Department of Chemistry, Capital Normal University, Beijing 100048, China.
Department of Chemistry, Capital Normal University, Beijing 100048, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2017 Jan 15;171:72-79. doi: 10.1016/j.saa.2016.07.039. Epub 2016 Jul 27.
Rhubarb has different medicinal efficacy to official rhubarb and may affect the clinical medication safety. In order to guarantee the quality of rhubarb, we established a method to distinguish unofficial rhubarbs. 52 official and unofficial rhubarb samples were analyzed using near-infrared (NIR) spectroscopy and mid-infrared (MIR) spectroscopy for classification. The feature vectors, which were selected by wavelet compression (WC) and interval partial least squares (iPLS) from NIR, MIR spectra, were fused together for identifying rhubarb samples. Partial least squares-discriminant analysis (PLS-DA), soft independent modeling of class analogies (SIMCA), support vector machine (SVM) and artificial neural network (ANN) were compared for classifying rhubarb. The use of data fusion strategies improved the classification model and allowed correct classification of all the samples.
大黄有别于正品大黄，其药用功效可能会影响临床用药安全。为保证大黄质量，本研究建立了一种非正品大黄的鉴别方法。利用近红外（NIR）和中红外（MIR）光谱对 52 种正品和非正品大黄样品进行分析分类。采用小波压缩（WC）和区间偏最小二乘法（iPLS）从 NIR、MIR 光谱中选择特征向量，融合后用于鉴别大黄样品。采用偏最小二乘判别分析（PLS-DA）、类相似的软独立建模（SIMCA）、支持向量机（SVM）和人工神经网络（ANN）对大黄进行分类比较。数据融合策略的使用提高了分类模型的性能，可对所有样本进行正确分类。
