Article(id=1200394149213107042, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1200394147019477416, articleNumber=null, orderNo=null, doi=10.16438/j.0513-4870.2023-1393, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1702483200000, receivedDateStr=2023-12-14, revisedDate=1713369600000, revisedDateStr=2024-04-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1764125867416, onlineDateStr=2025-11-26, pubDate=1720713600000, pubDateStr=2024-07-12, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1764125867416, onlineIssueDateStr=2025-11-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1764125867416, creator=13701087609, updateTime=1764125867416, updator=13701087609, issue=Issue{id=1200394147019477416, tenantId=1146029695717560320, journalId=1189982191388893191, year='2024', volume='59', issue='7', pageStart='1897', pageEnd='2182', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1764125866894, creator=13701087609, updateTime=1764225115484, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1200810425920115296, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1200394147019477416, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1200810425920115297, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1200394147019477416, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2117, endPage=2125, ext={EN=ArticleExt(id=1200394149770949477, articleId=1200394149213107042, tenantId=1146029695717560320, journalId=1189982191388893191, language=EN, title=Exploring correlation between appearance characters and internal quality of Phellodendri Chinensis Cortex slice based on the theory of "quality evaluation through morphological identification", columnId=1190335348761793317, journalTitle=Acta Pharmaceutica Sinica, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=

Phellodendri Chinensis Cortex (PCC) featured with thick cortex and bright-yellow is considered to be of high quality according to traditional appearance traits evaluation mode. However, the correlation between appearance traits and internal quality of PCC has not been scientifically revealed. Here, based on the theory of "Quality Evaluation Through Morphological Identification", the correlation of both sides was studied systematically. Firstly, the thickness of PCC slices was measured by vernier calipers for classification, and the colour of PCC slice was estimated by naked eyes and automatic colorimeter and classified. Secondly, high performance liquid chromatography (HPLC) was used to establish fingerprint chromatogram containing 12 characteristic peaks, and the contents of moisture and ethanolic extractive were determined as well. The correlation among the appearance traits of PCC slice (including the thickness and the spatial values of colour of PCC slice powder: L*Lightness, a*Red-Green, b*Yellow-Blue) and peak areas of 12 characteristic peaks, contents of moisture and extractive were explored through multivariate statistical analysis tools, including Pearson's correlation analysis, principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (PLS-DA) and analysis of variance (ANOVA). Association analysis results showed the morphological parameters (thickness and color) are significantly correlated with 2 kinds of principle chemical constituents (alkaloids and phenolic acids), among them, 5 major constituents including 2 isoquinoline alkaloids and 3 feruloylquinic acids were simultaneously quantitatively analyzed under the same chromatographic condition for establishing a correspondence between appearance traits and content of internal substances, and the sum of the contents of the five components was significantly higher in the thickness ≥ 4 mm (13.15% ± 2.25%) and bright yellow samples (12.14% ± 2.00%) than that in the thickness < 4 mm (10.89% ± 1.41%) and dark yellow samples (10.32% ± 2.41%). The research results confirm that thickness and colour, two main appearance parameters of PCC slice, can be used as the indicators for evaluating the quality PCC slice, which to some extent interprets the scientific connotation of traditional evaluation theory of "thick and yellow are better". At the same time, the results provided substantial data and foundation for the establishment of commodity grade standards of PCC slice.

, correspAuthors=Li-hua GU, Zheng-tao WANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2024 Acta Pharmaceutica Sinica. All rights reserved., 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, authorCompany=null, fund=null, authors=null, authorsList=Mei-long LU, Qian ZHANG, Li-hua GU, Li YANG, Zheng-tao WANG), CN=ArticleExt(id=1200394153352885146, articleId=1200394149213107042, tenantId=1146029695717560320, journalId=1189982191388893191, language=CN, title=基于“辨状论质”理论研究黄柏饮片外观性状与内在质量相关性, columnId=1190335348896011050, journalTitle=药学学报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=

黄柏传统评价以“肉厚色黄者”为佳, 但该性状特征的科学内涵尚未被阐释。本研究基于“辨状论质”理论, 首先采用游标卡尺和全自动测色色差仪对黄柏饮片厚度和粉末颜色进行量化, 同时采用高效液相色谱(HPLC) 法建立黄柏饮片特征图谱, 共表征12个特征峰, 将外观性状指标与水分、醇溶性浸出物以及特征成分进行Pearson相关性分析、主成分分析(PCA)、正交偏最小二乘判别分析(OPLS-DA) 和方差分析(ANOVA), 探讨黄柏饮片外观性状(包括厚度和粉末三色空间值L*亮度、a*红绿、b*黄蓝) 与内在成分及相对含量之间的相关性。结果表明黄柏的外观性状特征(厚度和颜色) 与黄柏中的化学成分(生物碱和酚酸) 的含量呈显著相关性。基于分析结果, 对5种代表性成分进行含量测定, 5种成分含量总和在厚度≥ 4 mm (13.15% ± 2.25%) 和鲜黄色样品(12.14% ± 2.00%) 中的含量显著高于厚度 < 4 mm (10.89% ± 1.41%) 和暗黄色黄柏样品(10.32% ± 2.41%), 将外观性状与成分含量建立“性状-质量-含量”对应关系。研究结果证实黄柏饮片厚度和颜色2个主要外观性状指标可作为判断其品质的依据, 一定程度上诠释了黄柏传统评价“肉厚色黄者为佳”的科学内涵, 同时为黄柏饮片商品等级标准的建立积累了数据和基础。

