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Article(id=1239238138275615175, tenantId=1146029695717560320, journalId=1205117023404326918, issueId=1239238136711139764, articleNumber=null, orderNo=null, doi=10.16155/j.0254-1793.2023-0464, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1718208000000, revisedDateStr=2024-06-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1773386996095, onlineDateStr=2026-03-13, pubDate=1722355200000, pubDateStr=2024-07-31, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773386996095, onlineIssueDateStr=2026-03-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773386996095, creator=13701087609, updateTime=1773386996095, updator=13701087609, issue=Issue{id=1239238136711139764, tenantId=1146029695717560320, journalId=1205117023404326918, year='2024', volume='44', issue='7', pageStart='1105', pageEnd='1284', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773386995723, creator=13701087609, updateTime=1773387118529, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1239238651851370909, tenantId=1146029695717560320, journalId=1205117023404326918, issueId=1239238136711139764, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1239238651851370910, tenantId=1146029695717560320, journalId=1205117023404326918, issueId=1239238136711139764, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1176, endPage=1185, ext={EN=ArticleExt(id=1239238138560827855, articleId=1239238138275615175, tenantId=1146029695717560320, journalId=1205117023404326918, language=EN, title=Geographical origin traceability of Desmodium caudatum (Thunb.) DC. by UPLC MS/MS coupled with BP neural network, columnId=1239148838318043851, journalTitle=Chinese Journal of Pharmaceutical Analysis, columnName=Ingredient Analys, runingTitle=null, highlight=null, articleAbstract=
Objective:

To establish the UPLC-MS/MS method for simultaneous determination of 9 components (nicotinic acid, kaempferol, swertisin, quercetin, luteolin, rutin, vitexin, spinosin, salicylic acid) in Desmodium caudatum (Thunb.) DC. and construct a back propagation(BP) neural network model to predict the origin of Desmodium caudatum (Thunb.) DC. from different habitats.

Methods:

The chromatographic separation was achieved on an Agilent Zorbax SB C18 column (50 mm×3.0 mm，1.8 μm). The mobile phase consisted of methanol -0.1% acctic acid (containing 0.02 mol·L-1 ammonium acetate) at a flow rate of 0.3 mL·min-1 with gradient elution, the MS analysis were performed by multiple reaction monitoring (MRM) under ESI+ and ESI. A correlation analysis was conducted on the contents of each component, and a BP neural network model was constructed to distinguish Desmodium caudatum (Thunb.) DC. from different habitats.

Results:

Under the optimized conditions, 9 components(nicotinic acid, kaempferol, swertisin, quercetin, luteolin, rutin, vitexin, spinosin, salicylic acid) showed good linear relationships in the ranges of 0.388 8-38.88 ng·mL-1, 10.07-1 006.6 ng·mL-1, 34.22-34 221.6 ng·mL-1, 3.944-394.4 ng·mL-1, 2.124-212.4 ng·mL-1, 4.344-434.4 ng·mL-1, 46.50-4 650.1 ng·mL-1, 1.649-164.9 ng·mL-1, 4.880-488.0 ng·mL-1, respectively (r>0.995 1), whose average recoveries were 96.9%-103.9% (RSDs<1.9%). The contents of the above nine components in 40 batches of Desmodium caudatum (Thunb.) DC. were 1.657-7.407 μg·g-1, 15.801-64.488 μg·g-1, 1 068.348-4 270.780 μg·g-1, 10.608-123.228 μg·g-1, 3.897-16.802 μg·g-1, 1.269-97.834 μg·g-1, 405.285-1 955.796 μg·g-1, 13.614-36.124 μg·g-1, 4.417-87.509 μg·g-1, respectively. According to correlation analysis, four components (swertisin, rutin, spinosin, and luteolin) in Desmodium caudatum (Thunb.) DC. showed a highly linear positive correlation, indicating that these four components had a certain synergistic effect in Desmodium caudatum (Thunb.) DC.. The BP neural network model was constructed to predict Desmodium caudatum (Thunb.) DC. from different habitats, and the accuracy of the test set reached 92.3%.

Conclusion:

The method is simple, sensitive and efficient, and can be used for the rapid determination of the components in Desmodium caudatum (Thunb.) DC.. Using the BP neural network model to predict the habitats plays a significant role in tracing the origin of Desmodium caudatum (Thunb.) DC..

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目的：

建立超高效液相色谱-三重四极杆串联质谱（UPLC-MS/MS）法同时测定小槐花中9个成分（烟酸、山柰酚、当药黄素、槲皮素、木犀草素、芦丁、牡荆素、斯皮诺素、水杨酸）的含量并构建BP（back propagation）神经网络模型对不同产地的小槐花进行产地预测。

方法：

采用安捷伦ZORBAX SB-C18（50 mm×3.0 mm，1.8 μm）色谱柱，以0.1%乙酸（含0.02 mol·L-1乙酸铵）水溶液（A）-甲醇（B）为流动相，梯度洗脱，体积流量0.3 mL·min-1。质谱采用ESI正负离子检测模式，多反应监测模式（MRM）的扫描模式。测得各成分含量进行相关性分析，并构建BP神经网络模型用于进行不同产地的小槐花药材的溯源。

结果：

小槐花中烟酸、山柰酚、当药黄素、槲皮素、木犀草素、芦丁、牡荆素、斯皮诺素、水杨酸9个成分质量浓度分别在0.388 8~38.88、10.07~1 006.6、34.22~34 221.6、3.944~394.4、2.124~212.4、4.344~434.4、46.50~4 650.1、1.649~164.9、4.880~488.0 ng·mL-1范围内线性关系良好（r>0.995 1），平均加样回收率96.9%~103.9%，RSD均<1.9%。40批小槐花中烟酸、山柰酚、当药黄素、槲皮素、木犀草素、芦丁、牡荆素、斯皮诺素、水杨酸9个成分的含量分别为1.657~7.407、15.801~64.488、1 068.348~4 270.780、10.608~123.228、3.897~16.802、1.269~97.834、405.285~1 955.796、13.614~36.124、4.417~87.509 μg·g-1。通过相关性分析可知，当药黄素、芦丁、斯皮诺素、木犀草素4个成分相互呈高度线性正相关，表明小槐花中这4个成分具有一定互相协同的作用。构建BP神经网络模型用于预测不同产地的小槐花样品，检验集的正确率达到92.3%。

