Quality Assessment of Gentiana rigescens from Different Geographical Origins Using FT-IR Spectroscopy Combined with HPLC.
Gentiana rigescens is a precious herbal medicine in China because of its liver-protective and choleretic effects. A method for the qualitative identification and quantitative evaluation of G. rigescens from Yunnan Province, China, has been developed employing Fourier transform infrared (FT-IR) spectroscopy and high performance liquid chromatography (HPLC) with the aid of chemometrics such as partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) regression. Our results indicated that PLS-DA model could efficiently discriminate G. rigescens from different geographical origins. It was found that the samples which could not be determined accurately were in the margin or outside of the 95% confidence ellipses. Moreover, the result implied that geographical origins variation of root samples were more obvious than that of stems and leaves. The quantitative analysis was based on gentiopicroside content which was the main active constituent in G. rigescens. For the prediction of gentiopicroside, the performances of model based on the parameters selected through grid search algorithm (GS) with seven-fold cross validation were better than those based on genetic algorithm (GA) and particle swarm optimization algorithm (PSO). For the SVM-GS model, the result was satisfactory. FT-IR spectroscopy coupled with PLS-DA and SVM-GS can be an alternative strategy for qualitative identification and quantitative evaluation of G. rigescens.
['Wu|Zhe|Z|', 'Zhao|Yanli|Y|', 'Zhang|Ji|J|', 'Wang|Yuanzhong|Y|']
D002851:Chromatography, High Pressure Liquid D016002:Discriminant Analysis D030012:Gentiana D057888:Iridoid Glucosides D016018:Least-Squares Analysis D018515:Plant Leaves D018517:Plant Roots D017550:Spectroscopy, Fourier Transform Infrared D060388:Support Vector Machine
Gentiana rigescens FT-IR spectroscopy qualitative quantitative partial least squares discriminant analysis support vector machines regression