A Novel Multi-Preprocessing Integration Method for the Qualitative and Quantitative Assessment of Wild Medicinal Plants: Gentiana rigescens as an Example
文献类型： 外文期刊
作者： Liu, Zhimin 1 ; Shen, Tao 1 ; Zhang, Ji 1 ; Li, Zhimin 1 ; Zhao, Yanli 1 ; Zuo, Zhitian 1 ; Zhang, Jinyu 1 ; Wang, Yuanzh 1 ;
作者机构： 1.Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming, Yunnan, Peoples R China
2.Yunnan Univ, Sch Agr, Kunming, Yunnan, Peoples R China
3.Yuxi Normal Univ, Coll Chem Biol & Environm, Yuxi, Peoples R China
关键词： wild Gentiana rigescens; multi-block data analysis; spectroscopy; pre-processing; sequential preprocessing through orthogonalization (SPORT)
期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：5.754； 五年影响因子：6.612 ）
ISSN： 1664-462X
年卷期： 2021 年 12 卷
页码：
收录情况： SCI
摘要： Until now, the over-exploitation of wild resources has increased growing concern over the quality of wild medicinal plants. This led to the necessity of developing a rapid method for the evaluation of wild medicinal plants. In this study, the content of total secoiridoids (gentiopicroside, swertiamarin, and sweroside) of Gentiana rigescens from 37 different regions in southwest China were analyzed by high performance liquid chromatography (HPLC). Furthermore, Fourier transform infrared (FT-IR) was adopted to trace the geographical origin (331 individuals) and predict the content of total secoiridoids (273 individuals). In the traditional FT-IR analysis, only one scatter correction technique could be selected from a series of preprocessing candidates to decrease the impact of the light correcting effect. Nevertheless, different scatter correction techniques may carry complementary information so that using the single scatter correction technique is sub-optimal. Hence, the emerging ensemble approach to preprocessing fusion, sequential preprocessing through orthogonalization (SPORT), was carried out to fuse the complementary information linked to different preprocessing methods. The results suggested that, compared with the best results obtained on the scatter correction modeling, SPORT increased the accuracy of the test set by 12.8% in qualitative analysis and decreased the RMSEP by 66.7% in quantitative analysis.
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