Abstract
Horizontal attenuation total reflection–Fourier transform infrared spectroscopy (HATR–FTIR) is used to measure the FTIR ofStephania tetrandraS. Moore andStephania cepharanthaHayata. 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 betweenStephania tetrandraS. Moore andStephania cepharanthaHayata, continuous wavelet transform (CWT) is used to decompose the FTIR ofStephania tetrandraS. Moore andStephania cepharanthaHayata. Three main scales are selected as the feature extracting space in the CWT domain. According the distribution of FTIR ofStephania tetrandraS. Moore andStephania cepharanthaHayata, 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 radius basis function neural network (RBFNN) to train so as to accurately classify theStephania tetrandraS. Moore andStephania cepharanthaHayata. 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 betweenStephania tetrandraS. Moore andStephania cepharanthaHayata is respectively 99.8 and 99.9% by using the proposed method.
Highlights
Fourier transform infrared spectroscopy method is a very common analysis tool with high sensitivity, resolution and fast speed, which has been widely used in the identification of Chinese traditional medicinal materials [1,2,3]
Lei et al proposed a novel method of calculating approximate derivative of signals in analytical chemistry by using the continuous wavelet transform (CWT) [9]
Moore has an absorption peak in 2928 cm−1, and Stephania cepharantha Hayata here has double peaks, which are in 2971 cm−1 and 2922 cm−1, respectively
Summary
Fourier transform infrared spectroscopy method is a very common analysis tool with high sensitivity, resolution and fast speed, which has been widely used in the identification of Chinese traditional medicinal materials [1,2,3]. Lei et al proposed a novel method of calculating approximate derivative of signals in analytical chemistry by using the continuous wavelet transform (CWT) [9]. Some researchers combine the wavelet transform with other some intelligent technique to analyze the signal of chemistry [10,11]. Artificial neural network can learn and train the information samples so that it will have similar memories of the human brain, identification capabilities and the implementation of various information processing functions. It has good self-learning, adaptive, associative memory, parallel processing and nonlinear conversion capabilities, which avoids complicated mathematical derivation.
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