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Article(id=1209792480342578161, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1209792462298674131, articleNumber=null, orderNo=null, doi=10.16438/j.0513-4870.2021-1173, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1630252800000, receivedDateStr=2021-08-30, revisedDate=1637251200000, revisedDateStr=2021-11-19, acceptedDate=null, acceptedDateStr=null, onlineDate=1766366604170, onlineDateStr=2025-12-22, pubDate=1647014400000, pubDateStr=2022-03-12, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1766366604170, onlineIssueDateStr=2025-12-22, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1766366604170, creator=13701087609, updateTime=1766366604170, updator=13701087609, issue=Issue{id=1209792462298674131, tenantId=1146029695717560320, journalId=1189982191388893191, year='2022', volume='57', issue='3', pageStart='547', pageEnd='844', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1766366599868, creator=13701087609, updateTime=1766370620295, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1209809325250450301, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1209792462298674131, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1209809325250450302, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1209792462298674131, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=775, endPage=782, ext={EN=ArticleExt(id=1209792481160466437, articleId=1209792480342578161, tenantId=1146029695717560320, journalId=1189982191388893191, language=EN, title=Quality evaluation of Aster souliei based on quantitative analysis of multi-components by single marker, columnId=1190335348761793317, journalTitle=Acta Pharmaceutica Sinica, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=

To establish a quantitative analysis of multi-components by single marker (QAMS) for the determination of Aster souliei Franch., the relative correction factors (fx) of neochlorogenic acid, cryptochlorogenic acid, rutin, isoquercitrin, isochlorogenic acid B, isochlorogenic acid A, isochlorogenic acid C, quercetin, apigenin and kaempferol were established by ultra-high performance liquid chromatography with chlorogenic acid as internal reference. Meanwhile, the content of each component was determined by the external standard method (ESM) and QAMS, and a linear regression model was established to verify the feasibility and accuracy of the QAMS. Hierarchical clustering analysis (HCA) and orthogonal partial least square discriminate analysis (OPLS-DA) were used to evaluate the quality of 23 batches of A. souliei. The results showed that the repeatability of each fx was good. The average content of neochlorogenic acid, cryptochlorogenic acid, rutin, isoquercitrin, isochlorogenic acid B, isochlorogenic acid A, isochlorogenic acid C, quercetin, apigenin and kaempferol in 23 batches of A. souliei by QAMS was 0.165, 0.234, 6.115, 0.478, 0.484, 3.359, 1.382, 0.210, 0.172, and 0.057 mg·g-1, respectively. The mean content determined by the ESM method was 0.163, 0.235, 6.172, 0.479, 0.483, 3.343, 1.413, 0.207, 0.171, and 0.056 mg·g-1. The results of HCA and OPLS-DA analysis show that 23 batches of A. souliei can be divided into two groups based on caffeic acid content. The content of the first group was between 0.873 to 5.647 mg·g-1, while the second was between 8.524 to 16.705 mg·g-1. This QAMS method can be used to simply and quickly evaluate the quality A. souliei.

, correspAuthors=Zhi-feng ZHANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2022 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=Xue-yan SU, Ying-xiu ZHANG, Ri-za ZHAO, Wen-pu CAI, Zhi-feng ZHANG), CN=ArticleExt(id=1209792485707092107, articleId=1209792480342578161, tenantId=1146029695717560320, journalId=1189982191388893191, language=CN, title=基于一测多评法的缘毛紫菀多成分含量测定及质量评价, columnId=1190335348896011050, journalTitle=药学学报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=

建立测定缘毛紫菀(Aster souliei Franch.) 多成分的一测多评法。采用超高效液相色谱法, 以绿原酸为内参物, 建立新绿原酸、隐绿原酸、芦丁、异槲皮苷、异绿原酸B、异绿原酸A、异绿原酸C、槲皮素、芹菜素、山柰酚10个成分的相对校正因子, 并同时采用外标法与一测多评法测定各成分的含量, 建立线性回归模型。采用系统聚类分析和正交偏最小二乘法-判别分析对23批缘毛紫菀进行质量评价。结果表明: 各校正因子重复性良好, 一测多评法测得23批缘毛紫菀中新绿原酸、隐绿原酸、芦丁、异槲皮苷、异绿原酸B、异绿原酸A、异绿原酸C、槲皮素、芹菜素、山柰酚的平均含量为0.165、0.234、6.115、0.478、0.484、3.359、1.382、0.210、0.172和0.057 mg·g-1; 外标法测得平均含量为0.163、0.235、6.172、0.479、0.483、3.343、1.413、0.207、0.171和0.056 mg·g-1, 两种方法各成分含量无显著性差异。聚类分析和正交偏最小二乘法-判别分析结果显示23批缘毛紫菀可以聚为两类, 这两类差异主要表现在咖啡酸类含量上, 第一类含量范围为0.873~5.647 mg·g-1, 第二类含量为8.524~16.705 mg·g-1。建立的一测多评法被证实是一种科学和缺少对照品的可替代方法, 可以简便快速评价缘毛紫菀的质量。

