Near infrared spectroscopy for simultaneous quantification of five chemical components in Arnebiae Radix (AR) with partial least squares and support vector machine algorithms

Abstract

Arnebiae Radix (AR) has high economic and medicinal value, and it is used as Chinese herbal medicine in clinic for a long time. Nowadays, the quality assessment of AR is generally performed on expensive instruments with time-consuming and laborious methods. In this study, the near-infrared (NIR) spectroscopy technique was applied to develop a rapid, simple and accurate method for quantifying five main chemical components including acetylshikonin, β-acetoxyisovalerylalkannin, isobutylshikonin, isovalerylshikonin and β,β-dimethylacrylalkannin in AR coupled with chemometrics, aiming at rapidly evaluating the quality of AR. NIR spectra of 110 batches of AR samples were collected to quantify the content of five chemical components. The partial least squares (PLS) and support vector machine (SVM) algorithms were separately used to develop the calibration models. Different spectral pretreatment methods and feature selection methods were compared in details during the model development. As a result, the performances of PLS models for quantifying the five chemical components were superior to SVM models. The correlation coefficients of calibration set (rcal) of PLS models for acetylshikonin, β-acetoxyisovalerylalkannin, isobutylshikonin, isovalerylshikonin and β,β-dimethylacrylalkannin were 0.9972, 0.9973, 0.9927, 0.9981 and 0.9977, respectively, while the correlation coefficients of prediction set (rpre) were 0.9913, 0.9855, 0.9825, 0.9916 and 0.9825, respectively. Besides, the relative prediction deviation (RPD) of PLS models reached 7.0, 5.85, 4.92, 7.52 and 5.43, respectively. This study demonstrated that the NIR spectroscopy technique was a promising approach as routine analysis of AR, and it could be an alternative method for rapid quality assessment of AR.