Comprehensive evaluation of Yiqi Fumai Injection quality by UPLC-Q-TOF- MSE fingerprint and chemical pattern recognition
10.7501/j.issn.0253-2670.2018.10.024
- Author:
Yan-Bin CHU
1
Author Information
1. China Pharmaceutical University
- Publication Type:Journal Article
- Keywords:
Chemical pattern recognition;
Fingerprint;
Hierarchical clustering analysis;
Principle component analysis;
Similarity;
UPLC-Q-TOF-MSE;
Yiqi Fumai Injection
- From:
Chinese Traditional and Herbal Drugs
2018;49(10):2410-2419
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish a UPLC-Q-TOF-MSE fingerprint of Yiqi Fumai Injection (YQFM) for providing reference for visual, easy and overall control of its quality. Methods The chromatographic separation was performed on a Waters Acquity UPLC HSS T3 (100 mm × 2.1 mm, 1.8 μm) column with the mobile phase consisting of acetonitrile and 0.1% formic acid for gradient elution. The flow rate was 0.3 mL/min, and the column temperature was 30 ℃. The capillary voltage was set at 2.5 kV. The nebulization gas was set to 800 L/h at 400 ℃, and the source temperature was 100 ℃. The BPC obtained with negative ion ESI mass spectra were selected for the fingerprint analysis. Similarity evaluation was used to evaluate the quality of YQFM from different batches. Based on the intensities of the ions for common peaks, HCA and PCA were performed using SPSS 19.0 and Simca-P software. Results The UPLC-Q-TOF-MSE fingerprint of YQFM was established by using 28 batches of sample and 18 common peaks were found, of which 15 mutual peaks from Ginseng Radix et Rhizoma rubra, three mutual peaks from Ophiopogonis Radix. Compared with the reference substances and references, 16 of the common peaks were identified and the similarity of 28 batches samples were over 0.970. 28 batches of YQFM could be divided into four grades when the sum of squared Euclidean distance is 5-10 in the result of HCA; PCA got seven principal components through dimension reduction and accumulative contribution rate reached 84.989%. By fitting the load factor model of the first principal component, ten markers greatly impacting on the quality were found. The comprehensive evaluation function of YQFM in different batches was constructed according to the principal component score. Among 28 batches of YQFM, the comprehensive score of S28 was the best, closely followed by S22, S11 and S9, while S14 and S13 was the worst. Conclusion The utilization of UPLC-Q-TOF-MSE fingerprint coupled with chemical pattern recognition could objectively and effectively assess the quality of YQFM, can provide a more comprehensive reference for the quality control of YQFM.