, correspAuthors=谷丽华, 王峥涛, authorNote=null, correspAuthorsNote=
*谷丽华, E-mail: ;
王峥涛, E-mail:
, copyrightStatement=版权所有©《药学学报》编辑部2024, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=eoWyN1OiCN6a7uT6JhtYbg==, magXml=fJ11sUC5h+DHbnymw0Ff3Q==, pdfUrl=null, pdf=IYg/wQDwwqPTkWZ8Tnatdw==, pdfFileSize=2580231, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=RHsFJ7QH9nzn62ChJn6KSA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=d5IlxUf8iEv0B2kZj3UPsA==, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=陆美龙, 张倩, 谷丽华, 杨莉, 王峥涛)}, authors=[Author(id=1200470891411919511, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1200394149213107042, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1200470891546137247, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1200394149213107042, authorId=1200470891411919511, language=EN, stringName=Mei-long LU, firstName=Mei-long, middleName=null, lastName=LU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, 2, address=1. The MOE Key Laboratory for Standardization of Chinese Medicines and the SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
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Phellodendrine promotes autophagy by regulating the AMPK/mTOR pathway and treats ulcerative colitis [J]. J Cell Mol Med, 2021, 25: 5707-5720., articleTitle=null, refAbstract=null), Reference(id=1200470902476493254, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1200394149213107042, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[31], rfOrder=30, authorNames=null, journalName=null, refType=null, unstructuredReference=Cui H, Cai Y, Wang L, et al. Berberine regulates Treg/Th17 balance to treat ulcerative colitis through modulating the gut microbiota in the colon [J]. Front Pharmacol, 2018, 9: 571-587., articleTitle=null, refAbstract=null), Reference(id=1200470902598128075, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1200394149213107042, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[32], rfOrder=31, authorNames=null, journalName=null, refType=null, unstructuredReference=Li L. Study on the Relative Mechanism of Phellodendrine Protective Effect on ROS-mediated Oxidative Stress (黄柏碱抗ROS介导的氧化应激相关机制研究) [D]. Chongqing: Southwest University, 2017., articleTitle=null, refAbstract=null), Reference(id=1200470902736540114, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1200394149213107042, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[33], rfOrder=32, authorNames=null, journalName=null, refType=null, unstructuredReference=Li L, Huang T, Tian C, et al. The defensive effect of phellodendrine against AAPH-induced oxidative stress through regulating the AKT/NF-kappaB pathway in zebrafish embryos [J]. Life Sci, 2016, 157: 97-106., articleTitle=null, refAbstract=null)], funds=[Fund(id=1200470897829204109, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1200394149213107042, awardId=ZY(2021-2023)-0215, language=CN, fundingSource=上海市“三年行动”计划项目(ZY(2021-2023)-0215), fundOrder=null, country=null), Fund(id=1200470897967616148, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1200394149213107042, awardId=21DZ2202100, language=CN, fundingSource=上海市技术标准项目(21DZ2202100), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1200470890942157434, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1200394149213107042, xref=null, ext=[AuthorCompanyExt(id=1200470890963128958, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1200394149213107042, companyId=1200470890942157434, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1. 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No.Collect placeCollect timeNo.Collect placeCollect time
HB-1Dujiangyan, Sichuan2021.12HB-25Shanghai Kangqiao Company2023.4.19
HB-2Dujiangyan, Sichuan2021.12HB-26Shanghai Kangqiao Company2023.4.19
HB-3Dujiangyan, Sichuan2021.12HB-27Shanghai Kangqiao Company2023.4.19
HB-4Dujiangyan, Sichuan2021.12HB-28Shanghai Kangqiao Company2023.4.19
HB-5Dujiangyan, Sichuan2021.12HB-29Shanghai Kangqiao Company2023.4.19
HB-6Dujiangyan, Sichuan2021.12HB-30Bozhou, Anhui2023.4.4
HB-7Dujiangyan, Sichuan2021.12HB-31Bozhou, Anhui2023.4.4
HB-8Dujiangyan, Sichuan2021.12HB-32Bozhou, Anhui2022.9.28
HB-9Dujiangyan, Sichuan2021.12HB-33Bozhou, Anhui2022.9.28
HB-10Dujiangyan, Sichuan2021.12HB-34Bozhou, Anhui2022.9.28
HB-11Dujiangyan, Sichuan2021.12HB-35Bozhou, Anhui2022.9.28
HB-12Dujiangyan, Sichuan2021.12HB-36Bozhou, Anhui2023.4.4
HB-13Dujiangyan, Sichuan2021.12HB-37Bozhou, Anhui2023.4.4
HB-14Shanghai Kangqiao Company2022.8.18HB-38Bozhou, Anhui2023.4.4
HB-15Shanghai Kangqiao Company2022.8.18HB-39Bozhou, Anhui2023.4.4
HB-16Shanghai Kangqiao Company2022.8.18HB-40Bozhou, Anhui2023.4.4
HB-17Shanghai Kangqiao Company2022.8.18HB-41Bozhou, Anhui2023.4.4
HB-18Shanghai Kangqiao Company2022.8.18HB-42Bozhou, Anhui2023.4.4
HB-19Shanghai Kangqiao Company2023.4.19HB-43Bozhou, Anhui2023.4.4
HB-20Shanghai Kangqiao Company2023.4.19HB-44Bozhou, Anhui2023.4.4
HB-21Shanghai Kangqiao Company2023.4.19HB-45Bozhou, Anhui2023.4.4
HB-22Shanghai Kangqiao Company2023.4.19HB-46Bozhou, Anhui2023.4.4
HB-23Shanghai Kangqiao Company2023.4.19HB-47Bozhou, Anhui2023.4.4
HB-24Shanghai Kangqiao Company2023.4.19HB-48Bozhou, Anhui2023.4.4
), ArticleFig(id=1200470896772239425, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1200394149213107042, language=CN, label=Table 1, caption=

