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基于指纹图谱结合化学模式识别的精天颗粒质量评价
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| 篇名： | 基于指纹图谱结合化学模式识别的精天颗粒质量评价 |
| TITLE： | Quality evaluation of Jingtian granule based on fingerprint combined with chemical pattern recognition |
| 摘要： | 目的 建立精天颗粒的超高效液相色谱（UPLC）指纹图谱，并结合化学模式识别法评价其质量。方法以Luna®OmegaPolarC18（150mm×2.1mm，1.6μm）为色谱柱，以乙腈-0.2%磷酸溶液为流动相进行梯度洗脱，流速为0.2mL/min，柱温为30℃，检测波长为265nm。以16号峰为参照峰，通过《中药色谱指纹图谱相似度评价系统（2012版）》建立精天颗粒的UPLC指纹图谱，指认共有峰并进行相似度评价，同时确认每个共有峰的饮片归属。运用化学模式识别方法中的层次聚类分析（HCA）、主成分分析（PCA）对13批样品（S1～S13）进行分类，并采用正交偏最小二乘-判别分析（OPLS-DA）来识别导致不同批次样品间差异的关键性成分。结果所建UPLC方法精密度、重复性、稳定性的RSD均不大于4.4%。在13批精天颗粒指纹图谱中共标定出25个共有峰；通过与对照指纹图谱比对，指认了其中10个共有峰，分别为3号峰（5-羟甲基糠醛）、5号峰（红景天苷）、8号峰（绿原酸）、15号峰（肉桂酸）、19号峰（芦荟大黄素）、20号峰（甘草酸铵）、21号峰（大黄酸）、23号峰（大黄素）、24号峰（甘草次酸）、25号峰（大黄酚）。13批样品指纹图谱的相似度为0.955～0.996。HCA结果显示，13批样品可聚为3类，其中S1、S5、S7、S11～S13为一类，S4、S6为一类，S2、S3、S8～S10为一类。PCA结果显示，主成分1～7的累计方差贡献率为92.666％。OPLS-DA进一步确定了13个差异性成分，这些成分主要来源于酒黄精、红景天、熟大黄和炙甘草等。结论所建立的精天颗粒UPLC指纹图谱方法简便、稳定、重复性良好，结合化学模式识别方法能有效揭示精天颗粒不同批次间的整体质量差异。方中酒黄精、红景天、熟大黄、穿山龙、猪苓、桂枝、炙甘草的质量是影响精天颗粒整体质量的关键。 |
| ABSTRACT： | OBJECTIVE To establish the ultra-high performance liquid chromatography （UPLC） fingerprint of Jingtian granule， and to evaluate its quality by chemical pattern recognition. METHODS Luna® Omega Polar C18 column （150 mm×2.1 mm， 1.6 μm） was used as the chromatographic column， and acetonitrile-0.2% phosphoric acid solution was used as the mobile phase for gradient elution. The flow rate was 0.2 mL/min， the column temperature was 30 ℃， and the detection wavelength was 265 nm. With peak 16 as the reference peak， the UPLC fingerprint of Jingtian granule was established by the Similarity Evaluation System of Chromatographic Fingerprint of Traditional Chinese Medicine （2012 edition）. The common peaks were identified， the similarity evaluation was carried out， and the ownership of each common peak was confirmed. Hierarchical cluster analysis （HCA） and principal component analysis （PCA） in chemical pattern recognition methods were used to classify 13 batches of samples （S1- S13）， and orthogonal partial least squares-discriminant analysis （OPLS-DA） was used to identify the key components of the differences between different batches of samples. RESULTS RSDs of precision， repeatability and stability of the UPLC method were not more than 4.4%. A total of 25 common peaks were identified in the fingerprints of 13 batches of Jingtian granules. By comparing with the reference substance fingerprint， 10 common peaks were identified， namely peak 3 （hydroxymethyl-2-furaldehyde）， peak 5 （salidroside）， peak 8（chlorogenic acid）， peak 15 （cinnamic acid）， peak 19 （aloe-emodin）， peak 20 （ammonium glycyrrhizinate）， peak 21 （rhein）， peak 23 （emodin）， peak 24 （glycyrrhetinic acid）， peak 25 （chrysophanol）. The similarities of fingerprints of 13 batches of samples were 0.955-0.996. The results of HCA showed that 13 batches of samples could be divided into three categories， among which samples S1， S5， S7， S11-S13 were clustered in one category， S4 and S6 were clustered in one category， S2， S3 and S8-S10 were clustered in one category. PCA results showed that the cumulative variance contribution rate of principal components 1-7 was 92.666%. OPLS-DA further identified 13 differential components， which were mainly derived from Polygonati Rhizoma with wine steaming， Rhodiolae Crenulatae Radix Et Rhizoma， prepared Rhei Radix Et Rhizoma and Glycyrrhizae Radix Et Rhizome Praeparata Cum Melle. CONCLUSIONS The established UPLC fingerprint of Jingtian granule is simple， stable and reproducible. Combined with the chemical pattern recognition method， it can effectively reveal the overall quality difference between different batches of Jingtian granule. The quality of Polygonati Rhizoma with wine steaming， Rhodiolae Crenulatae Radix Et Rhizoma， prepared Rhei Radix Et Rhizoma， Dioscoreae Nipponicae Rhizoma， Polyporus， Cinnamomi Ramulus， Glycyrrhizae Radix Et Rhizome Praeparata Cum Melle is the key to the overall quality of Jingtian granule. |
| 期刊： | 2025年第36卷第03期 |
| 作者： | 赵伟；陈树和；闫斌；郑琼芳；张伟欣；巴元明 |
| AUTHORS： | ZHAO Wei，CHEN Shuhe，YAN Bin，ZHENG Qiongfang，ZHANG Weixin，BA Yuanming |
| 关键字： | 精天颗粒；指纹图谱；超高效液相色谱法；层次聚类分析；主成分分析；正交偏最小二乘-判别分析；质量评价 |
| KEYWORDS： | Jingtian granule； fingerprint； ultra-high performance liquid chromatography； hierarchical cluster analysis； principal |
| 阅读数： | 6 次 |
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