结论：

试验建立的方法简便、灵敏、高效，可用于小槐花成分的快速测定，结合BP神经网络模型对产地进行预测在小槐花产地的溯源中有一定作用。

, correspAuthors=符传武, authorNote=null, correspAuthorsNote=
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FTIR法结合ANNs鉴别普洱茶的产地和年限[J]. 中国城乡企业卫生， 2018， 33(10)： 175, articleTitle=FTIR法结合ANNs鉴别普洱茶的产地和年限, refAbstract=null), Reference(id=1239238152196509762, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, doi=null, pmid=null, pmcid=null, year=2018, volume=33, issue=10, pageStart=175, pageEnd=null, url=null, language=null, rfNumber=[15], rfOrder=28, authorNames=CAO AH, QIN P, LI S, journalName=Chin J Urban Rural Enterpr Hyg, refType=null, unstructuredReference=CAO AH， QIN P， LI S. Identification of the origin and age of Pu’er tea by FTIR combined with ANNs[J]. Chin J Urban Rural Enterpr Hyg， 2018， 33(10)： 175, articleTitle=Identification of the origin and age of Pu’er tea by FTIR combined with ANNs, refAbstract=null), Reference(id=1239238152313950281, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, doi=null, pmid=null, pmcid=null, year=2018, volume=39, issue=4, pageStart=45, pageEnd=null, url=null, language=null, rfNumber=[16], rfOrder=29, authorNames=朱志均, 周华英, 罗坤豪, journalName=自动化与信息工程, refType=null, unstructuredReference=朱志均， 周华英， 罗坤豪， 等. 基于机器嗅觉结合BP神经网络的砂仁气味鉴别方法[J]. 自动化与信息工程， 2018， 39(4)： 45, articleTitle=基于机器嗅觉结合BP神经网络的砂仁气味鉴别方法, refAbstract=null), Reference(id=1239238152448168016, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, doi=null, pmid=null, pmcid=null, year=2018, volume=39, issue=4, pageStart=45, pageEnd=null, url=null, language=null, rfNumber=[16], rfOrder=30, authorNames=ZHU ZJ, ZHOU HY, LUO KH, journalName=Autom Inf Eng, refType=null, unstructuredReference=ZHU ZJ， ZHOU HY， LUO KH， et al. Odor identification of Aromi Fructus based on machine olfactory combined with BP neural network[J]. Autom Inf Eng， 2018， 39(4)： 45, articleTitle=Odor identification of Aromi Fructus based on machine olfactory combined with BP neural network, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1239238139651346944, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, xref=null, ext=[AuthorCompanyExt(id=1239238139659735552, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, companyId=1239238139651346944, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Liuzhou Quality Inspection and Testing Research Center, Liuzhou 545001, China), AuthorCompanyExt(id=1239238139668124161, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, companyId=1239238139651346944, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=柳州市质量检验检测研究中心，柳州 545001)])], figs=[ArticleFig(id=1239238146399982374, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, language=EN, label=Fig.1, caption=Extracted ion chromatograms of various components from reference substance (A) and sample (B), figureFileSmall=Ywd9oQxlaCtDpqRnVW3dhg==, figureFileBig=LSPZUARF5j39FOdkhnI2yw==, tableContent=null), ArticleFig(id=1239238146479674153, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, language=CN, label=图1, caption=对照品(A)和样品(B)中各成分的提取离子流色谱图, figureFileSmall=Ywd9oQxlaCtDpqRnVW3dhg==, figureFileBig=LSPZUARF5j39FOdkhnI2yw==, tableContent=null), ArticleFig(id=1239238146727138099, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, language=EN, label=Fig.2, caption=Back propagation neural network, figureFileSmall=5+zITQmsGUSACgZtSlHN5A==, figureFileBig=f7J6aevgjisTK6/DzTxjqw==, tableContent=null), ArticleFig(id=1239238146831995705, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, language=CN, label=图2, caption=BP神经网络图谱, figureFileSmall=5+zITQmsGUSACgZtSlHN5A==, figureFileBig=f7J6aevgjisTK6/DzTxjqw==, tableContent=null), ArticleFig(id=1239238146945241922, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, language=EN, label=Tab.1, caption=

Informations of Desmodium caudatum (Thunb.) DC. collected in May 2022

, figureFileSmall=null, figureFileBig=null, tableContent=
编号(number)来源(source)
P1、P11、P21、P31广西桂林全州县安和镇(Anhe Town，Quanzhou County，Guilin，Guangxi)
P2、P12、P22、P32江西九江武宁县(Wuning County，Jiujiang，Jiangxi)
P3、P13、P23、P33广西柳州融水县(Rongshui County，Liuzhou，Guangxi)
P4、P14、P24、P34广西桂林全州县两河镇(Lianghe Town，Quanzhou County，Guilin，Guangxi)
P5、P15、P25、P35广西柳州三江县(Sanjiang County，Liuzhou，Guangxi)
P6、P16、P26、P36广西柳州柳城县(Liucheng County，Liuzhou，Guangxi)
P7、P17、P27、P37广西柳州融安县(Rong’an County，Liuzhou，Guangxi)
P8、P18、P28、P38广西柳州柳江区(Liujiang District，Liuzhou，Guangxi)
P9、P19、P29、P39江西九江修水县(Xiushui County，Jiujiang，Jiangxi)
P10、P20、P30、P40广西贵港平南县(Pingnan County，Guigang，Guangxi)
), ArticleFig(id=1239238147050099533, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, language=CN, label=表1, caption=

2022年5月收集的小槐花样品信息

, figureFileSmall=null, figureFileBig=null, tableContent=
编号(number)来源(source)
P1、P11、P21、P31广西桂林全州县安和镇(Anhe Town，Quanzhou County，Guilin，Guangxi)
P2、P12、P22、P32江西九江武宁县(Wuning County，Jiujiang，Jiangxi)
P3、P13、P23、P33广西柳州融水县(Rongshui County，Liuzhou，Guangxi)
P4、P14、P24、P34广西桂林全州县两河镇(Lianghe Town，Quanzhou County，Guilin，Guangxi)
P5、P15、P25、P35广西柳州三江县(Sanjiang County，Liuzhou，Guangxi)
P6、P16、P26、P36广西柳州柳城县(Liucheng County，Liuzhou，Guangxi)
P7、P17、P27、P37广西柳州融安县(Rong’an County，Liuzhou，Guangxi)
P8、P18、P28、P38广西柳州柳江区(Liujiang District，Liuzhou，Guangxi)
P9、P19、P29、P39江西九江修水县(Xiushui County，Jiujiang，Jiangxi)
P10、P20、P30、P40广西贵港平南县(Pingnan County，Guigang，Guangxi)
), ArticleFig(id=1239238147159151445, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, language=EN, label=Tab.2, caption=