, correspAuthors=张志锋, authorNote=null, correspAuthorsNote=
*张志锋, Tel: 86-28-89165778, E-mail:
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No.OriginLongitude /ELatitude /NAltitude /mCollecting time
S1Gongjue, Tibet98°30′37″30°30′33″418 3.62020.7.11
S2Dege, Sichuan99°02′01″32°01′06″407 9.42020.7.30
S3Kangding, Sichuan101°40′10″30°10′26″375 1.92020.7.31
S4Basu, Tibet96°41′36″30°00′31″376 3.02019.6.24
S5Changdu, Tibet97°18′55″31°30′22″350 4.02019.6.25
S6Kangding, Sichuan101°30′56″29°44′07″337 0.02019.6.08
S7Daocheng, Sichuan101°05′49″31°01′44″390 0.02019.6.26
S8Gongjue, Tibet98°10′08″31°09′07″409 5.62020.7.12
S9Songpan, Sichuan103°23′26″32°55′53″350 5.02019.7.02
S10Kangding, Sichuan101°45′04″30°08′04″429 0.62020.7.08
S11Kangding, Sichuan101°38′16″30°09′41″366 7.52020.7.08
S12Basu, Tibet.96°44′31″29°30′20″391 7.92019.6.22
S13Ganzi, Sichuan99°34′01″31°43′59″380 0.02019.6.27
S14Ganzi, Sichuan99°37′45″31°42′26″373 7.52020.7.30
S15Hongyuan, Sichuan102°21′59″32°42′22″351 4.02019.7.30
S16Mangkang, Tibet98°41′29″29°13′59″399 9.82020.7.11
S17Changdu, Tibet97°28′16″31°24′25″369 1.02019.6.26
S18Litang, Sichuan99°42′12″30°16′57″420 0.02019.6.22
S19Zaduo, Qinghai95°28′54″32°51′12″403 4.32020.7.28
S20Dege, Sichuan99°00′56″31°33′29″420 0.02019.6.27
S21Daofu, Sichuan101°32′05″30°26′11″355 4.02019.6.28
S22Hongyuan, Sichuan102°25′59″32°45′09″351 1.02019.7.03
S23Daofu, Sichuan101°19′10″30°46′15″393 4.02019.6.27
), ArticleFig(id=1209809078013006842, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1209792480342578161, language=CN, label=Table 1, caption=

Sample information of A. souliei

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No.OriginLongitude /ELatitude /NAltitude /mCollecting time
S1Gongjue, Tibet98°30′37″30°30′33″418 3.62020.7.11
S2Dege, Sichuan99°02′01″32°01′06″407 9.42020.7.30
S3Kangding, Sichuan101°40′10″30°10′26″375 1.92020.7.31
S4Basu, Tibet96°41′36″30°00′31″376 3.02019.6.24
S5Changdu, Tibet97°18′55″31°30′22″350 4.02019.6.25
S6Kangding, Sichuan101°30′56″29°44′07″337 0.02019.6.08
S7Daocheng, Sichuan101°05′49″31°01′44″390 0.02019.6.26
S8Gongjue, Tibet98°10′08″31°09′07″409 5.62020.7.12
S9Songpan, Sichuan103°23′26″32°55′53″350 5.02019.7.02
S10Kangding, Sichuan101°45′04″30°08′04″429 0.62020.7.08
S11Kangding, Sichuan101°38′16″30°09′41″366 7.52020.7.08
S12Basu, Tibet.96°44′31″29°30′20″391 7.92019.6.22
S13Ganzi, Sichuan99°34′01″31°43′59″380 0.02019.6.27
S14Ganzi, Sichuan99°37′45″31°42′26″373 7.52020.7.30
S15Hongyuan, Sichuan102°21′59″32°42′22″351 4.02019.7.30
S16Mangkang, Tibet98°41′29″29°13′59″399 9.82020.7.11
S17Changdu, Tibet97°28′16″31°24′25″369 1.02019.6.26
S18Litang, Sichuan99°42′12″30°16′57″420 0.02019.6.22
S19Zaduo, Qinghai95°28′54″32°51′12″403 4.32020.7.28
S20Dege, Sichuan99°00′56″31°33′29″420 0.02019.6.27
S21Daofu, Sichuan101°32′05″30°26′11″355 4.02019.6.28
S22Hongyuan, Sichuan102°25′59″32°45′09″351 1.02019.7.03
S23Daofu, Sichuan101°19′10″30°46′15″393 4.02019.6.27
), ArticleFig(id=1209809078197555211, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1209792480342578161, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
AnalyteCriteria curveLinearity range/µgR2
NCGAY = 16 675 172.569 5 X - 23 667.494 80.001 4-0.057 60.999 6
CGAY = 13 322 876.949 7 X - 3 078.433 30.017 3-0.690 20.999 8
CCGAY = 18 558 214.835 1 X - 35 736.280 80.002 5-0.099 60.999 1
RTY = 10 098 248.024 4 X - 5 523.188 90.017 1-0.680 00.999 8
IQTY = 14 821 573.826 4 X - 20 399.487 90.004 6-0.184 00.999 8
ICGABY = 15 132 513.568 7 X - 70 235.930 70.011 5-0.461 80.999 7
ICGAAY = 19 721 207.220 0 X - 143 398.216 60.108 2-0.728 00.999 8
ICGACY = 17 435 506.293 2 X - 82 459.603 10.006 1-0.244 00.999 5
QTY = 20 284 311.803 2 X - 29 322.134 30.001 7-0.067 00.999 7
APGY = 16 023 713.208 3 X - 18 349.248 70.002 4-0.944 00.999 7
KPY = 17 300 538.833 4 X - 24 824.824 10.002 0-0.081 61.000 0
), ArticleFig(id=1209809078314995736, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1209792480342578161, language=CN, label=Table 2, caption=

