FT-IR spectroscopy coupled with HPLC for qualitative and quantitative analysis of different parts of Gentiana rigescens Franch
文献类型： 外文期刊
作者： He, Gang 1 ; Zhu, Xin-yan 1 ; Shen, Tao 3 ; Wang, Yuan-zhong 1 ;
作者机构： 1.Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Peoples R China
2.Yunnan Agr Univ, Coll Food Sci & Technol, Kunming 650201, Peoples R China
3.Yuxi Normal Univ, Coll Chem Biol & Environm, Yuxi, Peoples R China
关键词： Gentiana rigescens; Total secoiridoids; FT-IR; HPLC; Content prediction
期刊名称：INFRARED PHYSICS & TECHNOLOGY （ 影响因子：3.3； 五年影响因子：3.2 ）
ISSN： 1350-4495
年卷期： 2024 年 136 卷
页码：
收录情况： SCI
摘要： In recent years, people are paying more and more attention to their nutritional health care, and the demand for medicinal plants is increasing year by year, which has aroused widespread concern about their quality. Therefore, the development of methods that enable rapid evaluation of medicinal plants is necessary. The present study was based on 132 Gentiana rigescens (G. rigescens) plants analyzed for the content of total secoiridoids (gentiopicroside, loganic acid, swertiamarin) in different parts of the plant using high performance liquid chromatography. Fourier transform infrared (FT-IR) spectroscopy was also used to characterize the overall chemical information of different parts of G. rigescens. The use of two-dimensional correlation analysis algorithms further improves the acquisition of spectral information and effectively extracts the more chemically informative bands (1750-400 cm(-1)). On this basis, FT-IR spectroscopy combined with chemometrics was proposed for the prediction of total secoiridoids content in G. rigescens roots, stems and leaves. The results showed that the content of total secoiridoids showed a pattern of root > stem, leaf and gentiopicroside > loganic acid > swertiamarin. Partial least squares discriminant analysis (PLS-DA) was effective in identifying samples from different parts of the body with 100 % model accuracy. The optimized Partial least squares (PLS) model was able to predict the content of gentiopicroside, loganic acid, and swertiamarin in both roots and leaves with RPD values greater than 1.4. Unfortunately, the model had difficulty in predicting the content of loganic acid and swertiamarin in stems (RPD < 1.4). Overall, the method has good stability and applicability, and provides an effective and rapid analytical method for the quality evaluation of medicinal plants.
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