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

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4 000～10 000 cm-1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 0.901 7，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。

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

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 wavenumber 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 bioactivities 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.