技术研发与集成应用”重点专项-南药产业关键技术研究与应用示范 (2022YFD1600300); 广州市科技计划项目“基于人工智能辨识高分辨活性轮廓分析的岭南常用中药质量控制关键技术研究与应用” (SL2024B03J00626))}, authors=null, keywords=[Keyword(id=1304401278939984260, tenantId=1146029695717560320, journalId=1302319053441957962, articleId=1304388067020796532, language=CN, orderNo=1, keyword=姜蒸砂仁米), Keyword(id=1304401279007093125, tenantId=1146029695717560320, journalId=1302319053441957962, 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