Article(id=1304388065347265432, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304388047747969563, articleNumber=null, orderNo=null, doi=10.7501/j.issn.0253-2670.2026.10.008, pmid=null, cstr=null, oa=null, hot=0, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1765123200000, receivedDateStr=2025-12-08, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1788919949136, onlineDateStr=2026-09-09, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1788919949136, onlineIssueDateStr=2026-09-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1788919949136, creator=13701087609, updateTime=1788919949136, 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=3753, endPage=3763, ext={EN=ArticleExt(id=1304388065670226842, articleId=1304388065347265432, tenantId=1146029695717560320, journalId=1302319053441957962, language=EN, title=Optimization of processing technology for Mume Fructus flesh based on entropy weight method combined with analytic hierarchy process and back propagation neural network, columnId=null, journalTitle=Chinese Traditional and Herbal Drugs, columnName=null, runingTitle=null, highlight=null, articleAbstract=Objective To optimize the best processing technology of Wumei (Mume Fructus , MF) flesh and determine the best processing parameters. Methods The contents of 5-hydroxymethylfurfural (5-HMF), neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid and caffeic acid were determined by HPLC. The content of polysaccharide in MF flesh was determined by ultraviolet-visible spectrophotometer. The content of each chemical component, the content of water-soluble extracts and the appearance traits were selected as the evaluation indexes, and the weight coefficient of each evaluation index was determined by analytic hierarchy process (AHP)-entropy weight method hybrid weighting method. The L9 (34 ) orthogonal test was carried out with water addition, moistening time and steaming time as factors. Back propagation neural network (BPNN) was used to predict and verify the optimal processing parameters of MF flesh. Results The BPNN optimization process was superior to the orthogonal screening process, and the prediction results were stable and reliable. The composite score was the highest, which was 83.26. The best steaming process of MF flesh was 22.5 kg of water per 100 kg of MF flesh, moistening for 60 min, and steaming for 30 min. Conclusion The determined processing technology of MF flesh is scientific and feasible, which can provide scientific data support for the industrial production of decoction pieces, and also provide a theoretical basis for ensuring the quality and clinical application of MF flesh., authors=JIN Yake, ZHANG Ying, KE Xin, GAO Rong, YUAN Xinru, QIAO Chanchan, LI Ting, CUAN Yunzhao, LING Ying, SONG Tiantian, DUAN Xi, SONG Xiao, authorsList=JIN Yake, ZHANG Ying, KE Xin, GAO Rong, YUAN Xinru, QIAO Chanchan, LI Ting, CUAN Yunzhao, LING Ying, SONG Tiantian, DUAN Xi, SONG Xiao, 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=1304388065586340761, articleId=1304388065347265432, tenantId=1146029695717560320, journalId=1302319053441957962, language=CN, title=基于熵权法结合层次分析法和反向传播神经网络优选乌梅肉炮制工艺, columnId=1304140189132149234, journalTitle=中草药, columnName=药剂与工艺, runingTitle=null, highlight=null, articleAbstract=目的 优化乌梅Mume Fructus 肉的炮制工艺，确定最佳炮制工艺参数。