MS parameters of various components

, figureFileSmall=null, figureFileBig=null, tableContent=
成分
(component)		tR/min离子模式
(ion mode)		母离子
(parent ion)(MS1)m/z		子离子
(daughter ion)(MS2)m/z		去簇电压
(declustering potential)/V		碰撞能量
(collision energy)/eV
烟酸(nicotinic acid)0.74[M+H] -124.180.0*/78.08526/30
山柰酚(kaempferol)8.41[M-H] -284.9116.9*/226.9-110-50/-43
当药黄素(swertisin)6.29[M-H] -445.1297.1*/282.0-110-45/-48
槲皮素(quercetin)7.70[M-H] -301.1150.7*/179.0-140-24/-24
木犀草素(luteolin)7.96[M-H] -285.1133.0*/151.0-145-35/-35
芦丁(rutin)6.44[M-H] -609.0270.9*/255.0-190-70/-70
牡荆素(vitexin)6.01[M-H] -431.2310.8*/282.9-160-30/-45
斯皮诺素(spinosin)6.18[M-H] -607.3427.0*/306.9-160-44/-51
水杨酸(salicylic acid)6.82[M-H] -136.993.0*/65.1-77-21/-35
), ArticleFig(id=1239238147276591968, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, language=CN, label=表2, caption=

各成分质谱参数

, figureFileSmall=null, figureFileBig=null, tableContent=
成分
(component)		tR/min离子模式
(ion mode)		母离子
(parent ion)(MS1)m/z		子离子
(daughter ion)(MS2)m/z		去簇电压
(declustering potential)/V		碰撞能量
(collision energy)/eV
烟酸(nicotinic acid)0.74[M+H] -124.180.0*/78.08526/30
山柰酚(kaempferol)8.41[M-H] -284.9116.9*/226.9-110-50/-43
当药黄素(swertisin)6.29[M-H] -445.1297.1*/282.0-110-45/-48
槲皮素(quercetin)7.70[M-H] -301.1150.7*/179.0-140-24/-24
木犀草素(luteolin)7.96[M-H] -285.1133.0*/151.0-145-35/-35
芦丁(rutin)6.44[M-H] -609.0270.9*/255.0-190-70/-70
牡荆素(vitexin)6.01[M-H] -431.2310.8*/282.9-160-30/-45
斯皮诺素(spinosin)6.18[M-H] -607.3427.0*/306.9-160-44/-51
水杨酸(salicylic acid)6.82[M-H] -136.993.0*/65.1-77-21/-35
), ArticleFig(id=1239238147398226796, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, language=EN, label=Tab.3, caption=

Regression equations and linear range

, figureFileSmall=null, figureFileBig=null, tableContent=
成分
(component)		回归方程
(regression equation)		线性范围
(linear range)/(ng·mL-1)		r检测限(LOD)/(ng·mL-1)定量限(LOQ)/(ng·mL-1)
烟酸(nicotinic acid)Y=1 184.914 9X+719.314 60.388 8~38.880.995 40.120.39
山柰酚(kaempferol)Y=266.073 3X-531.203 210.07~1 006.60.999 40.832.77
当药黄素(swertisin)Y=585.150 8X+2 442.127 034.22~34 221.60.999 40.591.95
槲皮素(quercetin)Y=2 580.691 5X-4 649.525 33.944~394.40.998 40.120.41
木犀草素(luteolin)Y=6 809.860 3X-12 457.627 82.124~212.40.997 80.030.10
芦丁(rutin)Y=428.933 2X+892.862 04.344~434.40.997 10.361.19
牡荆素(vitexin)Y=1 809.458 5X+58 677.518 346.50~4 650.10.997 60.491.63
斯皮诺素(spinosin)Y=509.256 2X+78.604 41.649~164.90.997 50.250.83
水杨酸(salicylic acid)Y=9 356.849 8X+169 423.977 54.880~488.00.995 11.073.56
), ArticleFig(id=1239238147469529971, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, language=CN, label=表3, caption=

线性回归方程及线性范围

, figureFileSmall=null, figureFileBig=null, tableContent=
成分
(component)		回归方程
(regression equation)		线性范围
(linear range)/(ng·mL-1)		r检测限(LOD)/(ng·mL-1)定量限(LOQ)/(ng·mL-1)
烟酸(nicotinic acid)Y=1 184.914 9X+719.314 60.388 8~38.880.995 40.120.39
山柰酚(kaempferol)Y=266.073 3X-531.203 210.07~1 006.60.999 40.832.77
当药黄素(swertisin)Y=585.150 8X+2 442.127 034.22~34 221.60.999 40.591.95
槲皮素(quercetin)Y=2 580.691 5X-4 649.525 33.944~394.40.998 40.120.41
木犀草素(luteolin)Y=6 809.860 3X-12 457.627 82.124~212.40.997 80.030.10
芦丁(rutin)Y=428.933 2X+892.862 04.344~434.40.997 10.361.19
牡荆素(vitexin)Y=1 809.458 5X+58 677.518 346.50~4 650.10.997 60.491.63
斯皮诺素(spinosin)Y=509.256 2X+78.604 41.649~164.90.997 50.250.83
水杨酸(salicylic acid)Y=9 356.849 8X+169 423.977 54.880~488.00.995 11.073.56
), ArticleFig(id=1239238147595359103, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, language=EN, label=Tab.4, caption=

Contents of 9 components in Desmodium caudatum (Thunb.) DC.

, figureFileSmall=null, figureFileBig=null, tableContent=
编号
(number)		含量(content)/(μg·g-1)
烟酸
(nicotinic acid)		山柰酚
(kaempferol)		当药黄素
(swertisin)		槲皮素
(quercetin)		木犀草素
(luteolin)		芦丁
(rutin)		牡荆素
(vitexin)		斯皮诺素
(spinosin)		水杨酸
(salicylic acid)
P13.25138.0481 973.538119.2524.8315.593881.31121.21077.765
P21.65715.8011 188.98310.6085.7301.309587.32020.55950.152
P33.86958.1133 553.19355.12311.61253.205523.62326.61534.567
P43.39941.6051 846.482119.8563.8975.535870.76720.13077.660
P54.97725.9812 536.52044.18810.66710.613802.79215.7795.780
P63.36855.5253 456.61244.07116.38949.417512.74327.41427.506
P76.18337.7152 427.93426.22513.4652.112951.32023.42519.258
P84.11046.9993 879.00841.19611.52660.597644.85329.63033.648
P92.07227.7681 091.07214.3546.0491.414405.28513.61460.074
P105.52026.8293 985.57541.44615.78695.6601 915.08736.03017.753
P113.28639.4521 941.048119.7544.8545.826881.65521.19287.509
P125.28716.6291 151.92613.9046.7603.838586.84120.91351.969
P133.71051.5163 547.85857.72714.34557.812496.65129.26035.136
P144.52337.0161 753.213119.4734.3926.044826.55122.29475.711
P157.40723.8112 367.05142.06311.56711.805989.63423.0634.703
P165.76146.6923 498.30942.12716.41349.318521.02631.68628.029
P176.97237.4692 400.52429.07814.4772.9741 224.37824.63221.109
P185.00747.0613 719.44239.76312.91865.381621.67230.52229.053
P192.11321.4471 334.54913.6805.6721.485441.57716.58167.043
P204.20328.4223 951.92941.28015.40797.8341 953.27336.12418.736
P213.04636.2761 904.203120.1094.8176.287879.97721.17878.656
P222.46719.7971 068.34813.4876.4483.174596.15421.43844.766
P232.91452.2323365.17650.79312.48149.175505.01024.00033.884
P242.31437.8372 054.485123.2283.9025.239906.54919.24474.957
P257.37029.2662 628.33742.31810.90513.5781 000.12722.4016.016
P262.52254.1943 489.61850.76616.80248.255522.23432.06427.935
P275.05135.7772 323.02230.81413.0542.8261 198.08423.53721.024
P283.34153.4693 847.58942.09211.98160.535656.74827.49132.084
P292.53024.9531 279.30014.6475.3381.269432.63817.87865.754
P306.61825.8383 996.65540.35315.71390.4241 955.79632.40520.319
P313.19644.2531 903.282118.3914.8326.255879.99021.19875.928
P321.80624.1351 183.43814.2236.9123.559591.50618.79748.851
P335.00164.4883 767.68257.04412.53252.816572.06826.69434.831
P343.37041.3351 738.601117.1054.2045.331881.37418.89282.487
P355.71526.1662 598.76245.68411.90613.3491 206.46819.8844.417
P363.67257.3843 447.15049.48215.52149.747518.45834.18032.782
P377.03045.8452 598.51030.59013.3323.6491 202.39022.37924.668
P383.99260.1844 081.64841.85612.35856.092657.72530.97539.910
P393.21025.9661 415.92117.5496.1771.810522.07817.45378.688
P405.35529.1904 270.78040.18216.41186.3261 869.36835.37519.722
), ArticleFig(id=1239238147712799623, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, language=CN, label=表4, caption=