The information of Phellodendri Chinensis Cortex slice samples

, figureFileSmall=null, figureFileBig=null, tableContent=
No.Collect placeCollect timeNo.Collect placeCollect time
HB-1Dujiangyan, Sichuan2021.12HB-25Shanghai Kangqiao Company2023.4.19
HB-2Dujiangyan, Sichuan2021.12HB-26Shanghai Kangqiao Company2023.4.19
HB-3Dujiangyan, Sichuan2021.12HB-27Shanghai Kangqiao Company2023.4.19
HB-4Dujiangyan, Sichuan2021.12HB-28Shanghai Kangqiao Company2023.4.19
HB-5Dujiangyan, Sichuan2021.12HB-29Shanghai Kangqiao Company2023.4.19
HB-6Dujiangyan, Sichuan2021.12HB-30Bozhou, Anhui2023.4.4
HB-7Dujiangyan, Sichuan2021.12HB-31Bozhou, Anhui2023.4.4
HB-8Dujiangyan, Sichuan2021.12HB-32Bozhou, Anhui2022.9.28
HB-9Dujiangyan, Sichuan2021.12HB-33Bozhou, Anhui2022.9.28
HB-10Dujiangyan, Sichuan2021.12HB-34Bozhou, Anhui2022.9.28
HB-11Dujiangyan, Sichuan2021.12HB-35Bozhou, Anhui2022.9.28
HB-12Dujiangyan, Sichuan2021.12HB-36Bozhou, Anhui2023.4.4
HB-13Dujiangyan, Sichuan2021.12HB-37Bozhou, Anhui2023.4.4
HB-14Shanghai Kangqiao Company2022.8.18HB-38Bozhou, Anhui2023.4.4
HB-15Shanghai Kangqiao Company2022.8.18HB-39Bozhou, Anhui2023.4.4
HB-16Shanghai Kangqiao Company2022.8.18HB-40Bozhou, Anhui2023.4.4
HB-17Shanghai Kangqiao Company2022.8.18HB-41Bozhou, Anhui2023.4.4
HB-18Shanghai Kangqiao Company2022.8.18HB-42Bozhou, Anhui2023.4.4
HB-19Shanghai Kangqiao Company2023.4.19HB-43Bozhou, Anhui2023.4.4
HB-20Shanghai Kangqiao Company2023.4.19HB-44Bozhou, Anhui2023.4.4
HB-21Shanghai Kangqiao Company2023.4.19HB-45Bozhou, Anhui2023.4.4
HB-22Shanghai Kangqiao Company2023.4.19HB-46Bozhou, Anhui2023.4.4
HB-23Shanghai Kangqiao Company2023.4.19HB-47Bozhou, Anhui2023.4.4
HB-24Shanghai Kangqiao Company2023.4.19HB-48Bozhou, Anhui2023.4.4
), ArticleFig(id=1200470896923234379, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1200394149213107042, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
CompoundRegression equationR2Linearity range/μg·mL-1LOQ/μg·mL-1LOD/μg·mL-1
3-O-FQAY = 14 374X + 104.2300.999 647.20-3 0250.181 50.302 5
4-O-FQAY = 2 692.9X ‒ 14.0791.000 065.37-1 0460.915 21.634 3
5-O-FQAY = 10 077X ‒ 67.1160.999 716.50-1 0560.132 00.165 0
BerberineY = 18 905X ‒ 119.1400.999 970.56-1 1290.000 20.000 7
PhellodendrineY = 57 416X ‒ 24.3961.000 03.20-1 0240.001 00.032 0
), ArticleFig(id=1200470897086812245, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1200394149213107042, language=CN, label=Table 2, caption=

Linear relationship of five compounds in Phellodendri Chinensis Cortex slice

, figureFileSmall=null, figureFileBig=null, tableContent=
CompoundRegression equationR2Linearity range/μg·mL-1LOQ/μg·mL-1LOD/μg·mL-1
3-O-FQAY = 14 374X + 104.2300.999 647.20-3 0250.181 50.302 5
4-O-FQAY = 2 692.9X ‒ 14.0791.000 065.37-1 0460.915 21.634 3
5-O-FQAY = 10 077X ‒ 67.1160.999 716.50-1 0560.132 00.165 0
BerberineY = 18 905X ‒ 119.1400.999 970.56-1 1290.000 20.000 7
PhellodendrineY = 57 416X ‒ 24.3961.000 03.20-1 0240.001 00.032 0
), ArticleFig(id=1200470897309110375, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1200394149213107042, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
No.CompoundThicknessColour
≥ 4 mma< 4 mmbBright yellowcDark yellowd
15-O-FQA0.75 ± 0.180.62 ± 0.100.70 ± 0.150.52 ± 0.11
23-O-FQA3.12 ± 0.662.70 ± 0.553.01 ± 0.512.09 ± 0.75
34-O-FQA2.00 ± 0.501.68 ± 0.311.89 ± 0.411.40 ± 0.33
4Phellodendrine0.59 ± 0.110.49 ± 0.060.54 ± 0.090.48 ± 0.11
5Berberine6.66 ± 1.075.38 ± 0.635.97 ± 1.045.80 ± 1.20
1+2+35.88 ± 1.225.00 ± 0.875.61 ± 0.954.02 ± 1.16
4+57.25 ± 1.185.88 ± 0.696.52 ± 1.136.29 ± 1.31
Total13.15 ± 2.2510.89 ± 1.4112.14 ± 2.0010.32 ± 2.41
), ArticleFig(id=1200470897447522419, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1200394149213107042, language=CN, label=Table 3, caption=

Average contents of 5 constituents in Phellodendri Chinensis Cortex slice samples from different appearance types (an = 38, bn = 48, cn = 74, dn = 12, mean ± SD, %)