Criteria curves and linearity ranges of reference substances

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AnalyteCriteria curveLinearity range/µgR2
NCGAY = 16 675 172.569 5 X - 23 667.494 80.001 4-0.057 60.999 6
CGAY = 13 322 876.949 7 X - 3 078.433 30.017 3-0.690 20.999 8
CCGAY = 18 558 214.835 1 X - 35 736.280 80.002 5-0.099 60.999 1
RTY = 10 098 248.024 4 X - 5 523.188 90.017 1-0.680 00.999 8
IQTY = 14 821 573.826 4 X - 20 399.487 90.004 6-0.184 00.999 8
ICGABY = 15 132 513.568 7 X - 70 235.930 70.011 5-0.461 80.999 7
ICGAAY = 19 721 207.220 0 X - 143 398.216 60.108 2-0.728 00.999 8
ICGACY = 17 435 506.293 2 X - 82 459.603 10.006 1-0.244 00.999 5
QTY = 20 284 311.803 2 X - 29 322.134 30.001 7-0.067 00.999 7
APGY = 16 023 713.208 3 X - 18 349.248 70.002 4-0.944 00.999 7
KPY = 17 300 538.833 4 X - 24 824.824 10.002 0-0.081 61.000 0
), ArticleFig(id=1209809078394687522, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1209792480342578161, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
FactorNCGACCGARTIQCICGABICGAAICGACQTAPGKP
Column0.2380.0820.3680.0030.0930.00000000.1690.0050.361
Instrument0.0650.1340.3900.8850.7370.5490.5620.2200.0090.392
Analyst0.2330.6970.8830.7360.6740.2670.4360.5860.0120.358
), ArticleFig(id=1209809078512128043, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1209792480342578161, language=CN, label=Table 3, caption=

Significance levels of variance analysis of three factors

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FactorNCGACCGARTIQCICGABICGAAICGACQTAPGKP
Column0.2380.0820.3680.0030.0930.00000000.1690.0050.361
Instrument0.0650.1340.3900.8850.7370.5490.5620.2200.0090.392
Analyst0.2330.6970.8830.7360.6740.2670.4360.5860.0120.358
), ArticleFig(id=1209809078650540087, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1209792480342578161, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
No.CGANCGACCGARTIQCICGABICGAAICGACQTAPGKP
ESMQAMSESMQAMSESMQAMSESMQAMSESMQAMSESMQAMSESMQAMSESMQAMSESMQAMSESMQAMSESM
S13.0770.2440.2410.2330.2347.0557.1201.1321.1330.6320.6315.8265.7992.6422.7010.0860.0850.0870.0870.0440.043
S20.6340.0480.0470.2180.2199.1519.2360.7750.7750.6770.6761.1021.0970.2570.2621.3751.3550.4500.4460.3980.388
S31.3780.0520.0510.1630.16410.01810.1100.6240.6250.7930.7921.1351.1290.3640.3730.3180.3130.1500.1490.1620.158
S43.8830.3050.3010.2720.2745.4205.4700.4170.4170.8130.8126.8616.8290.1910.1950.0850.0840.1530.1520.0140.013
S52.3520.2080.2050.2240.2262.9632.9910.2160.2160.6800.6784.3204.2992.0232.0680.0430.0420.2020.2010.0120.011
S64.0290.1480.1460.4200.42212.26812.3810.5230.5241.1731.1710.2610.2601.4931.2270.2370.2330.2100.2090.0120.012
S74.8840.2250.2220.3190.3215.7275.7800.6400.6400.4930.4924.6264.6041.8411.8820.0860.0850.1350.1330.0250.024
S81.0800.0670.0660.1120.1135.0435.0890.3700.3710.2660.2651.2001.1950.5930.6070.2010.1980.2060.2040.0520.050
S94.1750.2050.2020.3250.3278.4118.4890.5680.5690.7510.7494.6814.6591.5671.6030.1090.1080.1160.1150.0270.026
S100.3290.0250.0250.1310.1322.9002.9270.1220.1220.0310.0310.2320.2310.1230.1260.7160.7060.0800.0790.1240.121
S110.3370.0290.0290.1100.1104.2514.2900.2490.2490.0960.0960.3180.3160.1810.1850.2270.2240.2760.2740.1590.155
S122.5960.2480.2440.2390.2414.7674.8110.3390.3390.5770.5755.1555.1312.5272.5840.1040.1030.2900.2870.0330.032
S130.3420.0700.0690.0700.0702.6762.7000.1480.1480.1400.1401.5661.5590.8460.8650.3210.3160.1830.1820.0200.020
S144.3130.2570.2530.2870.2895.4545.5051.5351.5360.9640.9627.3757.3403.4713.5490.0780.0760.1240.1230.0530.052
S154.1680.2110.2080.2750.2765.8895.9430.4130.4140.4850.4845.1805.1552.2082.2570.0380.0370.0810.0810.0160.015
S163.9770.2350.2320.2390.2403.2733.3040.7830.7830.4730.4725.5155.4892.3252.3770.0400.0400.3190.3170.0130.012
S172.9220.1900.1880.2450.2472.9012.9280.2790.2790.6820.6814.6924.6701.8241.8650.0540.0540.1060.1060.0130.012
S182.1220.1710.1690.1590.1602.8222.8490.1120.1120.2010.2012.3162.3061.8621.9040.1120.1100.1650.1640.0250.024
S193.5530.2650.2610.2360.2385.1865.2340.5850.5860.4920.4917.7657.7282.5452.6020.0740.0730.0930.0920.0210.020
S201.4270.1040.1020.2200.2229.4089.4950.4410.4410.0460.0461.2571.2510.5210.5320.1700.1670.1060.1050.0500.049
S212.2540.1120.1110.3060.30812.58412.7010.3110.3110.2000.1991.2751.2690.5610.5730.1490.1470.1280.1270.0110.011
S223.4410.2390.2350.2600.2624.4534.4940.2560.2560.2620.2613.1133.0991.2001.5270.0580.0580.1480.1470.0260.025
S232.4740.1380.1360.3160.3178.0308.1040.1640.1650.2050.2051.4921.4850.6120.6260.1450.1430.1500.1490.0120.012
Mean2.5980.1650.1630.2340.2356.1156.1720.4780.4790.4840.4833.3593.3431.3821.4130.2100.2070.1720.1710.0570.056
), ArticleFig(id=1209809078755397700, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1209792480342578161, language=CN, label=Table 4, caption=

Content determination results of 23 batches of A. souliei by QAMS and ESM (mg·g-1). ESM: External standard method; QAMS: Quantitative analysis of multi-components by single marker