小槐花中9个成分的含量(n=2)

, figureFileSmall=null, figureFileBig=null, tableContent=
编号
(number)		含量(content)/(μg·g-1)
烟酸
(nicotinic acid)		山柰酚
(kaempferol)		当药黄素
(swertisin)		槲皮素
(quercetin)		木犀草素
(luteolin)		芦丁
(rutin)		牡荆素
(vitexin)		斯皮诺素
(spinosin)		水杨酸
(salicylic acid)
P13.25138.0481 973.538119.2524.8315.593881.31121.21077.765
P21.65715.8011 188.98310.6085.7301.309587.32020.55950.152
P33.86958.1133 553.19355.12311.61253.205523.62326.61534.567
P43.39941.6051 846.482119.8563.8975.535870.76720.13077.660
P54.97725.9812 536.52044.18810.66710.613802.79215.7795.780
P63.36855.5253 456.61244.07116.38949.417512.74327.41427.506
P76.18337.7152 427.93426.22513.4652.112951.32023.42519.258
P84.11046.9993 879.00841.19611.52660.597644.85329.63033.648
P92.07227.7681 091.07214.3546.0491.414405.28513.61460.074
P105.52026.8293 985.57541.44615.78695.6601 915.08736.03017.753
P113.28639.4521 941.048119.7544.8545.826881.65521.19287.509
P125.28716.6291 151.92613.9046.7603.838586.84120.91351.969
P133.71051.5163 547.85857.72714.34557.812496.65129.26035.136
P144.52337.0161 753.213119.4734.3926.044826.55122.29475.711
P157.40723.8112 367.05142.06311.56711.805989.63423.0634.703
P165.76146.6923 498.30942.12716.41349.318521.02631.68628.029
P176.97237.4692 400.52429.07814.4772.9741 224.37824.63221.109
P185.00747.0613 719.44239.76312.91865.381621.67230.52229.053
P192.11321.4471 334.54913.6805.6721.485441.57716.58167.043
P204.20328.4223 951.92941.28015.40797.8341 953.27336.12418.736
P213.04636.2761 904.203120.1094.8176.287879.97721.17878.656
P222.46719.7971 068.34813.4876.4483.174596.15421.43844.766
P232.91452.2323365.17650.79312.48149.175505.01024.00033.884
P242.31437.8372 054.485123.2283.9025.239906.54919.24474.957
P257.37029.2662 628.33742.31810.90513.5781 000.12722.4016.016
P262.52254.1943 489.61850.76616.80248.255522.23432.06427.935
P275.05135.7772 323.02230.81413.0542.8261 198.08423.53721.024
P283.34153.4693 847.58942.09211.98160.535656.74827.49132.084
P292.53024.9531 279.30014.6475.3381.269432.63817.87865.754
P306.61825.8383 996.65540.35315.71390.4241 955.79632.40520.319
P313.19644.2531 903.282118.3914.8326.255879.99021.19875.928
P321.80624.1351 183.43814.2236.9123.559591.50618.79748.851
P335.00164.4883 767.68257.04412.53252.816572.06826.69434.831
P343.37041.3351 738.601117.1054.2045.331881.37418.89282.487
P355.71526.1662 598.76245.68411.90613.3491 206.46819.8844.417
P363.67257.3843 447.15049.48215.52149.747518.45834.18032.782
P377.03045.8452 598.51030.59013.3323.6491 202.39022.37924.668
P383.99260.1844 081.64841.85612.35856.092657.72530.97539.910
P393.21025.9661 415.92117.5496.1771.810522.07817.45378.688
P405.35529.1904 270.78040.18216.41186.3261 869.36835.37519.722
), ArticleFig(id=1239238147817657228, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, language=EN, label=Tab.5, caption=

Correlation analysis of components

, figureFileSmall=null, figureFileBig=null, tableContent=
成分
(component)		烟酸
(nicotinic acid)		山柰酚
(kaempferol)		当药黄素
(swertisin)		槲皮素
(quercetin)		木犀草素
(luteolin)		芦丁
(rutin)		牡荆素
(vitexin)		斯皮诺素
(spinosin)		水杨酸
(salicylic acid)
烟酸(nicotinic acid)1-0.0250.381-0.1500.531**0.1950.530**0.314-0.657**
山柰酚(kaempferol)-0.02510.595**0.3110.3690.367-0.2950.421**-0.044
当药黄素(swertisin)0.3810.595**1-0.0480.829**0.905**0.3510.874**-0.599**
槲皮素(quercetin)-0.1500.311-0.0481-0.419**-0.1310.077-0.0930.555**
木犀草素(luteolin)0.531**0.3690.829**-0.419**10.726**0.3450.793**-0.838**
芦丁(rutin)0.1950.3670.905**-0.1310.726**10.412**0.898**-0.474**
牡荆素(vitexin)0.530**-0.2950.3510.0770.3450.412**10.426**-0.358
斯皮诺素(spinosin)0.3140.421**0.874**-0.0930.793**0.898**0.426**1-0.495**
水杨酸(salicylic acid)-0.657**-0.044-0.599**0.555**-0.838**-0.474**-0.358-0.495**1
), ArticleFig(id=1239238147905737620, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, language=CN, label=表5, caption=

