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[Identifying the characteristics of FTIR spectra of herba epimedii icariin via wavelet analysis and RBF neural network].

In the present paper, the authors extracted active components of herba epimedii and their important features using Fourier transform infrared spectroscopy (FTIR), correlation coefficient comparison, and multilevel wavelet analysis. The extracted features were then used to classify herba epimedii via radial basis function (RBF) neural network. There were altogether 250 samples of the medicine with various different types, including epimedium brevicornu Maxim., E. sagittatum (Sieb. et Zucc. ) Maxim, E. pubescens Maxim., E. koreanum Nakai and E. wushanense T. S. Ying. An important component of herba epimedii, herba epimedii icariin, has a special peak at 1 259 cm(-1) on the FTIR spectra obtained from the methanol extraction, which is consistent with the result obtained by traditional HPLC qualitative analysis. Therefore, this special peak can be used to determine if herba epimedii contains herba epimedii icariin. Furthermore, large variations in the spectrum caused by low content of icariin, weak absorption peaks and noise were successfully removed by applying correlation coefficient comparison and multilevel wavelet analysis, which significantly increased the quality of classification of RBF neural network. This paper creates a framework of fast identification of herba epimedii icariin in raw herba epimedii by FTIR spectra via wavelet analysis and RBF neural network.