, figureFileSmall=null, figureFileBig=null, tableContent=
No.CompoundThicknessColour
≥ 4 mma< 4 mmbBright yellowcDark yellowd
15-O-FQA0.75 ± 0.180.62 ± 0.100.70 ± 0.150.52 ± 0.11
23-O-FQA3.12 ± 0.662.70 ± 0.553.01 ± 0.512.09 ± 0.75
34-O-FQA2.00 ± 0.501.68 ± 0.311.89 ± 0.411.40 ± 0.33
4Phellodendrine0.59 ± 0.110.49 ± 0.060.54 ± 0.090.48 ± 0.11
5Berberine6.66 ± 1.075.38 ± 0.635.97 ± 1.045.80 ± 1.20
1+2+35.88 ± 1.225.00 ± 0.875.61 ± 0.954.02 ± 1.16
4+57.25 ± 1.185.88 ± 0.696.52 ± 1.136.29 ± 1.31
Total13.15 ± 2.2510.89 ± 1.4112.14 ± 2.0010.32 ± 2.41
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基于“辨状论质”理论研究黄柏饮片外观性状与内在质量相关性
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陆美龙 1, 2 , 张倩 1, 2 , 谷丽华 1, 2, * , 杨莉 1, 2 , 王峥涛 1, 2, *
药学学报 | 研究论文 2024,59(7): 2117-2125
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药学学报 | 研究论文 2024, 59(7): 2117-2125
基于“辨状论质”理论研究黄柏饮片外观性状与内在质量相关性
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陆美龙1, 2, 张倩1, 2, 谷丽华1, 2, * , 杨莉1, 2, 王峥涛1, 2, *
作者信息
  • 1.上海中医药大学中药研究所, 中药标准化教育部重点实验室, 国家中医药管理局中药新资源与质量评价重点实验室, 上海 201203
  • 2.上海中药标准化研究中心, 上海 201203

通讯作者:

*谷丽华, E-mail: ;
王峥涛, E-mail:
Exploring correlation between appearance characters and internal quality of Phellodendri Chinensis Cortex slice based on the theory of "quality evaluation through morphological identification"
Mei-long LU1, 2, Qian ZHANG1, 2, Li-hua GU1, 2, * , Li YANG1, 2, Zheng-tao WANG1, 2, *
Affiliations
  • 1. The MOE Key Laboratory for Standardization of Chinese Medicines and the SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
  • 2. Shanghai R&D Center for Standardization of Chinese Medicines, Shanghai 201203, China
出版时间: 2024-07-12 doi: 10.16438/j.0513-4870.2023-1393
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黄柏传统评价以“肉厚色黄者”为佳, 但该性状特征的科学内涵尚未被阐释。本研究基于“辨状论质”理论, 首先采用游标卡尺和全自动测色色差仪对黄柏饮片厚度和粉末颜色进行量化, 同时采用高效液相色谱(HPLC) 法建立黄柏饮片特征图谱, 共表征12个特征峰, 将外观性状指标与水分、醇溶性浸出物以及特征成分进行Pearson相关性分析、主成分分析(PCA)、正交偏最小二乘判别分析(OPLS-DA) 和方差分析(ANOVA), 探讨黄柏饮片外观性状(包括厚度和粉末三色空间值L*亮度、a*红绿、b*黄蓝) 与内在成分及相对含量之间的相关性。结果表明黄柏的外观性状特征(厚度和颜色) 与黄柏中的化学成分(生物碱和酚酸) 的含量呈显著相关性。基于分析结果, 对5种代表性成分进行含量测定, 5种成分含量总和在厚度≥ 4 mm (13.15% ± 2.25%) 和鲜黄色样品(12.14% ± 2.00%) 中的含量显著高于厚度 < 4 mm (10.89% ± 1.41%) 和暗黄色黄柏样品(10.32% ± 2.41%), 将外观性状与成分含量建立“性状-质量-含量”对应关系。研究结果证实黄柏饮片厚度和颜色2个主要外观性状指标可作为判断其品质的依据, 一定程度上诠释了黄柏传统评价“肉厚色黄者为佳”的科学内涵, 同时为黄柏饮片商品等级标准的建立积累了数据和基础。

黄柏  /  外观性状  /  异喹啉生物碱  /  阿魏酰奎宁酸  /  含量测定  /  相关分析

Phellodendri Chinensis Cortex (PCC) featured with thick cortex and bright-yellow is considered to be of high quality according to traditional appearance traits evaluation mode. However, the correlation between appearance traits and internal quality of PCC has not been scientifically revealed. Here, based on the theory of "Quality Evaluation Through Morphological Identification", the correlation of both sides was studied systematically. Firstly, the thickness of PCC slices was measured by vernier calipers for classification, and the colour of PCC slice was estimated by naked eyes and automatic colorimeter and classified. Secondly, high performance liquid chromatography (HPLC) was used to establish fingerprint chromatogram containing 12 characteristic peaks, and the contents of moisture and ethanolic extractive were determined as well. The correlation among the appearance traits of PCC slice (including the thickness and the spatial values of colour of PCC slice powder: L*Lightness, a*Red-Green, b*Yellow-Blue) and peak areas of 12 characteristic peaks, contents of moisture and extractive were explored through multivariate statistical analysis tools, including Pearson's correlation analysis, principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (PLS-DA) and analysis of variance (ANOVA). Association analysis results showed the morphological parameters (thickness and color) are significantly correlated with 2 kinds of principle chemical constituents (alkaloids and phenolic acids), among them, 5 major constituents including 2 isoquinoline alkaloids and 3 feruloylquinic acids were simultaneously quantitatively analyzed under the same chromatographic condition for establishing a correspondence between appearance traits and content of internal substances, and the sum of the contents of the five components was significantly higher in the thickness ≥ 4 mm (13.15% ± 2.25%) and bright yellow samples (12.14% ± 2.00%) than that in the thickness < 4 mm (10.89% ± 1.41%) and dark yellow samples (10.32% ± 2.41%). The research results confirm that thickness and colour, two main appearance parameters of PCC slice, can be used as the indicators for evaluating the quality PCC slice, which to some extent interprets the scientific connotation of traditional evaluation theory of "thick and yellow are better". At the same time, the results provided substantial data and foundation for the establishment of commodity grade standards of PCC slice.