各成分相关性分析

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成分
(component)		烟酸
(nicotinic acid)		山柰酚
(kaempferol)		当药黄素
(swertisin)		槲皮素
(quercetin)		木犀草素
(luteolin)		芦丁
(rutin)		牡荆素
(vitexin)		斯皮诺素
(spinosin)		水杨酸
(salicylic acid)
烟酸(nicotinic acid)1-0.0250.381-0.1500.531**0.1950.530**0.314-0.657**
山柰酚(kaempferol)-0.02510.595**0.3110.3690.367-0.2950.421**-0.044
当药黄素(swertisin)0.3810.595**1-0.0480.829**0.905**0.3510.874**-0.599**
槲皮素(quercetin)-0.1500.311-0.0481-0.419**-0.1310.077-0.0930.555**
木犀草素(luteolin)0.531**0.3690.829**-0.419**10.726**0.3450.793**-0.838**
芦丁(rutin)0.1950.3670.905**-0.1310.726**10.412**0.898**-0.474**
牡荆素(vitexin)0.530**-0.2950.3510.0770.3450.412**10.426**-0.358
斯皮诺素(spinosin)0.3140.421**0.874**-0.0930.793**0.898**0.426**1-0.495**
水杨酸(salicylic acid)-0.657**-0.044-0.599**0.555**-0.838**-0.474**-0.358-0.495**1
), ArticleFig(id=1239238147972846491, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, language=EN, label=Tab.6, caption=

The BP neural network calculate results of 13 test sets of Desmodium caudatum (Thunb.) DC.

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编号
(number)		产地
(habitat)		概率(probability)预测产地
(habitat prediction)		结论
(conclusion)
产地1
(habitat 1)		产地2
(habitat 2)		产地3
(habitat 3)		产地4
(habitat 4)		产地5
(habitat 5)		产地6
(habitat 6)		产地7
(habitat 7)		产地8
(habitat 8)		产地9
(habitat 9)		产地10
(habitat 10)
P110.9980.0000.0000.0010.0000.0000.0000.0000.0000.0001正确(correct)
P330.0000.0001.0000.0000.0000.0000.0000.0000.0000.0003正确(correct)
P10100.0000.0000.0000.0000.0000.0010.0000.0000.0000.99810正确(correct)
P1440.2290.0000.0000.7700.0000.0000.0000.0000.0000.0004正确(correct)
P1770.0040.0000.0000.0000.0000.0000.9960.0000.0000.0007正确(correct)
P1880.0000.0000.0010.0000.0000.0000.0000.9990.0000.0018正确(correct)
P20100.0000.0000.0000.0000.0000.0010.0000.0000.0000.99810正确(correct)
P2220.0001.0000.0000.0000.0000.0000.0000.0000.0000.0002正确(correct)
P2330.0000.0001.0000.0000.0000.0000.0000.0000.0000.0003正确(correct)
P2440.0670.0000.0000.9320.0000.0000.0000.0000.0000.0004正确(correct)
P2550.0000.0010.0000.0000.9990.0000.0000.0000.0000.0005正确(correct)
P2770.0060.0000.0000.0000.0000.0000.9940.0000.0000.0007正确(correct)
P3880.0000.0000.8420.0000.0000.0020.0000.1550.0000.0003错误(error)
), ArticleFig(id=1239238148052538273, tenantId=1146029695717560320, journalId=1205117023404326918, articleId=1239238138275615175, language=CN, label=表6, caption=

BP神经网络对小槐花13个样品检验集的预测结果

, figureFileSmall=null, figureFileBig=null, tableContent=
编号
(number)		产地
(habitat)		概率(probability)预测产地
(habitat prediction)		结论
(conclusion)
产地1
(habitat 1)		产地2
(habitat 2)		产地3
(habitat 3)		产地4
(habitat 4)		产地5
(habitat 5)		产地6
(habitat 6)		产地7
(habitat 7)		产地8
(habitat 8)		产地9
(habitat 9)		产地10
(habitat 10)
P110.9980.0000.0000.0010.0000.0000.0000.0000.0000.0001正确(correct)
P330.0000.0001.0000.0000.0000.0000.0000.0000.0000.0003正确(correct)
P10100.0000.0000.0000.0000.0000.0010.0000.0000.0000.99810正确(correct)
P1440.2290.0000.0000.7700.0000.0000.0000.0000.0000.0004正确(correct)
P1770.0040.0000.0000.0000.0000.0000.9960.0000.0000.0007正确(correct)
P1880.0000.0000.0010.0000.0000.0000.0000.9990.0000.0018正确(correct)
P20100.0000.0000.0000.0000.0000.0010.0000.0000.0000.99810正确(correct)
P2220.0001.0000.0000.0000.0000.0000.0000.0000.0000.0002正确(correct)
P2330.0000.0001.0000.0000.0000.0000.0000.0000.0000.0003正确(correct)
P2440.0670.0000.0000.9320.0000.0000.0000.0000.0000.0004正确(correct)
P2550.0000.0010.0000.0000.9990.0000.0000.0000.0000.0005正确(correct)
P2770.0060.0000.0000.0000.0000.0000.9940.0000.0000.0007正确(correct)
P3880.0000.0000.8420.0000.0000.0020.0000.1550.0000.0003错误(error)
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基于UPLC-MS/MS法结合BP神经网络模型在小槐花产地溯源中的应用
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杨婧 , 符传武 * , 覃华亮 , 覃冬杰 , 覃子龙
药物分析杂志 | 成分分析 2024,44(7): 1176-1185
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药物分析杂志 | 成分分析 2024, 44(7): 1176-1185
基于UPLC-MS/MS法结合BP神经网络模型在小槐花产地溯源中的应用
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杨婧 , 符传武* , 覃华亮, 覃冬杰, 覃子龙
作者信息
  • 柳州市质量检验检测研究中心，柳州 545001

    • Tel：13557329896；E-mail：

通讯作者:

* Tel：18007726258；E-mail：
Geographical origin traceability of Desmodium caudatum (Thunb.) DC. by UPLC MS/MS coupled with BP neural network
Jing YANG , Chuan-wu FU* , Hua-liang QIN, Dong-jie QIN, Zi-long QIN
Affiliations
  • Liuzhou Quality Inspection and Testing Research Center, Liuzhou 545001, China
出版时间: 2024-07-31 doi: 10.16155/j.0254-1793.2023-0464
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摘要
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目的：

建立超高效液相色谱-三重四极杆串联质谱（UPLC-MS/MS）法同时测定小槐花中9个成分（烟酸、山柰酚、当药黄素、槲皮素、木犀草素、芦丁、牡荆素、斯皮诺素、水杨酸）的含量并构建BP（back propagation）神经网络模型对不同产地的小槐花进行产地预测。

