中药大黄质量的化学模式识别

本文用PRIMA法对大黄属(Rheum)6种植物的29个样品的质量进行了化学模式识别研究。以泻下药理实验的结果佐证、核对。根据35维HPLC数据或16维UV数据,与SIMCA法、Bayes判别法、非线性映照法(NLM)等模式识别法比较,PRIMA法具有计算速度快、适用范围广的特点,比植物形态学方法的正确率高。本文还根据一阶导数紫外光谱提供的数据,简化了化学模式识別法鉴定中药大黄的操作步骤。

QUALITY ASSESSMENT OF THE CHINESE TRADITIONAL MEDICINE RHUBARB BY CHEMICAL PATTERN RECOGNITION

Traditionally, the identification of Chinese medicines is performed by the morphological method. As chemical constituents play the role of therapeutic action, it is more rational to identify Chinese traditional medicines by analyzing their chemical constituents. Furthermore, the therapeutic action of a Chinese traditional medicine is usually the result of coordination of its chemical constituents. In this paper, the PRIMA method was adopted to classify 29 samples of the Chinese traditional medicine rhubarb, with 16 samples as the training set and 13 samples as the test set. The recognition ability was 100% and the prediction ability was 92% on HPLC data, while both of them were 100% on UV data. As far as this research is concerned, the PRIMA method is superior to the Bayes classification rule, the SIMCA method and nonlinear mapping in its simplicity, rapidity and correctness. Pharmacologic experiments were carried out to confirm the results.