Optimization of Fourier transform near-infrared spectroscopy model in determining saponin compounds of Panax notoginseng roots

Abstract

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 correlation 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 compounds in P. notoginseng. This study offers a rapid and reliable quantitative method for P. notoginseng quality control.