Phellodendri Chinensis Cortex  /  appearance  /  isoquinoline alkaloid  /  feruloylquinic acid  /  quantification  /  correlation analysis
陆美龙, 张倩, 谷丽华, 杨莉, 王峥涛. 基于“辨状论质”理论研究黄柏饮片外观性状与内在质量相关性. 药学学报, 2024 , 59 (7) : 2117 -2125 . DOI: 10.16438/j.0513-4870.2023-1393
Mei-long LU, Qian ZHANG, Li-hua GU, Li YANG, Zheng-tao WANG. Exploring correlation between appearance characters and internal quality of Phellodendri Chinensis Cortex slice based on the theory of "quality evaluation through morphological identification"[J]. Acta Pharmaceutica Sinica, 2024 , 59 (7) : 2117 -2125 . DOI: 10.16438/j.0513-4870.2023-1393
传统的中药质量评价方法是基于千百年来人们对药材外观特征的观察和感官感受所形成的独特的经验总结。1994年, 谢宗万先生[1, 2]首次提出“辨状论质”的概念, 其本质是依据中药材的外观性状, 包括形、色、气、味, 利用看、摸、闻、尝等方法判断药材的真伪优劣, 阐明药材的质量。采用“辨状论质”法鉴别药材真伪优劣具有快速、简便、经济的优势。明确能够反映药材内在质量的性状特征, 未来与现代仿生感官技术和红外感应测量技术相结合, 可实现药材现场、批量、实时的快速有效的质量监测和评价, 有利于现代中药的发展应用。
然而目前传统经验鉴别描述较为模糊, 缺乏科学、详实的实验数据支撑, 缺乏客观量化标准, 应用受到限制。借助现代科学技术的发展, 诸多学者采用现代分析技术对中药内在成分进行分析和挖掘, 开展中药外观性状与关键化学成分的相关性研究[3, 4], 结合最新的药理学研究数据和成果, 可为“辨状论质”的科学内涵提供现代科学证据, 同时也是对传统性状特征进行再评价、筛选和排序的过程, 进一步发展“辨状论质”概念内涵, 确定关键质控点, 提升其可操作性。
黄柏为芸香科植物黄皮树Phellodendron chinense Schneid.的干燥树皮。始载于《神农本草经》, 具有清热燥湿, 泻火除蒸, 解毒疗疮的功效[5]。黄柏的主要化学成分有异喹啉生物碱类(小檗碱、黄柏碱)、柠檬苦素三萜类(黄柏内酯、黄柏酮) 和酚酸类(绿原酸)[6, 7]。历代本草多处记载以外观性状特征对黄柏品质进行优劣评价, 如“蜀出肉厚色深者为佳”[8, 9]、“皮紧厚二、三分, 鲜黄者上”[10]。现代药材等级标准仍沿袭此评价模式, 如2018年由中华中医药学会发布的《中药材商品规格等级》, 根据黄柏药材的宽度和厚度将黄柏药材划分为一等(宽度≥ 30 cm, 厚度≥ 0.3 cm) 和二等(厚度为0.1 cm~0.3 cm)。同时, 有学者以一种或几种生物碱(如小檗碱、黄柏碱、药根碱等) 为指标, 研究和划分黄柏的质量优劣和等级标准[11, 12], 另有学者以颜色为指标, 采用分光测色仪测定外观颜色Lab色彩空间值区分黄柏与关黄柏[13], 但研究尚缺乏系统性全面性。2020年版《中华人民共和国药典》以小檗碱和黄柏碱为黄柏药材质量的定性定量指标。
本实验室在研究中发现黄柏中含有丰富的阿魏酰奎宁酸类成分, 该类成分具有抗氧化、抗炎、降糖和抗肿瘤[14-18]等药理活性, 与黄柏的传统功效较为一致, 但尚未得到关注。目前市场上流通的黄柏饮片样本存在厚度和颜色的显著差异。因此, 本研究基于历代本草和中药材规格等级标准对黄柏“辨状论质”内容进行梳理和总结, 以颜色和厚度为主要性状指标, 建立黄柏饮片的特征图谱和关键成分的含量测定方法, 探讨“肉厚色黄为佳”的传统“辨状论质”特征与关键化学成分之间的相关性, 为黄柏“辨状论质”提供科学理论基础, 同时为黄柏饮片商品规格等级的建立提供依据。
仪器与试剂  Agilent 1260 Infinity高效液相色谱仪和Agilent ZORBAX SB-C18色谱柱(250 mm × 4.6 mm, 5 μm), 美国安捷伦科技有限公司; SQP型电子天平和BCE2241-1CCN型电子天平, 赛多利斯科学仪器有限公司; 超声波清洗仪器(BRANSON 5800), 美国Branson公司; 手提式高速中药粉碎机, 上海市明德机械制造有限公司。
