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新疆紫草的HPLC指纹图谱建立、化学模式识别分析及其含量测定
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| 篇名： | 新疆紫草的HPLC指纹图谱建立、化学模式识别分析及其含量测定 |
| TITLE： | HPLC Fingerprint Establishment ，Chemistry Pattern Recognition Analysis and Content Determination of Arnebia euchroma |
| 摘要： | 目的：建立新疆紫草的高效液相色谱（HPLC）指纹图谱，进行化学模式识别分析，并测定其中3种成分的含量。方法：采用HPLC法。以乙酰紫草素为参照，绘制34批不同来源新疆紫草药材样品的HPLC指纹图谱，采用《中药色谱指纹图谱相似度评价系统（2012A版）》进行相似度评价，确定共有峰；采用SPSS19.0、SIMCA14.1统计软件进行聚类分析、主成分分析和正交偏最小二乘法-判别分析，以变量投影重要性值大于1为标准，筛选影响新疆紫草药材质量的差异标志物，并以相同HPLC法测定其中3种成分的含量。结果：34批新疆紫草药材共有12个共有峰；除市售样品中的3批药材相似度低于0.72外，其余药材的相似度均高于0.86；共指认出左旋紫草素、乙酰紫草素、β，β′-二甲基丙烯酰阿卡宁等3个共有峰。34批新疆紫草药材可聚为2类，其中S1、S4～S6、S13、S15～S20、S22、S26～S34聚为一类，其余聚为一类。前3个主成分因子的方差贡献率分别为52.834％、18.600％、8.387％，累积方差贡献率为79.821％。左旋紫草素、乙酰紫草素、β，β′-二甲基丙烯酰阿卡宁等6个成分为影响其质量的差异标志物。左旋紫草素、乙酰紫草素、β，β′-二甲基丙烯酰阿卡宁检测质量浓度的线性范围分别为0.72～90、2.05～410、2.50～500µg/mL（r均大于0.999）；定量限分别为0.132、0.135、0.118µg/mL，检测限分别为0.040、0.041、0.036µg/mL；精密度、稳定性（24h）、重复性、耐用性试验的RSD均小于3％；加样回收率分别为95.959％～100.201％（RSD＝1.669％，n＝6）、97.818％～102.698％（RSD＝1.788％，n＝6）、95.831％～99.344％（RSD＝1.600％，n＝6）。含量分别为0.002％～0.134％、0.025％～1.388％、0.022％～0.881％。结论：所建HPLC指纹图谱和含量测定方法简便、稳定性好，可用于新疆紫草药材的质量评价和定量分析；左旋紫草素、乙酰紫草素、β，β′-二甲基丙烯酰阿卡宁等成分的含量各有不同，为不同来源新疆紫草的差异标志物。 |
| ABSTRACT： | OBJECTIVE：To es tablish the HPLC fingerprint of Arnebia euchroma ，analyze them with chemical pattern recognition technology ， and determine the contents of 3 components. METHODS ： HPLC method was adopted. Using acetylshikonin as reference ，HPLC fingerprint of 34 batches of A. euchroma from different sources were drawn. Similarity Evaluation System for TCM Chromatographic Fingerprint （2012A edition ）was used to evaluate the similarity of the samples ，and common peaks were determined. SPSS 19.0 and SIMCA 14.1 statistical software was used for cluster analysis ，principle component analysis and orthogonal partial least squares-discriminate analysis. According to the standard of the variable importance in the project greater than 1，the differential markers affecting the quality difference of A. euchroma were screened. Meanwhile ，the contents of 3 components were determined by the same HPLC method. RESULTS ：There were 12 common peaks in HPLC fingerprints for 34 batches of A. euchroma . The similarity of other samples were more than 0.86，except that t he three （No.2016A3005-5） batches of medicinal herbs on the market were less than 0.72；3 common peaks were identified ， such as shikonin ，acetylshikonin， β ，β ′-dimethylacrylic acanine. These 34 batches of samples could be classified into two categories . S 1， qq.com S4-S6，S13，S15-S20，S22，S26-S34 were clustered into one category，and others clustered into the other category. By principal component analysis ，the contribution rates of three principle components were 52.834％ ，18.600％ and 8.387％ . Accumulative contribution rate was 79.821％ . Six constituents，such as shikonin，acetylshikonin and β，β'-dimethylacrylic acanine were screened as differential markers，representing the major differences of A. euchroma . The linear range of above three components were 0.72-90，2.05-410，2.50-500 µg/mL（r all more than 0.999）， respectively. The limits of quantification were 0.132，0.135，0.118 µg/mL，respectively. The limits of detection were 0.040，0.041， 0.036 µg/mL，respectively. RSDs of precision ，stability（24 h），reproducibility and durability tests were all lower than 3％. Recoveries were 95.959％-100.201％（RSD＝1.669％，n＝6），97.818％-102.698％（RSD＝1.788％，n＝6），95.831％-99.344％ （RSD＝1.600％，n＝6）. The contents of above three components were 0.002％-0.134％，0.025％-1.388％，0.022％-0.881％. CONCLUSIONS：Established HPLC fingerprint and content determination method are simple and stable ，can be used for quality evaluation and quantitative analysis of A. euchroma . Shikonin ，acetylshikonin and β，β'-dimethylacrylic acanine are different in the content and are differential markers of A. euchroma from different source. |
| 期刊： | 2020年第31卷第14期 |
| 作者： | 马留纯，马生军，朱金芳，杨建波，周明聪，宋晓雨 |
| AUTHORS： | MA Liuchun ，MA Shengjun ，ZHU Jinfang ，YANG Jianbo ，ZHOU Mingcong ，SONG Xiaoyu |
| 关键字： | 新疆紫草；高效液相色谱法；指纹图谱；主成分分析；聚类分析；正交偏最小二乘法-判别分析；含量测定 |
| KEYWORDS： | Arnebia euchroma ；HPLC；Fingerprint；Principle component analysis ；Cluster analysis ；Orthogonal partial least |
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