Horizontal attenuation total reflection-Fourier transformation infrared spectroscopy (HATR-FT-IR) is used to measure the Mid-IR (MIR) of semen armeniacae amarum and its confusable varieties semen persicae. In order to extrude the difference between semen armeniacae amarum and semen persicae, discrete wavelet transformation (DWT) is used to decompose the MIR of semen armeniacae amarum and semen persicae. Two main scales are selected as the feature extracting space in the DWT domain. According to the distribution of semen armeniacae amarum and semen persicae’s MIR, five feature regions are determined at every spectra band by selecting two scales in the DWT domain. Thus, ten feature parameters form the feature vector. The feature vector is input to the back-propagation artificial neural network (BP-ANN) to train so as to accurately classify the semen armeniacae amarum and semen persicae. 100 couples of MIR are used to train and test the proposed method, where 50 couples of data are used to train samples and other 50 couples of data are used to test samples. Experimental results show that the accurate recognition rate between semen armeniacae amarum and semen persicae is averaged 99% following the proposed method.
Fourier transform infrared (FT-IR) and horizontal attenuated total reflectance (HATR) technique are used to obtain the FT-IR spectra of the seed of green bristle grass (the seed fromSetaria viridis(L.) Beauv), yellow foxtail seed (the seed fromSetaria glauca(L.) Beauv), and the Chinese pennisetum seed (the seed fromSetaria faberiiHerrum). In order to extrude the difference among them, cluster analysis is considered to identify the three kinds of plant seeds. Because they belong to the sibling plant seeds, and have similar chemical components and close FT-IR spectra. The result of Cluster analysis is not satisfactory. The discrete wavelet transformation (DWT) and a support vector machine (SVM) were used for further study. The compression detail 3 and 4 in DWT are used to extract the feature vectors, which are used to train SVM. The trained SVM is used to classify seed of green bristle grass, yellow foxtail seed and Chinese pennisetum seed. The seed samples are collected from different places around the country. With 40 testing samples we could effectively identify the sibling plants, seed of green bristle grass, yellow foxtail seed and Chinese pennisetum seed by FT-IR with discrete wavelet feature extraction and SVM classification.
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