对照品5-O-阿魏酰奎宁酸(5-O-feruloylquinic acid, 5-O-FQA, 批号为PRF22071321, 纯度为98%)、3-O-阿魏酰奎宁酸(3-O-feruloylquinic acid, 3-O-FQA, 批号为PRF22110723, 纯度为98%)、4-O-阿魏酰奎宁酸(5-O-feruloylquinic acid, 4-O-FQA, 批号为PRF22110222, 纯度为98%)、木兰花碱(magnoflorine, 批号为AF20082219, 纯度为98%)、黄柏碱(phellodendrine, 批号为AF21070303, 纯度为98%)、盐酸药根碱(jatrorrhizine hydrochloride, 批号为AF20072502, 纯度为98%)、巴马汀(palmatine, 批号为AF20071152, 纯度98%)、小檗碱(berberine, 批号为AZ21090715, 纯度为98%)、黄柏内酯(obakulactone, 批号为AF21052757, 纯度为98%) 均购自成都普瑞法科技开发有限公司; 绿原酸(chlorogenic acid, 批号为25-1004, 纯度为98%) 和黄柏酮(obakunone, 批号为20-2008, 纯度为98%) 购自上海中药标准化研究中心。色谱级乙腈(Fisher Scientific, 加拿大); 分析级甲醇、乙醇(上海国药集团化学试剂有限公司); Milli-Q超纯水仪(Millipore, 美国)。
实验材料  共收集黄柏饮片样品48批, 见表 1。其中19批样品采集于安徽省亳州市中药材市场, 13批样品收集于四川都江堰, 16批样品收集于上海康桥中药饮片公司。所有样品均产自四川, 经上海标准化研究中心谷丽华研究员收集鉴定, 标本保存在上海中药标准化研究中心。
外观性状测定  随机取10批黄柏饮片, 采用游标卡尺测量厚度, 每批按厚度分为1~2 mm、2~3 mm、3~4 mm、4~5 mm、≥ 5 mm五种类别; 取48批黄柏饮片, 从每批饮片中各随机取500片, 用游标卡尺测量厚度, 将厚度符合药典规定(≥ 1mm) 的样品, 按厚度划分为≥ 4 mm和 < 4 mm两类, 共得到86批不同厚度的黄柏样品。
分别以目测法和全自动测色色差仪评价黄柏饮片的粉末颜色。以目测法观察黄柏饮片断面和粉末颜色, 将黄柏分为亮黄色和暗黄色两类; 使用全自动测色色差仪测定黄柏粉末(过三号筛) 色度值L* (代表明暗度)、a* (代表红绿色度)、b* (代表黄蓝色度)。色差仪测定条件: 可选光源为D65; 视场选择为8°视角; 测定孔径为8 mm; 光源为LED蓝光激发; 仪器误差: ∆E*ab ≤ 0.4。仪器经黑白板校正后对样品进行测定。
供试品溶液的制备  取黄柏样品粉末(过三号筛) 约0.2 g, 精密称定, 置100 mL具塞锥形瓶中, 精密加入75%甲醇20 mL, 超声处理(功率250 W, 频率40 kHz) 30 min, 取出, 待提取液放冷至室温后, 用75%甲醇补足减失重量, 摇匀, 滤过, 取续滤液过0.45 μm微孔滤膜, 即得。
对照品溶液的制备  取绿原酸、5-O-FQA、3-O-FQA、4-O-FQA、木兰花碱、黄柏碱、盐酸药根碱、巴马汀、小檗碱、黄柏酮、黄柏内酯对照品适量, 精密称定, 用75%甲醇溶解并制备成浓度分别为0.162、0.147、0.423、0.146、0.163、0.143、0.009、0.007、0.157、0.008、0.007 mg·mL-1的混合对照品溶液。
色谱条件  色谱柱采用Agilent ZORBAX SB C18 (250 mm × 4.6 mm, 5 μm), 流动相为乙腈(A)-0.07 mol·L-1氯化铵水溶液(B), 梯度洗脱: 0~8 min, 8%→12% A; 8~25 min, 12%→20% A; 25~55 min, 20%→50% A; 体积流量为1 mL·min-1, 柱温为25 ℃, 进样量为5 μL; 特征图谱检测波长为215 nm; 含量测定取各色谱峰的最大吸收波长。液质分析流动相为乙腈-0.1%甲酸体系。
质谱条件  Agilent Dual AJS ESI离子源, 干燥气温度300 ℃, 干燥气体流速8 L·min-1, 雾化器压力241 kPa, 毛细管电压3 500 V, 碰撞电压140 V。质谱选用一级质谱(MS) 模式, 采用负离子模式进行扫描, 质量扫描范围m/z 50~1 000。数据采用Agilent Mass Hunter-Data Acquisition 10.0软件采集。
水分和浸出物测定  参照2020年版《中华人民共和国药典》四部通则中水分测定法第二法(烘干法) 和醇溶性浸出物测定法(热浸法) 对黄柏饮片水分和浸出物进行测定。
统计学处理  文中数据相关性分析采用GraphPad Prism 8.0、Origin 2021和SIMCA 14.1软件。将P < 0.05定义为具有显著性差异, P < 0.01定义为具有极显著性差异。
12个成分与相邻色谱峰分离度均大于1.5, 色谱图见图 1, 专属性良好。
取同一批黄柏样品(HB-29), 按“供试品溶液制备”项和“色谱条件”项操作, 连续进样6次, 记录色谱图, 以小檗碱为参照峰, 各特征峰相对保留时间和相对峰面积的RSD均小于3.0%, 表明仪器精密度良好。
取同一批黄柏样品(HB-29), 按照“供试品溶液制备”项和“色谱条件”项进行测定, 记录色谱图。以小檗碱为参照峰, 各特征峰相对保留时间和相对峰面积的RSD均小于3.0%, 表明该方法重现性良好。
取同一批黄柏样品(HB-29), 按照“供试品溶液制备”项和“色谱条件”项操作, 分别在第0、2、4、8、12、24、48 h进样, 记录色谱图。以小檗碱为参照峰, 各特征峰相对保留时间和相对峰面积的RSD均小于4.0%, 表明供试品溶液在48 h内稳定。
特征图谱共标定12个特征峰, 采用对照品比对及质谱辅助鉴定的方法, 共指认出10个特征峰: 1号峰为绿原酸, 2号峰为5-O-FQA, 3号峰为3-O-FQA, 4号峰为5-O-FQA, 5号峰为木兰花碱, 6号峰为黄柏碱, 9号峰为药根碱, 10号峰为巴马汀, 11号峰为小檗碱, 12号峰为黄柏内酯。将86批黄柏饮片色谱图导入《中药色谱指纹图谱相似度评价系统》软件(2012版) 进行分析, 以HB-1为参照图谱, 采用中位数法, 时间窗宽度为0.1 min, 运用多点校正Mark峰匹配, 建立黄柏饮片的特征图谱, 86批黄柏饮片的相似度为0.978~1.000。
