化学计量学
指纹(计算)
模式识别(心理学)
计算机科学
色谱法
人工智能
化学
作者
Xin Wang,Xitong Liu,Jianhui Wang,Gang Wang,Yue Zhang,Lili Lan,Guoxiang Sun
标识
DOI:10.1016/j.saa.2021.119554
摘要
In this study, we explored the quality consistent evaluation method of Yankening Tablets (YKNT) from different manufacturers by using multiple fingerprint profiles, including dual-wavelength ultra-high performance liquid chromatography (UPLC) serial fingerprint and Fourier Transform Infrared Spectroscopy (FT-IR) fingerprint, combined with quantitative analysis of multi-components by single marker (QAMS) method. In the Average method of systematic quantified fingerprint method (AMSQFM), three fingerprint parameters of macro qualitative similarity (Sm-UPLC-FTIR), macro quantitative similarity (Pm-UPLC-FTIR), and the variation coefficient of fingerprint homogeneity (αUPLC-FTIR) were calculated based on the ratio method. The Sm-UPLC-FTIR values of all the samples were greater than 0.80, the αUPLC-FTIR values were less than 0.20, and the Pm-UPLC-FTIR values range from 72.8% to 119.8%. Method validation results showed the established fingerprint method had good precision, solution stability, and method repeatability, all samples could be roughly divided into different levels. The contents of berberine (BBR) and baicalin (BCL) measured by the calibration curve method (CCM) and QAMS method were compared, and t-test results (Pvalue > 0.05) indicated there was no significant difference between the two methods, which showed that QAMS could accurately quantify the markers of the YKNT. The explanatory ability (R2Y) values of BBR and BCL in the PLS model were both greater than 0.94, and the root mean square error of estimation (RMSEE) and root mean square error of prediction (RMSEP) values were both less than 2.5, indicating that the established model was reliable. Hierarchical cluster analysis divided all samples into four categories. This research made a major contribution to the quality consistent evaluation of Traditional Chinese Medicine (TCM) and food.
科研通智能强力驱动
Strongly Powered by AbleSci AI