Quality evaluation of Jingtian granule based on fingerprint combined with chemical pattern recognition
- VernacularTitle:基于指纹图谱结合化学模式识别的精天颗粒质量评价
- Author:
Wei ZHAO
1
;
Shuhe CHEN
2
;
Bin YAN
2
;
Qiongfang ZHENG
1
;
Weixin ZHANG
1
;
Yuanming BA
2
Author Information
1. College of Pharmacy，Hubei University of Chinese Medicine，Wuhan 430065，China
2. Dept. of Pharmacy，Hubei Provincial Hospital of Traditional Chinese Medicine/the Affiliated Hospital of Hubei University of Chinese Medicine/Hubei Province Academy of Traditional Chinese Medicine，Wuhan 430061，China
- Publication Type:Journal Article
- Keywords:
Jingtian granule;
fingerprint;
ultra-high performance liquid chromatography;
hierarchical cluster analysis;
principal
- From:
China Pharmacy
2025;36(3):300-305
- CountryChina
- Language:Chinese
-
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.