86批黄柏饮片的水分和浸出物测定结果均符合2020年版《中华人民共和国药典》的规定。
分别统计厚度为1~2、2~3、3~4、4~5和≥ 5 mm类别下特征图谱中12个特征峰的色谱峰面积进行相关性分析, 以含量最高且具有活性的三个代表成分为例。分析结果显示各成分含量随着黄柏皮厚度增加而增加, 4 mm为含量差异的明显节点(图 2)。
进一步验证厚度与内在成分的相关性, 将厚度分别为≥ 4和 < 4 mm两类共计86批黄柏样本的浸出物、水分、Lab值、特征图谱中12个特征峰的峰面积作为原始数据, 应用Pearson相关分析、单因素方差分析(one-way ANOVA)、主成分分析(PCA)、正交偏最小二乘法判别分析(OPLS-DA) 和方差分析方法, 探讨外观性状与各内参数的关系, 寻找与黄柏品质相关的内在指标成分。
图 3所示, 红色圈代表正相关, 蓝色圈代表负相关, 颜色越深代表相关性越显著。黄柏外观性状指标与主要化学成分之间存在相关性: 厚度与小檗碱、浸出物、黄柏碱、药根碱、3-O-FQA呈极显著正相关(P < 0.01), 与峰8呈极显著负相关(P < 0.01)。黄柏粉末颜色L*值与3-O-FQA、4-O-FQA、5-O-FQA含量呈极显著正相关, 与巴马汀含量呈显著负相关; 粉末颜色a*值与小檗碱、浸出物含量呈极显著正相关, 与绿原酸、峰8含量呈负相关; 粉末颜色b*值与3-O-FQA、4-O-FQA、5-O-FQA、黄柏碱含量呈极显著正相关。相关分析结果表明黄柏的厚度参数主要与生物碱成分小檗碱、黄柏碱及浸出物具有较强的正相关, 颜色参数主要与3-O-FQA、4-O-FQA、5-O-FQA具有较显著的正相关。水分与其他各参数的相关系数均低于0.40。
为进一步发掘不同外观性状黄柏成分的轮廓特征, 将厚度和颜色数据导入SIMCA 14.1软件中进行PCA和OPLS-DA分析, 根据变量载荷评价参数值(VIP) 筛选影响外观性状的关键内在参数。
采用非监督模式识别方法PCA观察样品的自然聚集情况, 不同厚度、不同颜色的黄柏样品的PCA得分见图 4A。2种厚度的样品分别聚为一类, 2种颜色的样品较为分散, 由此可见, 影响黄柏有效成分含量最关键的外观性状因素是皮的厚度。OPLS-DA结果(图 4B) 与PCA结果一致。VIP得分图(图 4C) 显示4-O-FQA (峰4)、峰8、绿原酸(峰1)、黄柏碱(峰6)、小檗碱(峰11)、3-O-FQA (峰3)、5-O-FQA (峰2)、浸出物和峰7这9个指标的VIP值> 1, 表明这9个指标在不同外观性状区分中起到重要作用。
对OPLS-DA筛选到的VIP值> 1的差异化合物相对含量进行单因素方差分析, 验证其与不同外观性状参数的相关性, 同时采用GraphPad Prism 8.0软件绘制差异化合物相对含量的雨点图, 见图 5。结果小檗碱、黄柏碱在两种厚度下的差异达到极显著水平(P < 0.000 1)、其次是浸出物, 再次是5-O-FQA、3-O-FQA及4-O-FQA等; 3-O-FQA、4-O-FQA、5-O-FQA在2种颜色下的差异达到极显著水平(P < 0.001), 其次是黄柏碱。综合相关分析、主成分分析、正交偏最小二乘判别分析的结果, 不同厚度和颜色黄柏样品的内在指标存在差异, 并呈现一定规律性。但就外观性状来说, 厚度对内在指标的影响较颜色更大, 不同颜色的样品内在指标有差异但受到厚度的影响。基于以上分析, 选择小檗碱、黄柏碱、3-O-FQA、4-O-FQA和5-O-FQA五种最相关成分进行含量测定, 为黄柏质量评价和等级标准建立提供数据基础。
采用HPLC建立黄柏样品中2种生物碱成分(小檗碱、黄柏碱) 和3种酚酸成分(5-O-FQA、3-O-FQA、4-O-FQA) 的多成分含量测定方法, 根据各成分最大吸收波长设置检测波长为324 nm (0~14 min, 5-O-FQA、3-O-FQA、4-O-FQA)、206 nm (14~20 min, 黄柏碱) 和230 nm (20~55 min, 小檗碱)。
小檗碱、黄柏碱、5-O-FQA、4-O-FQA和3-O-FQA五种成分含量测定方法的线性关系考察结果见表 2, 各成分在各自范围内线性关系良好。精密度、稳定性、重复性试验结果的RSD均小于3.0%, 表明仪器精密度和方法重复性良好, 供试品溶液室温放置72 h内基本稳定; 平均加样回收率分别为95.77%、100.27%、100.29%、95.75%、98.80%, RSD分别为1.50%、1.70%、1.20%、1.10%、1.50%, 表明方法科学可靠。
精密称取86批黄柏饮片粉末, 按“供试品溶液制备”项下制备样品溶液, 按“色谱条件”项下进样分析, 计算各样品中小檗碱、黄柏碱、3-O-FQA、4-O-FQA、5-O-FQA的含量。五种成分的含量在不同厚度和不同颜色样品中的均值信息见表 3, 厚度≥ 4 mm和鲜黄色样品中的成分含量显著高于厚度 < 4 mm和暗黄色黄柏样品。
