一种基于近红外光谱的天然产物抗氧化活性预测评价方法

An Approach to Predicting Antioxidative Activities of Natural Products Based on Near Infrared Spectroscopy

提出了一种基于近红外（NIR）光谱的黄酮类提取物抗氧化活性计算预测新方法。采用1，1-二苯-2-苦肼基（DPPH）法测定28种黄酮类中药材提取物的抗氧化活性，并在400010000cm2范围扫描样品的红外光谱，采用偏最小二乘（PLS）算法建立了黄酮类组分近红外光谱与抗氧化活性之间的校正模型。建模过程中，以交叉验证相关系数（R2），交叉验证误差均方根（RMSECV）为指标，确定了用于建模的最优近红外波段和光谱预处理方法。校正模型的RSECV为9．50％，R2为0．9017，预测误差均方根（RMSEP）为14．8％。该方法快速无损、操作简便，可用于中药及天然产物提取物抗氧化活性的快速评价。

The aim of present study is to develop a new approach to predicting antioxidative activities of flavonoid extracts from traditional Chinese medicine based on near-infrared spectroscopy （NIR）. The 1,1-diphenyl-2-picryl-hydrazyl （DPPH） method was employed to assay the antioxidative activities of twenty-eight extracts. Then, the near infrared diffuse reflectance spectra of those samples were acquired in the range of 4 000-10 000 cm-1. The partial least square （PLS） algorithm was used to generate the calibration model that correlates the spectra and the antioxidative activities of those natural products. The optimal wavenum- her ranges and the preprocessing method of original spectrum data were selected during the establishment of calibration model according to the root mean square error of cross-validation （RMSECV）. The established model has been successfully applied to predict hioactivities of six samples in the validation set. The value of RMSECV of the proposed model was 9.50% with R2 of 0. 901 7, and the value of RMSEP was 14. 8%. The result indicated that the method can be used for the fast determination of the antioxidative activities of flavonoids as well as other active components of natural products.