方法：

采用安捷伦ZORBAX SB-C18（50 mm×3.0 mm，1.8 μm）色谱柱，以0.1%乙酸（含0.02 mol·L-1乙酸铵）水溶液（A）-甲醇（B）为流动相，梯度洗脱，体积流量0.3 mL·min-1。质谱采用ESI正负离子检测模式，多反应监测模式（MRM）的扫描模式。测得各成分含量进行相关性分析，并构建BP神经网络模型用于进行不同产地的小槐花药材的溯源。

结果：

小槐花中烟酸、山柰酚、当药黄素、槲皮素、木犀草素、芦丁、牡荆素、斯皮诺素、水杨酸9个成分质量浓度分别在0.388 8~38.88、10.07~1 006.6、34.22~34 221.6、3.944~394.4、2.124~212.4、4.344~434.4、46.50~4 650.1、1.649~164.9、4.880~488.0 ng·mL-1范围内线性关系良好（r>0.995 1），平均加样回收率96.9%~103.9%，RSD均<1.9%。40批小槐花中烟酸、山柰酚、当药黄素、槲皮素、木犀草素、芦丁、牡荆素、斯皮诺素、水杨酸9个成分的含量分别为1.657~7.407、15.801~64.488、1 068.348~4 270.780、10.608~123.228、3.897~16.802、1.269~97.834、405.285~1 955.796、13.614~36.124、4.417~87.509 μg·g-1。通过相关性分析可知，当药黄素、芦丁、斯皮诺素、木犀草素4个成分相互呈高度线性正相关，表明小槐花中这4个成分具有一定互相协同的作用。构建BP神经网络模型用于预测不同产地的小槐花样品，检验集的正确率达到92.3%。

结论：

试验建立的方法简便、灵敏、高效，可用于小槐花成分的快速测定，结合BP神经网络模型对产地进行预测在小槐花产地的溯源中有一定作用。

小槐花  /  超高效液相色谱-三重四极杆串联质谱  /  相关性分析  /  BP神经网络模型  /  烟酸  /  山柰酚  /  当药黄素  /  槲皮素  /  木犀草素  /  芦丁  /  牡荆素  /  斯皮诺素  /  水杨酸
Objective:

To establish the UPLC-MS/MS method for simultaneous determination of 9 components (nicotinic acid, kaempferol, swertisin, quercetin, luteolin, rutin, vitexin, spinosin, salicylic acid) in Desmodium caudatum (Thunb.) DC. and construct a back propagation(BP) neural network model to predict the origin of Desmodium caudatum (Thunb.) DC. from different habitats.

Methods:

The chromatographic separation was achieved on an Agilent Zorbax SB C18 column (50 mm×3.0 mm，1.8 μm). The mobile phase consisted of methanol -0.1% acctic acid (containing 0.02 mol·L-1 ammonium acetate) at a flow rate of 0.3 mL·min-1 with gradient elution, the MS analysis were performed by multiple reaction monitoring (MRM) under ESI+ and ESI. A correlation analysis was conducted on the contents of each component, and a BP neural network model was constructed to distinguish Desmodium caudatum (Thunb.) DC. from different habitats.

Results:

Under the optimized conditions, 9 components(nicotinic acid, kaempferol, swertisin, quercetin, luteolin, rutin, vitexin, spinosin, salicylic acid) showed good linear relationships in the ranges of 0.388 8-38.88 ng·mL-1, 10.07-1 006.6 ng·mL-1, 34.22-34 221.6 ng·mL-1, 3.944-394.4 ng·mL-1, 2.124-212.4 ng·mL-1, 4.344-434.4 ng·mL-1, 46.50-4 650.1 ng·mL-1, 1.649-164.9 ng·mL-1, 4.880-488.0 ng·mL-1, respectively (r>0.995 1), whose average recoveries were 96.9%-103.9% (RSDs<1.9%). The contents of the above nine components in 40 batches of Desmodium caudatum (Thunb.) DC. were 1.657-7.407 μg·g-1, 15.801-64.488 μg·g-1, 1 068.348-4 270.780 μg·g-1, 10.608-123.228 μg·g-1, 3.897-16.802 μg·g-1, 1.269-97.834 μg·g-1, 405.285-1 955.796 μg·g-1, 13.614-36.124 μg·g-1, 4.417-87.509 μg·g-1, respectively. According to correlation analysis, four components (swertisin, rutin, spinosin, and luteolin) in Desmodium caudatum (Thunb.) DC. showed a highly linear positive correlation, indicating that these four components had a certain synergistic effect in Desmodium caudatum (Thunb.) DC.. The BP neural network model was constructed to predict Desmodium caudatum (Thunb.) DC. from different habitats, and the accuracy of the test set reached 92.3%.

Conclusion:

The method is simple, sensitive and efficient, and can be used for the rapid determination of the components in Desmodium caudatum (Thunb.) DC.. Using the BP neural network model to predict the habitats plays a significant role in tracing the origin of Desmodium caudatum (Thunb.) DC..

