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白芍饮片的HPLC指纹图谱建立及聚类分析、主成分分析
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| 篇名： | 白芍饮片的HPLC指纹图谱建立及聚类分析、主成分分析 |
| TITLE： | |
| 摘要： | 目的：建立白芍饮片的高效液相色谱（HPLC）指纹图谱，并进行聚类分析和主成分分析。方法：采用HPLC法。色谱柱为SunFire® C18，流动相为乙腈-0.05％磷酸水溶液（梯度洗脱），流速为1.0 mL/min，检测波长为230 nm，柱温为30 ℃，采集时间为70 min，进样量为15 μL。以芍药苷为参照，建立26批不同产地白芍饮片及30批不同炮制方法白芍饮片的HPLC指纹图谱；采用《中药色谱指纹图谱相似度评价系统》（2012版）进行相似度评价，确定共有峰；采用SPSS 20.0软件进行聚类分析和主成分分析。结果：26批不同产地白芍饮片共有9个共有峰，相似度均大于0.880；共指认了6个峰，分别为没食子酸、儿茶素、芍药内酯苷、芍药苷、1，2，3，4，6-五没食子酰葡萄糖、苯甲酰芍药苷；聚类分析结果显示，当余弦距离为15时26批样品可聚为2类，S1～S21聚为一类，S22～S26聚为一类；经主成分分析，前2个主成分的累积方差贡献率为81.124％。30批不同炮制方法白芍饮片共有10个共有峰，相似度均大于0.970；共指认了7个峰，分别为没食子酸、儿茶素、丹皮酚新苷、芍药内酯苷、芍药苷、1，2，3，4，6-五没食子酰葡萄糖、苯甲酰芍药苷；聚类分析结果显示，当余弦距离为25时，30批样品可聚为2类，B1～B10聚为一类，C1～C10、J1～J10聚为一类；经主成分分析，前4个主成分的累积方差贡献率为86.887％。结论：所建HPLC指纹图谱及聚类分析和主成分分析结果可为不同白芍饮片的质量控制提供参考。 |
| ABSTRACT： | OBJECTIVE： To establish HPLC fingerprints of Paeonia tactilora decoction pieces， and to conduct its cluster analysis and principal component analysis. METHODS： HPLC method was adopted. The determination was performed on SunFire® C18 column with mobile phase consisted of acetonitril-0.05％ phosphoric acid solution （gradient elution） at the flow rate of 1.0 mL/min. The detection wavelength was set at 230 nm， the column temperature was 30 ℃， the collection time was 70 min，and sample size was 15 μL. Using paeoniflorin as reference， HPLC fingerprints of 26 batches P. tactilora decoction pieces from different habitats and 30 batches by different processed methods were established. The similarity of samples was evaluated by TCM Chromatographic Fingerprint Similarity Evaluation System （2012 edition） to confirm common peak. Cluster analysis and principal component analysis were performed by using SPSS 20.0 software. RESULTS： There were 9 common peaks in HPLC fingerprints of 26 batches of sample from different habitats， the similarity of which was higher than 0.880. Six peaks were identified， including gallic acid， catechin， albiflorin， paeoniflorin， 1，2，3，4，6-pentagalloylglucose and benzoylpaeoniflorin. Cluster analysis showed that 26 batches of samples were clustered into 2 categories when cosine distance was 15. S1-S21 were clustered into one category； S22-S26 were clustered into the other category. By principal component analysis， the accumulative contribution rate of two main components was 81.124％. There were 10 common peaks in HPLC fingerprints of 30 batches of sample by different processed methods， the simi- larity of which was higher than 0.970. Seven peaks were identified， including gallic acid， catechin， aplopaeonoside， albiflorin， paeoniflorin， 1，2，3，4，6-pentagalloylglucose and benzoylpaeoniflorin. Cluster analysis showed that 30 batches of samples were clustered into 2 categories when cosine distance was 25. B1-B10 were clustered into one category； C1-C10 and J1-J10 were clustered into the other category. By principal component analysis， the accumulative contribution rate of four main components was 86.887％. CONCLUSIONS： Established HPLC fingerprint， the results of cluster analysis and principal component analysis can provide reference for quality control of decoction pieces of P. tactilora. |
| 期刊： | 2019年第30卷第24期 |
| 作者： | 林秀敏，张振凌，王胜超，闫梦真，陈祎甜，张江山 |
| AUTHORS： | LIN Xiumin，ZHANG Zhenling，WANG Shengchao，YAN Mengzhen，CHEN Yitian，ZHANG Jiangshan |
| 关键字： | 白芍饮片；高效液相色谱法；指纹图谱法；聚类分析；主成分分析 |
| KEYWORDS： | Paeonia tactilora decoction pieces； HPLC； Fingerprint； Cluster analysis； Principal component analysis |
| 阅读数： | 322 次 |
| 本月下载数： | 6 次 |
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