几种模式识别方法用于中药挥发油红外光谱法鉴别的比较研究Δ

摘要

OBJECTIVE:To compare the performance of several pattern recognition methods in the identification of volatile oils of traditional Chinese medicine(TCM)by infrared spectroscopy. METHODS:The volatile oils of several Lonicera and Citrus TCM were determined by infrared spectroscopy. All samples of infrared spectrum were classified by hierarchical clustering,K-mean clustering,artificial neural networks,and support vector machine. RESULTS:The results of hierarchical clustering and K-mean clus-tering were ineffective. Methods of artificial neural networks and support vector machine achieved correct classification rate of 100%. CONCLUSIONS:Artificial neural networks and support vector machine can be combined with infrared spectroscopy to cre-ate chemometric fingerprinting for the identification of volatile oils of TCM.%目的：比较几种模式识别方法在中药挥发油红外光谱法鉴别中的分类效果。方法：对多种忍冬属和柑橘属中药的挥发油进行红外光谱测定，应用系统聚类、K-均值聚类、人工神经网络、支持向量机方法对样品红外光谱进行分类。结果：系统聚类与K-均值聚类分类效果不佳，人工神经网络和支持向量机方法均取得100％分类正确率。结论：可以将人工神经网络和支持向量机模式识别方法与红外光谱法结合，构建化学计量学指纹图谱技术，用于中药挥发油的鉴别。