Abstract: A neural classifier based on stepwise correlative component analysis, named SCCA-HBP, for classifying the quality pattern of Chinese Medicinal Materials(CMM) is proposed. The chemical pattern features are extracted by stepwise acquirement of the class correlative components from chromatographic analysis dataset with a high dimension, and then are used as the inputs in the neural classifier to reduce the dimension of input variables. Further, a hybrid BP algorithm with dead interval of error is derived for training the neural classifier in order to increase training speed and classification accuracy. The performance of the neural classifier is tested by using a set of 32 Angelica samples with different quality grades. The classification accuracy of SCCA-HBP is 100%, better than PCA-BP( 84.4%) and SCCA-BP(90.6%). Moreover, the training time of HBP is 54.2% of the cost obtained with BP algorithm.

Key words: Quality evaluation of Chinese medicinal materials, Angelica, Pattern feature extraction, Chemical pattern classification, Neural classifier