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No.CGANCGACCGARTIQCICGABICGAAICGACQTAPGKP
ESMQAMSESMQAMSESMQAMSESMQAMSESMQAMSESMQAMSESMQAMSESMQAMSESMQAMSESMQAMSESM
S13.0770.2440.2410.2330.2347.0557.1201.1321.1330.6320.6315.8265.7992.6422.7010.0860.0850.0870.0870.0440.043
S20.6340.0480.0470.2180.2199.1519.2360.7750.7750.6770.6761.1021.0970.2570.2621.3751.3550.4500.4460.3980.388
S31.3780.0520.0510.1630.16410.01810.1100.6240.6250.7930.7921.1351.1290.3640.3730.3180.3130.1500.1490.1620.158
S43.8830.3050.3010.2720.2745.4205.4700.4170.4170.8130.8126.8616.8290.1910.1950.0850.0840.1530.1520.0140.013
S52.3520.2080.2050.2240.2262.9632.9910.2160.2160.6800.6784.3204.2992.0232.0680.0430.0420.2020.2010.0120.011
S64.0290.1480.1460.4200.42212.26812.3810.5230.5241.1731.1710.2610.2601.4931.2270.2370.2330.2100.2090.0120.012
S74.8840.2250.2220.3190.3215.7275.7800.6400.6400.4930.4924.6264.6041.8411.8820.0860.0850.1350.1330.0250.024
S81.0800.0670.0660.1120.1135.0435.0890.3700.3710.2660.2651.2001.1950.5930.6070.2010.1980.2060.2040.0520.050
S94.1750.2050.2020.3250.3278.4118.4890.5680.5690.7510.7494.6814.6591.5671.6030.1090.1080.1160.1150.0270.026
S100.3290.0250.0250.1310.1322.9002.9270.1220.1220.0310.0310.2320.2310.1230.1260.7160.7060.0800.0790.1240.121
S110.3370.0290.0290.1100.1104.2514.2900.2490.2490.0960.0960.3180.3160.1810.1850.2270.2240.2760.2740.1590.155
S122.5960.2480.2440.2390.2414.7674.8110.3390.3390.5770.5755.1555.1312.5272.5840.1040.1030.2900.2870.0330.032
S130.3420.0700.0690.0700.0702.6762.7000.1480.1480.1400.1401.5661.5590.8460.8650.3210.3160.1830.1820.0200.020
S144.3130.2570.2530.2870.2895.4545.5051.5351.5360.9640.9627.3757.3403.4713.5490.0780.0760.1240.1230.0530.052
S154.1680.2110.2080.2750.2765.8895.9430.4130.4140.4850.4845.1805.1552.2082.2570.0380.0370.0810.0810.0160.015
S163.9770.2350.2320.2390.2403.2733.3040.7830.7830.4730.4725.5155.4892.3252.3770.0400.0400.3190.3170.0130.012
S172.9220.1900.1880.2450.2472.9012.9280.2790.2790.6820.6814.6924.6701.8241.8650.0540.0540.1060.1060.0130.012
S182.1220.1710.1690.1590.1602.8222.8490.1120.1120.2010.2012.3162.3061.8621.9040.1120.1100.1650.1640.0250.024
S193.5530.2650.2610.2360.2385.1865.2340.5850.5860.4920.4917.7657.7282.5452.6020.0740.0730.0930.0920.0210.020
S201.4270.1040.1020.2200.2229.4089.4950.4410.4410.0460.0461.2571.2510.5210.5320.1700.1670.1060.1050.0500.049
S212.2540.1120.1110.3060.30812.58412.7010.3110.3110.2000.1991.2751.2690.5610.5730.1490.1470.1280.1270.0110.011
S223.4410.2390.2350.2600.2624.4534.4940.2560.2560.2620.2613.1133.0991.2001.5270.0580.0580.1480.1470.0260.025
S232.4740.1380.1360.3160.3178.0308.1040.1640.1650.2050.2051.4921.4850.6120.6260.1450.1430.1500.1490.0120.012
Mean2.5980.1650.1630.2340.2356.1156.1720.4780.4790.4840.4833.3593.3431.3821.4130.2100.2070.1720.1710.0570.056
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基于一测多评法的缘毛紫菀多成分含量测定及质量评价
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苏学燕 1 , 张英秀 1 , 赵日杂 1 , 蔡汶甫 1 , 张志锋 1, 2, *
药学学报 | 研究论文 2022,57(3): 775-782
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药学学报 | 研究论文 2022, 57(3): 775-782
基于一测多评法的缘毛紫菀多成分含量测定及质量评价
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苏学燕1, 张英秀1, 赵日杂1, 蔡汶甫1, 张志锋1, 2, *
作者信息
  • 1.西南民族大学药学院, 青藏高原研究院, 四川 成都 610041
  • 2.四川省羌彝药用资源保护与利用技术工程实验室, 四川 成都 610041

通讯作者:

*张志锋, Tel: 86-28-89165778, E-mail:
Quality evaluation of Aster souliei based on quantitative analysis of multi-components by single marker
Xue-yan SU1, Ying-xiu ZHANG1, Ri-za ZHAO1, Wen-pu CAI1, Zhi-feng ZHANG1, 2, *
Affiliations
  • 1. College of Pharmacy and Institute of Tibetan Plateau, Southwest Minzu University, Chengdu 610041, China
  • 2. Sichuan Provincial Qiang-Yi Medicinal Resources Protection and Utilization Technology Engineering Laboratory, Chengdu 610041, China
出版时间: 2022-03-12 doi: 10.16438/j.0513-4870.2021-1173
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摘要
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建立测定缘毛紫菀(Aster souliei Franch.) 多成分的一测多评法。采用超高效液相色谱法, 以绿原酸为内参物, 建立新绿原酸、隐绿原酸、芦丁、异槲皮苷、异绿原酸B、异绿原酸A、异绿原酸C、槲皮素、芹菜素、山柰酚10个成分的相对校正因子, 并同时采用外标法与一测多评法测定各成分的含量, 建立线性回归模型。采用系统聚类分析和正交偏最小二乘法-判别分析对23批缘毛紫菀进行质量评价。结果表明: 各校正因子重复性良好, 一测多评法测得23批缘毛紫菀中新绿原酸、隐绿原酸、芦丁、异槲皮苷、异绿原酸B、异绿原酸A、异绿原酸C、槲皮素、芹菜素、山柰酚的平均含量为0.165、0.234、6.115、0.478、0.484、3.359、1.382、0.210、0.172和0.057 mg·g-1; 外标法测得平均含量为0.163、0.235、6.172、0.479、0.483、3.343、1.413、0.207、0.171和0.056 mg·g-1, 两种方法各成分含量无显著性差异。聚类分析和正交偏最小二乘法-判别分析结果显示23批缘毛紫菀可以聚为两类, 这两类差异主要表现在咖啡酸类含量上, 第一类含量范围为0.873~5.647 mg·g-1, 第二类含量为8.524~16.705 mg·g-1。建立的一测多评法被证实是一种科学和缺少对照品的可替代方法, 可以简便快速评价缘毛紫菀的质量。

缘毛紫菀  /  一测多评法  /  相对校正因子  /  绿原酸  /  质量评价

To establish a quantitative analysis of multi-components by single marker (QAMS) for the determination of Aster souliei Franch., the relative correction factors (fx) of neochlorogenic acid, cryptochlorogenic acid, rutin, isoquercitrin, isochlorogenic acid B, isochlorogenic acid A, isochlorogenic acid C, quercetin, apigenin and kaempferol were established by ultra-high performance liquid chromatography with chlorogenic acid as internal reference. Meanwhile, the content of each component was determined by the external standard method (ESM) and QAMS, and a linear regression model was established to verify the feasibility and accuracy of the QAMS. Hierarchical clustering analysis (HCA) and orthogonal partial least square discriminate analysis (OPLS-DA) were used to evaluate the quality of 23 batches of A. souliei. The results showed that the repeatability of each fx was good. The average content of neochlorogenic acid, cryptochlorogenic acid, rutin, isoquercitrin, isochlorogenic acid B, isochlorogenic acid A, isochlorogenic acid C, quercetin, apigenin and kaempferol in 23 batches of A. souliei by QAMS was 0.165, 0.234, 6.115, 0.478, 0.484, 3.359, 1.382, 0.210, 0.172, and 0.057 mg·g-1, respectively. The mean content determined by the ESM method was 0.163, 0.235, 6.172, 0.479, 0.483, 3.343, 1.413, 0.207, 0.171, and 0.056 mg·g-1. The results of HCA and OPLS-DA analysis show that 23 batches of A. souliei can be divided into two groups based on caffeic acid content. The content of the first group was between 0.873 to 5.647 mg·g-1, while the second was between 8.524 to 16.705 mg·g-1. This QAMS method can be used to simply and quickly evaluate the quality A. souliei.