方法 采用HPLC法测定乌梅肉中指标成分5-羟甲基呋喃甲醛（5-hydroxymethylfurfural，5-HMF）、新绿原酸、绿原酸、隐绿原酸、咖啡酸的含量；采用紫外-可见分光光度计测定乌梅肉中多糖的含量；选择各化学成分的含量、水溶性浸出物含量以及外观性状作为评价指标，通过层次分析（analytic hierarchy process，AHP）-熵权法混合加权法确定各评价指标的权重系数；选择加水量、闷润时间、蒸制时间为因素进行L₉(3⁴)正交试验，采用反向传播神经网络（back propagation neural network，BPNN）预测乌梅肉的最优炮制工艺参数并验证。结果 BPNN优化工艺优于正交筛选工艺，且预测结果稳定可靠，综合评分最高，为83.26，乌梅肉的最佳蒸制工艺为100 kg乌梅加22.5 kg水，闷润60 min，蒸制30 min。结论 所确定的乌梅肉炮制工艺切实可行，能够为饮片的工业化生产提供科学的数据支持，也可为保障乌梅肉的质量及临床应用提供理论依据。, authors=靳雅珂1 , 张颖1 , 柯鑫1 , 高蓉1 , 袁心如1 , 乔婵婵1 , 李婷1 , 炊云钊1 , 令颖1 , 宋甜甜1 , 段玺2 , 宋逍1,3 , authorsList=靳雅珂, 张颖, 柯鑫, 高蓉, 袁心如, 乔婵婵, 李婷, 炊云钊, 令颖, 宋甜甜, 段玺, 宋逍, 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Guchang Zhixie Pill: A comprehensive review of the clinical applications, toxicological profile, and quality markers [J]. Curr Top Med Chem , 2025, 25(20): 2399-2416.et al . Traceability of Mume Fructus pulp origin, processing method, authenticity based on flash GC E-nose and FT-NIRS technology combined with machine learning and data fusion strategy [J]. Microchem J , 2025, 217: 114817.et al . Evaluation of quality attributes of different parts of Poria cocos during stress sweating process based on AHP-EWM and RSM [J]. Ind Crops Prod , 2024, 210: 118047.et al . Quantitative analysis of adulterated crop flours in Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) using near infrared spectrometry combined with back-propagation neural network algorithm [J]. J Future Foods , 2026, 6(5): 818-830.et al . Anti-interference detection of mixed NOX via In2 O3 -based sensor array combining with neural network model at room temperature [J]. J Hazard Mater , 2024, 463: 132857.et al . Saposhnikovia divaricata -Fructus Mume herb pair alleviates airway inflammation in asthma by inhibiting IL-17 secretion from γδT17 cells via the Notch1 signaling pathway [J]. J Ethnopharmacol , 2026, 360: 121107.et al . Effects of Mume Fructus extract on smoking-induced cough and TNBS-induced colitis in rats and its underlying mechanisms based on gut-lung body fluid metabolism [J]. J Ethnopharmacol , 2026, 355(Pt A): 120539.et al . Chemical composition and antioxidant activity of Polygonatum kingianum processed by the traditional method of “nine cycles of steaming and sun-drying” [J]. Food Chem X , 2024, 22: 101292.et al . An integrated strategy of secondary metabolomics and glycomics to investigate multi-component variations in wine-processing of medicinal herbs and functional foods: A case study on Fructus Corni [J]. Arab J Chem , 2023, 16(3): 104525.
中草药
|药剂与工艺
2026
, 57
(10) :
3753
-3763
基于熵权法结合层次分析法和反向传播神经网络优选乌梅肉炮制工艺
全屏
靳雅珂1 , 张颖1 , 柯鑫1 , 高蓉1 , 袁心如1 , 乔婵婵1 , 李婷1 , 炊云钊1 , 令颖1 , 宋甜甜1 , 段玺2 , 宋逍1,3
作者信息
1 陕西中医药大学, 陕西 咸阳 712046;
通讯作者:
宋逍
作者简介:
靳雅珂: 靳雅珂,硕士研究生,研究方向为中药新制剂与新剂型研究。E-mail:1944271485@qq.com
Optimization of processing technology for Mume Fructus flesh based on entropy weight method combined with analytic hierarchy process and back propagation neural network
JIN Yake, ZHANG Ying, KE Xin, GAO Rong, YUAN Xinru, QIAO Chanchan, LI Ting, CUAN Yunzhao, LING Ying, SONG Tiantian, DUAN Xi, SONG Xiao
Affiliations
doi: 10.7501/j.issn.0253-2670.2026.10.008
文章导航
目的 优化乌梅Mume Fructus 肉的炮制工艺，确定最佳炮制工艺参数。方法 采用HPLC法测定乌梅肉中指标成分5-羟甲基呋喃甲醛（5-hydroxymethylfurfural，5-HMF）、新绿原酸、绿原酸、隐绿原酸、咖啡酸的含量；采用紫外-可见分光光度计测定乌梅肉中多糖的含量；选择各化学成分的含量、水溶性浸出物含量以及外观性状作为评价指标，通过层次分析（analytic hierarchy process，AHP）-熵权法混合加权法确定各评价指标的权重系数；选择加水量、闷润时间、蒸制时间为因素进行L₉(3⁴)正交试验，采用反向传播神经网络（back propagation neural network，BPNN）预测乌梅肉的最优炮制工艺参数并验证。结果 BPNN优化工艺优于正交筛选工艺，且预测结果稳定可靠，综合评分最高，为83.26，乌梅肉的最佳蒸制工艺为100 kg乌梅加22.5 kg水，闷润60 min，蒸制30 min。结论 所确定的乌梅肉炮制工艺切实可行，能够为饮片的工业化生产提供科学的数据支持，也可为保障乌梅肉的质量及临床应用提供理论依据。
