Geographic origin identification and rapid determination of four constituents of Gentiana rigescens by FTIR combined with chemometrics
文献类型: 外文期刊
第一作者: Zhao, Yanli
作者: Zhao, Yanli;Zhang, Ji;Wang, Yuanzhong;Yuan, Tianjun
作者机构:
关键词: Gentiana rigescens; Monte Carlo uninformative variable elimination; partial least squares discriminant analysis; partial least squares regression; t test
期刊名称:JOURNAL OF CHEMOMETRICS ( 影响因子:2.467; 五年影响因子:2.513 )
ISSN: 0886-9383
年卷期: 2019 年 33 卷 4 期
页码:
收录情况: SCI
摘要: Gentiana rigescens is one of the most important traditional Chinese medicines because of its therapeutic effects in protecting the liver and promoting bile discharge, anti-hypertension, and spasm and pain relief. To enhance quality control, a comprehensive assessment of G. rigescens from different geographic origins by Fourier-transform infrared (FTIR) in combination with chemometrics was conducted. Through pretreatment by the second derivative, spectrum standard deviation and Monte Carlo uninformative variable elimination (MC-UVE), the characteristic FTIR spectra were selected and an identification model of the different geographic origins of G. rigescens was built by partial least squares discriminant analysis (PLS-DA). The validation results showed that the accuracy rates of the model were 100%, 97.22%, and 100% for samples from Yunnan, Guizhou, and Sichuan, respectively. On the basis of the correlation between characteristic spectral points and the contents of swertiamarin, gentiopicroside, sweroside, and loganic acid, quantitative models were established by partial least squares regression (PLSR). The measured and predicted values were not significantly different as assessed through the paired t test (p > 0.05). Additionally, the characteristic spectra were interpreted, and 93 common peaks were selected. FTIR analysis combined with chemometric methods could accurately identify the different geographic origins of G. rigescens and rapidly predict the content of swertiamarin, gentiopicroside, sweroside, and loganic acid, thus providing a comprehensive method of evaluating traditional Chinese medicines.
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