Aster souliei  /  quantitative analysis of multi-components by single marker  /  relative correction factor  /  chlorogenic acid  /  quality evaluation
苏学燕, 张英秀, 赵日杂, 蔡汶甫, 张志锋. 基于一测多评法的缘毛紫菀多成分含量测定及质量评价. 药学学报, 2022 , 57 (3) : 775 -782 . DOI: 10.16438/j.0513-4870.2021-1173
Xue-yan SU, Ying-xiu ZHANG, Ri-za ZHAO, Wen-pu CAI, Zhi-feng ZHANG. Quality evaluation of Aster souliei based on quantitative analysis of multi-components by single marker[J]. Acta Pharmaceutica Sinica, 2022 , 57 (3) : 775 -782 . DOI: 10.16438/j.0513-4870.2021-1173
正文
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藏紫菀是藏医药中常用的植物药之一, 在藏医药典籍《月王药诊》[1]、《四部医典》[2]和《晶珠本草》[3]等均有记载, 有清热解毒、镇咳祛痰的功效, 用于瘟疫病、中毒症、支气管炎、咳嗽气喘、咳吐脓血等疾病的治疗。《藏药志》[4]记载藏紫菀来源于菊科紫菀属和飞蓬属7种基原植物, 而缘毛紫菀是最为常用的一种, 主要分布于甘肃、青海、四川、云南、西藏等地, 海拔2 700~4 000 m[5, 6]。缘毛紫菀收录于《中华本草·藏药卷》[7]及《中华人民共和国卫生部药品标准》[8], 然而该标准仅收载了性状和粉末鉴别, 难以有效控制药材质量。现代研究表明, 缘毛紫菀主要含有黄酮类、有机酸类、二萜、三萜类等化合物[9, 10], 其中黄酮类和咖啡酸类化合物具有抗菌、抗肿瘤、镇咳、祛痰、平喘、抗病毒、抗氧化活性等药理作用, 是缘毛紫菀的主要活性成分[11, 12], 因此, 建立一种准确测定这些成分含量的分析方法, 对它们进行质量控制, 是评价缘毛紫菀药材资源品质的有效手段。
缘毛紫菀的质量和药效不是由单一化合物决定, 而是由多种成分共同发挥作用。目前, 多成分测定主要有两种方法, 一种是外标法(external standard method, ESM), 即使用标准品建立回归方程进行含量计算, ESM已被广泛应用于中药成分定量分析, 并在研究和质量评价领域发挥了重要作用[13]。然而, 近年来, 由于标准品高成本、中药多样性和复杂性、检测不稳定性等诸多不确定因素[14], 在质量监督评价中, 同时确定五种以上的组分尤为困难[15], ESM在某些情况下对中药质量的控制和评价具有挑战性。另一种方法只需要一个内参物就能同时测定多组分的含量, 称为一测多评法(quantitative analysis of multi-components by single marker, QAMS)[16], 采用QAMS对多组分进行定量分析, 不仅可以缩短检测时间和减少实验成本, 而且提高了方法的实用性, 更有效、全面地控制中药质量。QAMS已应用于黄芪[17]、五味子[18]、杜鹃[19]、人参及三七[20]等中药成分的测定, 此外, 也应用于制剂的含量测定[21-23]。相对校正因子(relative correction factor, fx) 在QAMS多组分同时测定中可作为不同实验室的一个常数, 是其多组分同时测定的主要决定因素[24]。
本研究建立QAMS同时测定缘毛紫菀中新绿原酸、绿原酸、隐绿原酸、芦丁、异槲皮苷、异绿原酸B、异绿原酸A、异绿原酸C、槲皮素、芹菜素、山柰酚等11种化学成分, QAMS的适用性和可行性主要由环境和fx的一致性决定, 如分析员、仪器和色谱柱等。以QAMS和ESM测定的结果作为自变量和因变量, 建立线性回归模型, 检验含量结果的偏差。采用系统聚类分析(hierarchical clustering analysis, HCA) 和正交偏最小二乘法-判别分析(orthogonal partial least square discriminate analysis, OPLS-DA) 对不同产地的23批缘毛紫菀进行质量评价。
材料与方法
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实验材料  实验药材23批缘毛紫菀均为作者实地采集, 来源信息见表 1, 经作者鉴定为菊科紫菀属缘毛紫菀(Aster souliei Franch.) 的干燥全草。
仪器   Waters CLASS超高效液相色谱仪(Waters公司, 美国); Agilent 1290超高效液相色谱仪(Agilent公司, 美国); LC-30A超高效液相色谱仪(Shimadzu公司, 日本)。Acquity UPLC HSS C18色谱柱、Acquity UPLC HSS CYANO色谱柱、Acquity UPLC HSS T3色谱柱、Acquity UPLC HSS C8色谱柱(2.1 mm × 100 mm, 1.8 μm) (Waters公司, 美国); Agilent proshell 120 C18色谱柱(2.1 mm × 100 mm, 1.8 μm) (Agilent公司, 美国); Inertsil WP300 C18色谱柱(2.1 mm × 100 mm, 1.8 μm) (Shimadzu公司, 日本)。
试剂和药品  新绿原酸(批号: 20042105)、绿原酸(批号: 20081301)、芦丁(批号: 19012405)、异绿原酸A (批号: 20102822)、异绿原酸B (批号: 20011801)、槲皮素(批号: 16010127)、山柰酚(批号: 19100101) 均购自成都康邦生物科技有限公司, 纯度≥ 98%; 芹菜素(批号: 20080706)、异绿原酸C (批号: 21012502)、隐绿原酸(批号: 20042403) 均购自四川省维克奇生物科技有限公司, 纯度≥ 98%; 异槲皮苷(批号: 18062702) 购自成都普菲德生物技术有限公司, 纯度≥ 98%, 色谱乙腈、甲酸(Sigma-Aldrich公司, 上海)。
色谱条件   流动相为乙腈(A) -0.1%甲酸水溶液(B), 梯度洗脱程序为0~5 min, 10%~16%A; 5~18 min, 16%~18%A; 18~26 min, 18%~24%A; 26~28 min, 24%~24%A; 28~35 min, 24%~30%A; 35~45 min, 30%~45%A。检测波长分别为254 nm (芦丁、异槲皮苷、槲皮素); 266 nm (山柰酚、芹菜素); 325 nm (新绿原酸、绿原酸、隐绿原酸、异绿原酸B、异绿原酸A、异绿原酸C), 体积流量0.2 mL·min-1, 柱温35 ℃, 进样量1 µL。