Desmodium caudatum (Thunb.) DC.  /  UPLC-MS/MS  /  correlation analysis  /  BP neural network model  /  nicotinic acid  /  kaempferol  /  swertisin  /  quercetin  /  luteolin  /  rutin  /  vitexin  /  spinosin  /  salicylic acid
杨婧, 符传武, 覃华亮, 覃冬杰, 覃子龙. 基于UPLC-MS/MS法结合BP神经网络模型在小槐花产地溯源中的应用. 药物分析杂志, 2024 , 44 (7) : 1176 -1185 . DOI: 10.16155/j.0254-1793.2023-0464
Jing YANG, Chuan-wu FU, Hua-liang QIN, Dong-jie QIN, Zi-long QIN. Geographical origin traceability of Desmodium caudatum (Thunb.) DC. by UPLC MS/MS coupled with BP neural network[J]. Chinese Journal of Pharmaceutical Analysis, 2024 , 44 (7) : 1176 -1185 . DOI: 10.16155/j.0254-1793.2023-0464
正文
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小槐花为豆科山蚂蝗属植物小槐花Desmodium caudatum (Thunb.) DC.的全株，含有多种有机酸，如机酸类水杨酸、维生素B系列化合物烟酸等[1]及各类黄酮类物质，如芦丁、山柰酚、当药黄素等[2-6]。这些黄酮化合物具有抗氧化、抗炎、抗菌、调节免疫功能等作用，对改善微循环、保护血管壁、降低血脂、抗过敏等方面有一定作用。此外，小槐花还具有降压、降糖、抗凝血、止咳、解热、利尿等作用。研究也发现小槐花具有一定的抗肿瘤活性和抗病毒活性[6-9]。
现今，大量的研究者投入到中药材产地溯源研究中，很好地分辨药材的产地对于识别未知产地的药材是否为道地药材及将未知产地的药材进行产地区分具有很重要的作用。以往研究者通过对药材的性状、显微鉴别来判断中药材是否来源它的优势产地，BP神经网络模型的引入，被大量应用于矿产及中草药的产地溯源中。BP神经网络，即反向传播神经网络(back propagation neural network)，是一种常用的人工神经网络模型。它是由多层神经元组成的前馈网络，其中信息从输入层经过隐藏层传递到输出层[10-16]。通过对大量样品数据的分析由计算机对样品进行神经网络的反向传播算法，训练已知产地的小槐花样品并调整网络的权重和偏置，使得模型能够对未知产地的小槐花进行有效的分类和预测产地。此前，王虹等[10]曾利用检测铁矿的无机元素通过BP神经网络模型对铁矿产地进行溯源，彭政等[11]曾利用来自不同产地的陈皮中的无机元素对陈皮的产地进行溯源，运用BP神经网络模型有效地预测出未知产地的陈皮样品属于哪一产地。
本试验运用小槐花的9个成分作为BP神经网络的输入层，有效防止因为无法测出不同产地的无机元素或因其他人为因素导致无机元素分布差异而造成产地预测误差。同时也较传统主观鉴别手段从性状、显微鉴别等方面的检验方法多了大量的数据支持及模型对样品随机训练，减少了主观判断对药材鉴别的影响，为药材产地溯源引进了新的思路。
1　材料
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1.1　仪器
LC-30AD超高效液相色谱仪(岛津公司)；Triple Quad 4500三重四极杆质谱仪(AB SCIEX公司)；XS205DU十万分之一电子分析天平(梅特勒-托利多公司)；AE200S万分之一电子分析天平(梅特勒-托利多公司)；SK8200HP超声波清洗器(上海科导超声仪器有限公司)；GWB-2E超纯水仪(北京普析通用仪器有限责任公司)；FW100高速粉碎机(天津市泰斯特仪器有限公司)
1.2　试药
对照品烟酸(批号100434-200301，含量100%)、山柰酚(批号110861-201310，含量93.2%)、槲皮素(批号100081-201610，含量99.1%)、木犀草素(批号111520-200504，含量100%)、芦丁(批号100080-200707，含量90.5%)、牡荆素(批号111687-201704，含量94.9%)、斯皮诺素(批号111869-201704，含量97.2%)均购自中国食品药品检定研究院，当药黄素(批号7832，含量98.0%)购自NATURE STANDARD公司，水杨酸(批号21051035，含量99.6%)购自TMstandard公司。甲醇、乙腈为色谱纯，均购自欧姆尼科技有限公司，其他试剂均为分析纯，水为自制超纯水。
1.3　材料
小槐花药材分别收集于广西桂林全州县安和镇、江西九江武宁县、广西柳州融水县、广西桂林全州县两河镇、广西柳州三江县、广西柳州柳城县、广西柳州融安县、广西柳州柳江区、江西九江修水县、广西贵港平南县10个地区，共40批，见表1。所有样品经柳州市质量检验检测研究中心药品科副主任药师覃华亮鉴定为豆科山蚂蟥属植物小槐花Desmodium caudatum (Thunb.) DC.的全株。样品经过高速粉碎机打粉过后过2号筛，在干燥条件下贮存备用。
2　方法与结果
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2.1　色谱-质谱条件
2.1.1　色谱条件
采用安捷伦ZORBAX SB-C18(50 mm×3.0 mm，1.8 μm)色谱柱，以0.1%乙酸(含0.02 mol·L-1乙酸铵)水溶液(A)-甲醇(B)为流动相，梯度洗脱(0~7 min，2%B→60%B；7~8 min，60%B；8~9 min，60%B→2%B；9~12 min，2%B)，体积流量0.3 mL·min-1，进样量1 μL。
2.1.2　质谱条件
采用电喷雾离子源(ESI)，正负离子模式检测，离子化电压±4.5 kV，离子源温度500 ℃，气帘气压力68 948 Pa，喷雾气压力379 211 Pa，辅助加热气压力379 211 Pa，碰撞气为氮气，多反应监测模式(MRM)。具体参数见表2，各成分的提取离子流色谱图见图1。
2.2　混合对照品溶液的制备
精密称取烟酸、山柰酚、当药黄素、槲皮素、木犀草素、芦丁、牡荆素、斯皮诺素、水杨酸的对照品适量，分别置于100 mL棕色量瓶中，加甲醇溶解并稀释至刻度，摇匀，即得上述各对照品的储备液。再精密量取各储备液适量，置于同一100 mL棕色量瓶中，随后加甲醇稀释至刻度并摇匀，即得含烟酸38.88 ng·mL-1、山柰酚1 006.6 ng·mL-1、当药黄素34 221.6 ng·mL-1、槲皮素394.4 ng·mL-1、木犀草素212.4 ng·mL-1、芦丁434.4 ng·mL-1、牡荆素4 650.1 ng·mL-1、斯皮诺素164.9 ng·mL-1、水杨酸488.0 ng·mL-1的混合对照品溶液。
2.3　供试品溶液的制备
取小槐花粉末约0.25 g，精密称定，置100 mL棕色量瓶中，精密加入甲醇-25%盐酸(4∶1，v/v)适量，超声(频率53 kHz，功率500 W)45 min，放冷，用甲醇-25%盐酸(4∶1，v/v)稀释至刻度摇匀，取上清液2 mL，用0.22 μm微孔滤膜过滤，即得。
2.4　检测限和定量限
精密量取混合对照品溶液0.8 mL，置10 mL棕色量瓶中，加入甲醇稀释至刻度，摇匀，按“2.1”项下条件进样分析，记录色谱图，并分别以信噪比为3∶1和10∶1计算检测限和定量限，结果见表3。