本研究对提取参数进行了考察, 分别比较了不同提取溶剂、提取时间、溶剂体积对黄柏中各成分的提取效率。黄柏中含有季铵类生物碱, 文献中多采用含酸试剂如0.1%磷酸的甲醇溶液[19]、0.1%甲酸的乙腈溶液[20], 以提高生物碱类的提取效率。本实验比较了1%盐酸甲醇、75%甲醇和甲醇三种提取溶剂, 发现对小檗碱、黄柏碱的提取率: 75%甲醇 > 1%盐酸甲醇 > 甲醇, 75%甲醇对阿魏酰奎宁酸成分的提取率5~10倍于1%盐酸甲醇的提取率。阿魏酰奎宁酸结构中存在酯基和邻二酚羟基, 易受提取试剂pH值及提取温度的影响而发生水解和异构化[21], 1%盐酸甲醇溶液使阿魏酰奎宁酸类成分发生转化, 转化程度与酸含量和提取时间有关。因此最终选择75%甲醇超声处理用于样品制备。
黄柏作为茎皮类药材, 古人常以色泽的深浅和茎皮的厚度来判别品质的优劣[22]。历代本草中有关黄柏品质评价均以“肉厚色黄者为佳”。本研究发现黄柏中各成分含量随树皮厚度呈递增趋势, 与“皮类药材生长年限与有效成分积累成正比”的理论一致[23]。有文献[24]报道随着生长年限的增加, 其小檗碱含量呈逐渐增长的趋势, 到达一定树龄后增长趋势变缓慢并呈现下降的趋势。实验发现厚度小于4 mm时, 各成分含量呈递增趋势, 厚度大于4 mm后各成分含量增长缓慢, 以4 mm厚度为界限将黄柏饮片样本划分两类, 化学成分含量差异最为显著。Pearson相关分析结果显示与树皮厚度最相关的是生物碱类, 主要为小檗碱、黄柏碱; 与颜色最相关的是阿魏酰奎宁酸类成分, 主要为3-O-FQA、4-O-FQA和5-O-FQA。PCA和OPLS-DA结果显示,两种厚度样品各聚为一类, 不同颜色样品聚类受到厚度的影响, 较为离散, 说明影响黄柏有效成分含量最关键的因素仍是树皮厚度。
中药颜色的形成是受内在物质基础和外界环境因子等多种因素共同作用的结果, 内在物质是颜色形成的主要原因, 药材呈色机理主要与其呈色物质的共轭体系结构密切相关。分子共轭体系中的ππ*跃迁和含有杂原子的不饱和有机物的p→π共轭体系中的n→π*或n→σ*跃迁是其呈色的主要原因[25]。黄柏以色黄者为佳, 收集的样品主要呈现出亮黄色和暗黄色两种颜色特征, 在黄色、明暗两个色度方向上有差异。实验中色度值b*表示黄蓝色度, L*表示明暗程度, a*表示红绿色度, 相关分析表明b*L*与阿魏酰奎宁酸成分呈显著正相关, a*与小檗碱和浸出物呈显著正相关。小檗碱呈亮黄色, 在黄柏中的含量高, 被认为是黄柏的主要呈色物质之一[26]。3-O-FQA、4-O-FQA和5-O-FQA颜色呈类白色至浅黄色, 含量在不同颜色样品中差异较其他成分显著。此类成分容易氧化, 易受药材的外界环境如产地、采收期、储藏、炮制工艺[27, 28]等因素的影响, 可能是表征黄柏外观颜色的潜在标志物。
小檗碱和黄柏碱具有抑菌[29]、抗炎[30, 31]、抗氧化[32, 33]的作用, 一直是黄柏的指标成分, 体现了现行标准的科学性。阿魏酰奎宁酸类成分是一种有效的酚类抗氧剂, 具有抗氧化、清除体内自由基、抑制突变和抗肿瘤的生物活性[21], 该类成分在黄柏中的含量很高, 接近生物碱的总含量, 且与黄柏颜色具有显著相关性, 值得关注。
黄柏作为大宗药材, 因其市场需求大、成材周期长导致野生资源减少, 目前以栽培为主, 其性状特征、质量可能存在一些改变, 深入探讨其外观性状与内在品质的关联性有助于中药产业优化和临床用药安全。本研究基于“辨状论质”理论, 系统探索并确证黄柏外观性状与内在指标存在相关性, 一定程度上诠释了黄柏传统评价“肉厚色黄者为佳”的科学内涵, 同时建议在对黄柏饮片现有评价标准基础上, 对其外观指标厚度和颜色进行具体规定, 阿魏酰奎宁酸类成分在不同外观性状黄柏饮片中均体现显著性差异, 其中3-O-阿魏酰奎宁酸含量高, 各组之间差异较大, 且具有良好的药理活性。因此, 建议在现有评价标准基础上纳入阿魏酰奎宁酸含量指标引导黄柏饮片优质优价。
作者贡献: 谷丽华和王峥涛负责选题和实验设计; 陆美龙负责实验实施及文章初稿撰写; 张倩参与收集实验材料和格式修改; 陆美龙、谷丽华、杨莉、王峥涛负责文章修改。
利益冲突: 不存在利益冲突。
  • 上海市“三年行动”计划项目(ZY(2021-2023)-0215)
  • 上海市技术标准项目(21DZ2202100)
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2024年第59卷第7期
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doi: 10.16438/j.0513-4870.2023-1393
  • 接收时间:2023-12-14
  • 首发时间:2025-11-26
  • 出版时间:2024-07-12
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  • 收稿日期:2023-12-14
  • 修回日期:2024-04-18
基金
上海市“三年行动”计划项目(ZY(2021-2023)-0215)
上海市技术标准项目(21DZ2202100)
作者信息
    1.上海中医药大学中药研究所, 中药标准化教育部重点实验室, 国家中医药管理局中药新资源与质量评价重点实验室, 上海 201203
    2.上海中药标准化研究中心, 上海 201203

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*谷丽华, E-mail: ;
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
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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