Optimization of Fourier transform near-infrared spectroscopy model in determining saponin compounds of Panax notoginseng roots
文献类型： 外文期刊
作者： Li, Chaoping 1 ; Zuo, Zhitian 2 ; Wang, Yuanzhong 2 ;
作者机构： 1.Yunnan Univ Chinese Med, Coll Tradit Chinese Med, Kunming 650500, Yunnan, Peoples R China
2.Yunnan Acad Agr Sci, Med Plants Res Inst, 2238 Beijing Rd, Kunming 650200, Yunnan, Peoples R China
关键词： Panax notoginseng (Burk.) FHChen; Near infrared spectroscopy; Saponin compounds; Variable selection; Chemometrics
期刊名称：VIBRATIONAL SPECTROSCOPY （ 影响因子：2.5； 五年影响因子：2.5 ）
ISSN： 0924-2031
年卷期： 2024 年 130 卷
页码：
收录情况： SCI
摘要： As a traditional Chinese medicine, Panax notoginseng (Burk.) F.H.Chen (P. notoginseng) is abundant in chemical compounds, particularly the high content of saponin compounds, which have been extensively implemented in clinical treatment. The traditional chemical methods have drawbacks of destroying samples and taking a long time to analyze the saponin compounds content. In this study, we investigated the viability of employing Fourier transform near infrared spectroscopy (FT-NIR) to assess the saponin compounds content of P. notoginseng rapidly. The partial least squares regression (PLSR) prediction model was established based on spectral information from 252 samples. The effects of various variable selection methods, including variable importance in projection (VIP), competitive adaptive reweighted sampling (CARS), uninformative variables elimination (UVE), and cor-relation coefficients (Correlation) on the model performance, were compared. One examined variable selection algorithm that stood out was the correlation coefficient method. The Correlation-PLSR model' calibration and prediction sets had a high coefficient of determination (Rc2: 0.966-0.989; Rp2: 0.968-0.999) and low root mean square error (RMSEC: 1.293-5.984; RMSEP: 0.291-1.810). It was indicated it can rapidly predict saponin com-pounds in P. notoginseng. This study offers a rapid and reliable quantitative method for P. notoginseng quality control.
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