指纹(计算)
色谱法
主成分分析
甲酸
高效液相色谱法
线性判别分析
化学计量学
质量评定
梯度洗脱
偏最小二乘回归
化学
人工智能
模式识别(心理学)
计算机科学
机器学习
外部质量评估
工程类
运营管理
作者
Lin Zhou,Zhi Sun,Wenhua Xue,Shuhong Liang,Lihua Zuo,Juan Ding,Lingling Zhao,Xiaofang Jiang,Qingquan Jia,Hua Wei,Kefeng Liu,Youhong Hu,Jie Zhao
出处
期刊:PubMed
日期:2018-08-01
卷期号:43 (16): 3279-3284
标识
DOI:10.19540/j.cnki.cjcmm.20180611.006
摘要
To establish the ultra performance liquid chromatography (UPLC) fingerprint of Dandeng Tongnao Ruanjiaonang and conduct a systemic, comprehensive quality evaluation of the drug by combining with a chemical pattern recognition method. In this study, Waters UPLC ultra-high performance liquid chromatography instrument and ACQUITY UPLCHSS T3 chromatographic colum n were employed to perform the separation with acetonitrile-0.1% formic acid aqueous solution as the mobile phase for gradient elution; and the detection wavelength was set at 256 nm to establish the UPLC fingerprint of 10 batches of Dandeng Tongnao Ruanjiaonang. Then, the further quality assessment of the drug was carried out by similarity evaluation, Cluster Analysis(CA), Principal Component Analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Finally, 77 peaks were recognised as common peaks in the fingerprint, and 15 peaks of them were identified using standard references. The similarity value of these 10 batches of drugs was all above 0.960, indicating a relatively stable quality. But minor differences were still discovered between the batches of the drug by CA and PCA. Finally, 6 common peaks were recognised as the quality makers using OPLS-DA method. The analysis method established in this study was scientific, accurate, reliable and simple; fingerprint combined with chemical pattern recognition technique can be used to systematically and comprehensively evaluate the drug quality of Dandeng Tongnao Ruanjiaonang; what's more, it could also provide a reference for the quality control of traditional Chinese medicine and its preparations at the same time.
科研通智能强力驱动
Strongly Powered by AbleSci AI