对照品溶液的制备   精密称取新绿原酸、绿原酸、隐绿原酸、芦丁、异槲皮苷、异绿原酸B、异绿原酸A、异绿原酸C、槲皮素、芹菜素、山柰酚对照品适量, 加甲醇定容, 摇匀, 得到质量浓度分别为28.80、345.10、49.80、340.00、92.00、230.90、364.01、122.15、33.50、47.20和40.80 µg·mL-1的混合对照品溶液。
供试品溶液的制备   取缘毛紫菀药材粉末约1.0 g, 精密称定, 置100 mL具塞锥形瓶中, 加入70%甲醇溶液20 mL, 摇匀, 超声提取30 min, 过滤, 取上清液, 过0.22 μm微孔滤膜, 即得供试品溶液。
方法学考察
系统适应性  取混合对照品溶液、供试品溶液, 分别注入液相色谱仪, 比较色谱图。
线性关系  分别吸取混合对照品溶液0.05、0.1、0.5、1、1.5、2 µL, 按“色谱条件”项下色谱条件进样测定。以峰面积为纵坐标(Y), 含量为横坐标(X), 进行线性回归分析。
精密度  精密吸取混合对照品溶液, 连续进样6次, 计算各成分峰面积RSD值。
重复性  称取粉末6份, 每份约1 g, 精密称定, 按照“供试品溶液的制备”项下方法制备成供试品溶液6份, 按照“色谱条件”项下条件, 各精密吸取1 μL测定, 计算各峰面积RSD值。
稳定性  称取粉末约1 g, 精密称定, 按照“供试品溶液的制备”项下方法制备, 分别于制备后0、4、8、12、24 h进样分析, 计算各成分峰面积RSD值。
加样回收率 称取样品粉末6份, 每份约0.5 g, 精密称定, 分别加入与待测成分含量相近的各对照品, 按“供试品溶液的制备”项下方法制备成供试品溶液, 按照“色谱条件”项下条件分别精密吸取1 μL测定, 并计算11个待测成分的加样回收率及RSD。
相对校正因子  为了得到每种化合物的相对校正因子(fx), 必须选择合适的内参物, 内参物应选择含量较高, 性质稳定, 较易获得, 药理作用明确的物质。绿原酸在供试品溶液中具有以上优点, 因此本实验选择绿原酸为内参物。取“对照品溶液的制备”项下混合对照品溶液, 按“色谱条件”项下色谱条件, 以绿原酸为内参物(s), 按公式(1) 分别计算测定化合物的fx, 按公式(2) 计算各化合物的含量。
$ {f}_{x}=\frac{{A}_{\mathrm{s}}/{W}_{\mathrm{s}}}{{A}_{x}/{W}_{x}} $
$ {W}_{x}=\frac{{A}_{x}\times {W}_{\mathrm{s}}}{{A}_{\mathrm{s}}}\times {f}_{x} $
式(1) 和(2) 中, As为内参物的峰面积; Ax为其余组分的峰面积; fx为相对校正因子; Wx为各化合物的含量; Ws为内参物的含量。
田口设计   为了评估fx的波动性和稳定性, 本文以6个色谱柱、3台液相色谱仪、3个分析员为控制因子, 进行田口设计。应用Minitab 17.0软件设计实验得到18组不同组合, 在Minitab 17.0软件对测定结果进行分析。
样品含量测定   将23批不同产地缘毛紫菀按“供试品溶液的制备”项下方法制备, 并按“色谱条件”项下进样检测, 采用QAMS以绿原酸为内参物, 应用fx计算新绿原酸、隐绿原酸、芦丁、异槲皮苷、异绿原酸B、异绿原酸A、异绿原酸C、槲皮素、芹菜素、山柰酚的含量, 并与ESM测得的含量结果建立回归模型进行差异分析。
质量评价  将23批缘毛紫菀11个化学成分的QAMS测定结果导入SIMCA14.1进行HCA和OPLS-DA, 对缘毛紫菀进行质量评价。
结果
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1 方法学考察
1.1 系统适应性
在已建立的色谱条件下, 供试品中11个待测成分色谱峰峰形良好, 与相邻峰的分离度大于1.5, 混合对照品及供试品的色谱图见图 1。
1.2 线性关系
回归方程见表 2, 结果表明11种成分在相应范围内线性关系良好。
1.3 精密度
新绿原酸、绿原酸、隐绿原酸、芦丁、异槲皮苷、异绿原酸B、异绿原酸A、异绿原酸C、槲皮素、芹菜素和山柰酚的RSD分别为0.78%、0.56%、0.77%、0.89%、0.67%、0.66%、0.59%、0.89%、0.64%、0.75%和0.84%, 结果表明该方法精密度良好。
1.4 重复性
新绿原酸、绿原酸、隐绿原酸、芦丁、异槲皮苷、异绿原酸B、异绿原酸A、异绿原酸C、槲皮素、芹菜素和山柰酚的RSD分别为0.73%、0.82%、0.34%、0.72%、0.59%、0.74%、1.35%、0.98%、1.54%、0.99%和1.06%, 结果表明该方法的重复性良好。
1.5 稳定性
新绿原酸、绿原酸、隐绿原酸、芦丁、异槲皮苷、异绿原酸B、异绿原酸A、异绿原酸C、槲皮素、芹菜素和山柰酚的RSD分别为0.63%、0.88%、2.22%、1.23%、1.26%、0.56%、1.89%、0.98%、1.04%、0.55%和0.76%, 结果表明供试品溶液在24 h内稳定。
1.6 加样回收率
新绿原酸、绿原酸、隐绿原酸、芦丁、异槲皮苷、异绿原酸B、异绿原酸A、异绿原酸C、槲皮素、芹菜素、山柰酚的加样回收率分别为99.98%、95.37%、102.46%、95.76%、88.89%、109.37%、113.46%、97.65%、94.74%、99.38%、99.69%, RSD分别为0.87%、0.45%、1.56%、1.09%、0.88%、0.61%、2.34%、0.78%、0.45%、1.38%、0.82%, 结果表明回收率均符合要求。
2 相对校正因子
2.1 田口设计
Minitab 17.0软件分析得平均值残差图(图 2), 由正太概率图(图 2A) 可知, 所有fx的数值点呈现为线性分布, 由拟合值(图 2B) 可知, 数据收集是合理和充分的, 并且过程设计是稳定的。从直方图(图 2C) 和残差与顺序图(图 2D) 可知, 所有结果都是随机分布的, 说明没有异常值。基于田口设计的结果, 可得色谱柱、仪器和分析员平均值的响应结果为0.016 0、0.009 7和0.005 3, 信噪比的响应结果为0.140 9、0.068 4和0.042 2, 从而得到色谱柱是最大的影响因素。
2.2 直观分析
不同色谱柱、仪器、分析员组合测得新绿原酸、隐绿原酸、芦丁、异槲皮苷、异绿原酸B、异绿原酸A、异绿原酸C、槲皮素、芹菜素和山柰酚的fx分别为0.789、1.055、0.607、0.873、0.886、1.153、1.015、1.154、0.935和0.995。RSD分别为0.65%、2.42%、1.59%、0.71%、2.26%、2.17%、2.04%、1.27%、2.06%和4.71%。可以看出, 所有10个成分fx值的RSD ≤ 5%, 表明不同仪器、色谱柱、分析员造成的误差都很小。