2.5　线性关系考察
精密吸取各对照品储备液0.08 mL(烟酸)、0.4 mL(山柰酚)、6 mL(当药黄素)、0.2 mL(槲皮素)、0.12 mL(木犀草素)、0.2 mL(芦丁)、2 mL(牡荆素)、0.08 mL(斯皮诺素)、0.2 mL(水杨酸)，置于同一100 mL棕色量瓶中，并用甲醇稀释至刻度，摇匀，配制成混合对照品工作溶液；再取混合对照品工作溶液15、10、5、2.5、0.2 mL，分别置于20 mL棕色量瓶中，用甲醇稀释至刻度，摇匀，即得系列混合对照品工作溶液。取上述系列混合对照品工作溶液，按“2.1”项下条件进样测定，以质量浓度(X)为横坐标，峰面积(Y)为纵坐标，进行线性回归。结果见表3，9个成分在相应的浓度范围内线性关系良好。
2.6　精密度考察
取小槐花样品粉末(编号P6)，按“2.3”项下方法制备供试品溶液，按“2.1”项下条件连续进样6次，记录色谱图，计算烟酸、山柰酚、当药黄素、槲皮素、木犀草素、芦丁、牡荆素、斯皮诺素、水杨酸峰面积的RSD分别为0.18%、0.31%、0.55%、0.91%、1.2%、0.65%、1.3%、1.2%、0.46%，表明精密度较好。
2.7　稳定性考察
取小槐花样品粉末(编号P6)，按“2.3”项下方法制备供试品溶液，分别于溶液制备后0、2、4、6、8、12 h按“2.1”项下条件进样测定，记录色谱图，计算烟酸、山柰酚、当药黄素、槲皮素、木犀草素、芦丁、牡荆素、斯皮诺素、水杨酸峰面积的RSD分别为0.38%、2.0%、0.66%、0.87%、1.2%、0.43%、0.83%、0.61%、0.50%，表明供试品溶液中上述成分在12 h内具有较好的稳定性。
2.8　重复性考察
取小槐花样品粉末(编号P6)，按“2.3”项下的方法制备供试品溶液6份，按“2.1”项下条件进样测定，记录色谱图及峰面积，计算烟酸、山柰酚、当药黄素、槲皮素、木犀草素、芦丁、牡荆素、斯皮诺素、水杨酸的平均含量分别为3.384、55.645、3 457.679、44.028、16.156、49.412、516.661、27.309、27.278 μg·g-1，RSD分别为0.24%、0.15%、0.16%、0.17%、0.25%、0.44%、0.33%、0.17%、0.41%。表明该方法的重复性良好。
2.9　加样回收率试验
精密称取已知含量的小槐花粉末(编号P6)6份，每份0.125 g，分别置于100 mL棕色量瓶中，精密加入混合对照品溶液(精密称取烟酸、山柰酚、当药黄素、槲皮素、木犀草素、芦丁、牡荆素、斯皮诺素、水杨酸的对照品适量，加甲醇制成上述成分质量浓度分别为427.68、3 523.1、239 551.2、2 760.8、1 062.0、3 040.8、32 550.7、1 731.45、1 708.0 ng·mL-1的混合溶液)适量，按“2.3”项下方法制备供试溶液，按“2.1”项下条件进样测定，记录各成分峰面积并结合加入各成分的量计算加样回收率，结果烟酸、山柰酚、当药黄素、槲皮素、木犀草素、芦丁、牡荆素、斯皮诺素、水杨酸的回收率分别为101.1%、97.8%、102.2%、103.9%、96.9%、102.7%、102.3%、100.9%、100.4%，RSD分别为1.1%、1.7%、0.70%、1.9%、1.7%、1.4%、1.3%、1.8%、0.99%，表明本方法回收率良好。
2.10　样品含量测定
按照“2.3”项下方法制备40批不同产地的小槐花的供试品溶液各2份，精密吸取各供试品溶液1 μL，在“2.1”项条件下进行测定，结果见表4。
2.11　小槐花各成分间的相关性分析
采用SIMCA 14.1软件对40个不同来源的小槐花中的烟酸、山柰酚、当药黄素、槲皮素、木犀草素、芦丁、牡荆素、斯皮诺素、水杨酸这9个成分含量进行相关性分析，Pearson相关系数结果见表5。由表5可知，Pearson相关系数>0.7，则2个成分之间呈高度线性正相关，小槐花中当药黄素、芦丁、斯皮诺素、木犀草素这4个成分相互呈高度线性正相关，表明小槐花中这4个成分具有一定互相协同的作用。
2.12　BP神经网络建模
分别将10个来源的共40批的样品导入IBM SPSS Statistics 23统计分析软件中随机分为训练集与检验集。并通过软件随机分区，产生了27个训练集及13个检验集。将10个来源不同的小槐花中10个产地依次赋值1~10作为神经网络模型的输出值，分别为广西桂林全州县安和镇、江西九江武宁县、广西柳州融水县、广西桂林全州县两河镇、广西柳州三江县、广西柳州柳城县、广西柳州融安县、广西柳州柳江区、江西九江修水县、广西贵港平南县，同时将9个成分依次赋值为A1~A9，分别是烟酸、山柰酚、当药黄素、槲皮素、木犀草素、芦丁、牡荆素、斯皮诺素、水杨酸。将40批小槐花的9个成分含量值作为神经网络单元的输入值，建立1个3层的小槐花来源BP神经网络模型见图2。此神经网络模型的网络输入层节点为10，隐藏层节点数为7个，激活函数为双曲正切函数；输出层节点为10个，激活函数为归一化指数函数Softmax。该模型训练集预测概率准确性为100%，检验集预测概率准确性为92.3%，产地预测准确率较好，模型可靠。BP神经网络对13批样品检验集的预测结果见表6。编号P38的产地预测错误，预测为产地3(广西柳州融水县)的原因是，其真实产地8(广西柳州柳江区)与预测产地过于接近，并且聚类分析和主成分分析都将产地广西柳州融水县与产地广西柳州柳江区看作同一类，所以造成了此误差。总体来说此模型可较准确地对未知产地的小槐花进行产地预测。
3　讨论
收起
本实验分别考察了多种流动相组合，发现使用乙腈-0.1%乙酸水溶液(含0.02 mol·L-1乙酸铵)为流动相进行梯度洗脱时，各被测成分均显示出较好的峰形及较高的响应值，且各成分分离效果好，整个仪器进样测定耗时短，适用于多批次样品的测定。
通过对样品提取方式的考察，发现使用甲醇-25%盐酸(4∶1，v/v)作为提取溶剂，选用超声作为提取方法，得出的试验色谱图及峰面积较大，显现的峰数较多。
本研究采用UPLC-MS/MS对小槐花的多种成分进行测定，快捷有效，同时对小槐花中9个成分进行相关性分析，发现当药黄素、芦丁、斯皮诺素、木犀草素这4个成分呈高度正相关性，即若小槐花中这4个成分有1个含量较高，其他3个含量也是一种相对高的状态，这4个成分在小槐花药材里具有一种相对较强的协同作用，产生这一现象的原因可能和小槐花本身的药效具有抗病毒、抗炎及降糖等相关药理作用有关，当药黄素、芦丁、木犀草素等成分均有消炎抗氧化抗糖尿病的作用。通过利用小槐花中9个成分的含量作为变量构造BP神经网络模型，预测小槐花产地，检验集正确率达到92.3%。一定程度上可以运用在以后的小槐花产地分析中。小槐花的相关检测标准还相对匮乏，对于小槐花的产地分析，可以更好地研究各产地小槐花的质量，从而为今后小槐花质量标准的制定提供一定的数据支持。
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2024年第44卷第7期
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doi: 10.16155/j.0254-1793.2023-0464
  • 首发时间：2026-03-13
  • 出版时间：2024-07-31
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2种不同金属材料的力学参数
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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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