熵权法
/
层次分析法
/
乌梅肉
/
反向传播神经网络
/
炮制工艺
/
正交试验
Objective To optimize the best processing technology of Wumei (Mume Fructus , MF) flesh and determine the best processing parameters. Methods The contents of 5-hydroxymethylfurfural (5-HMF), neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid and caffeic acid were determined by HPLC. The content of polysaccharide in MF flesh was determined by ultraviolet-visible spectrophotometer. The content of each chemical component, the content of water-soluble extracts and the appearance traits were selected as the evaluation indexes, and the weight coefficient of each evaluation index was determined by analytic hierarchy process (AHP)-entropy weight method hybrid weighting method. The L9 (34 ) orthogonal test was carried out with water addition, moistening time and steaming time as factors. Back propagation neural network (BPNN) was used to predict and verify the optimal processing parameters of MF flesh. Results The BPNN optimization process was superior to the orthogonal screening process, and the prediction results were stable and reliable. The composite score was the highest, which was 83.26. The best steaming process of MF flesh was 22.5 kg of water per 100 kg of MF flesh, moistening for 60 min, and steaming for 30 min. Conclusion The determined processing technology of MF flesh is scientific and feasible, which can provide scientific data support for the industrial production of decoction pieces, and also provide a theoretical basis for ensuring the quality and clinical application of MF flesh.
entropy weight method
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analytic hierarchy process
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Mume Fructus flesh
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back propagation neural network
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processing technology
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orthogonal test
靳雅珂, 张颖, 柯鑫, 高蓉, 袁心如, 乔婵婵, 李婷, 炊云钊, 令颖, 宋甜甜, 段玺, 宋逍.
基于熵权法结合层次分析法和反向传播神经网络优选乌梅肉炮制工艺.
中草药,
2026
, 57
(10)
: 3753
-3763
.
DOI: 10.7501/j.issn.0253-2670.2026.10.008
JIN Yake, ZHANG Ying, KE Xin, GAO Rong, YUAN Xinru, QIAO Chanchan, LI Ting, CUAN Yunzhao, LING Ying, SONG Tiantian, DUAN Xi, SONG Xiao.
Optimization of processing technology for Mume Fructus flesh based on entropy weight method combined with analytic hierarchy process and back propagation neural network[J].
Chinese Traditional and Herbal Drugs ,
2026
, 57
(10)
: 3753
-3763
.
DOI: 10.7501/j.issn.0253-2670.2026.10.008
国家重点研发计划项目 (2019YFC1711204); 陕西省教育厅项目 (21JC011); 陕西省中医药管理局项目 (2021-04-ZZ-007); 咸阳市科学技术局项目 (L2024-QCY-ZYYJJQ-X50)
参考文献
引证文献
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2026年第57卷第10期
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doi: 10.7501/j.issn.0253-2670.2026.10.008
接收时间：2025-12-08
首发时间：2026-09-09
https://castjournals.cast.org.cn/joweb/zcy/CN/10.7501/j.issn.0253-2670.2026.10.008
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2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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