2.3 相关性分析
为了描述10个成分之间fx的相关性, 基于田口设计结果进行相关性分析。结果显示, 异绿原酸A和异绿原酸B相关系数为0.808, 这表明异绿原酸A和异绿原酸B的fx之间有很强的相关性, 这一结果与异绿原酸A和异绿原酸B非常相似的化合物结构一致。此外, 显著性在不同的化合物中很大, 如异绿原酸A (0.993) 和山柰酚(0.968) 等, 可能是由于溶剂效应、共轭效应、吸收带或电子跃迁等的影响。
2.4 方差分析
表 3的结果表明, 色谱柱、仪器和分析员的方差分析差异显著。色谱柱的影响比分析员和仪器的更大, 并且是最显著的因素, 尤其是对异绿原酸A和异绿原酸C应做好控制。
综上, fx更容易受到色谱柱的影响, 但是不同仪器、分析人员和色谱柱造成的误差很小, 可能是因为不同的仪器、色谱柱如灵敏度、积分或其他参数不同, 但如果在同一仪器和色谱柱中检测不同的化合物, fx的结果不会有很大差异。虽然fx在不同的分析人员、仪器或色谱柱下不可避免地会发生变化, 但这些变化不会对fx结果产生重大影响。通过控制色谱柱的参数, QAMS可应用于不同的实验室。
3 一测多评法(QAMS) 与外标法(ESM) 结果比较
用QAMS与ESM两种方法分别测定23批不同产地缘毛紫菀中新绿原酸、隐绿原酸、芦丁、异槲皮苷、异绿原酸B、异绿原酸A、异绿原酸C、槲皮素、芹菜素、山柰酚的含量, 结果见表 4, 以QAMS测定结果为自变量, ESM测定结果为因变量, 在两个变量之间建立线性回归模型, 回归模型中的所有系数均具有统计学意义, 为0.006, 模型得到R2分别为0.994 3、0.997 6、0.997 8、0.997 4、0.994 6、0.994 8、0.996 4、0.996 2、0.995 7和0.998 5, 表明两个变量之间具有显著相关性, QAMS和ESM测定结果没有显著性差异。用QAMS和ESM两种方法对缘毛紫菀中除绿原酸外的10种成分含量比较, 结果所有成分含量都位于线性y = 1.006 4x - 0.001 6附近(R2 = 0.999 9)。以绿原酸为内参物, 用QAMS计算的含量误差很小, 表明QAMS可用于不同实验室对缘毛紫菀中11种化合物的测定。
4 基于QAMS结果的缘毛紫菀质量评价
4.1 聚类分析
将以QAMS测定的23批不同产地缘毛紫菀11个化学成分含量作为变量, 导入SIMCA14.1进行HCA, 从图 3可知, 23批缘毛紫菀可以聚为两类, 其中S2、S3、S6、S8、S10、S11、S13、S20、S21、S23聚为第一类, 其余聚为第二类, 两类样品成分含量的差异可能缘于产地及生境的不同。
4.2 正交偏最小二乘法-判别分析
为了更清晰地得到样品组间和组内的关系, 有监督模式的OPLS-DA被执行, 由OPLS-DA得分图(图 4) 可知, 不同产地缘毛紫菀明显分为两类, 与聚类分析的结果一致。结果绿原酸、新绿原酸、异绿原酸A、异绿原酸C在两类样品中存在显著性差异, 四种成分均为咖啡酸类化合物, 第一类总咖啡酸含量范围为0.873~5.647 mg·g-1, 第二类为8.524~16.705 mg·g-1, 两类总咖啡酸含量相差较大, 说明产地对缘毛紫菀的咖啡酸类化合物具有显著影响。
讨论
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本研究前期通过单因素实验, 系统考察了不同提取方法、不同提取时间以及不同提取溶剂的提取效率, 以各待测成分含量为评价指标, 最终确定缘毛紫菀药材的最优提取方法为超声提取、提取时间为30 min、提取溶剂为70%甲醇。
中药成分复杂多样, 多成分含量测定已经成为中药质量评价的发展趋势。其中QAMS的提出, 通过建立成分之间的fx, 仅用一个对照品实现多个成分的定量, 实现了多指标中药成分的同时检测, 也提供了更好的中药质量评价新模型的分析方法, 已广泛得以应用。本实验采用QAMS同时测定来自不同产地缘毛紫菀药材中11个成分的含量, 与ESM含量测定结果相比无显著性差异, 根据11个化学成分含量对23批不同产地的缘毛紫菀进行质量评价, HCA和OPLS-DA结果显示, 23批不同产地缘毛紫菀共聚为两类, 两类总咖啡酸含量相差较大, 而咖啡酸类物质具有抗炎、抗癌和抗病毒等多种生物活性, 是一类具有广阔应用前景的天然活性成分[25], 因而咖啡酸类成分可以作为指标成分, 来控制缘毛紫菀的质量。综上, 本研究所建立的QAMS快速、简便、准确, 为缘毛紫菀药材的质量控制及临床合理应用提供了科学数据。
作者贡献: 苏学燕进行了本文方法建立、实验数据处理、撰写文章; 张英秀和赵日杂进行了实验设计及文章修改等; 蔡汶甫对本文的方法建立提供帮助; 张志锋从选题与设计、方法建立及文章修改提供了指导。
利益冲突: 无利益冲突。
基金
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  • 国家重点研发计划课题(2019YFC1712503)
  • 国家自然科学基金项目(31870314)
  • 四川省科技厅区域创新合作项目(2020YFQ0007)
  • 西南民族大学中央高校基本科研业务费专项资助(2021PTJS31)
文献
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2022年第57卷第3期
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doi: 10.16438/j.0513-4870.2021-1173
  • 接收时间：2021-08-30
  • 首发时间：2025-12-22
  • 出版时间：2022-03-12
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  • 收稿日期：2021-08-30
  • 修回日期：2021-11-19
基金
国家重点研发计划课题(2019YFC1712503)
国家自然科学基金项目(31870314)
四川省科技厅区域创新合作项目(2020YFQ0007)
西南民族大学中央高校基本科研业务费专项资助(2021PTJS31)
作者信息
    1.西南民族大学药学院, 青藏高原研究院, 四川 成都 610041
    2.四川省羌彝药用资源保护与利用技术工程实验室, 四川 成都 610041

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*张志锋, Tel: 86-28-89165778, E-mail:
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
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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