Identification for Stephania Tetrandra S. Moore and Stephania Cepharantha Hayata by Wavelet Transform and BP Neural Network

Horizontal attenuation total reflection-Fourier transform infrared spectroscopy (HATR-FTIR) is used to measure the FTIR of Stephania tetrandra S. Moore and Stephania cepharantha Hayata. Because they belong to the same family and the same genus Chinese traditional medicinal materials, their chemical components are very similar. In order to extrude the difference between them continuous wavelet transform (CWT) is used to decompose their FTIRs. Three main scales are selected as the feature extracting space in the CWT domain. According the distribution of FTIR of theirs, three feature regions are determined at every spectra band at selected three scales in the CWT domain. Thus nine feature parameters form the feature vector. The feature vector is input to the BP neural network (BPNN) to train so as to accurately classify the Stephania tetrandra S. Moore and Stephania cepharantha Hayata. 128 couples of FTIR are used to train and test the proposed method, where 78 couples of data are used as training samples and 50 couples of data are used as testing samples. Experimental results show that the accurate recognition rate between Stephania tetrandra S. Moore and Stephania cepharantha Hayata is respectively 99.6% and 99.8% by using the proposed method.