Horizontal attenuation total reflection-Fourier transform infrared spectroscopy （HATR-FTIR） is used to measure the FTIR of Fimbristylis miliacea （L.） Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed. ...Horizontal attenuation total reflection-Fourier transform infrared spectroscopy （HATR-FTIR） is used to measure the FTIR of Fimbristylis miliacea （L.） Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed. In order to extrude the difference between Fimbristylis miliacea （L.） Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed, continuous wavelet transform （CWT） is used to decompose the FFIR of Fimbristylis miliacea （L.） Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed. Three main scales are selected as the feature extracting space in the CWT domain. According to the distribution of FTIR of Fimbristylis miliacea （L.） Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed, 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 radial basis function neural network （RBFNN） to train so as to accurately classify the Fimbristylis miliacea （L.） Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed. 110 couples of FI＇IR are used to train and test the proposed method, where 60 couples are used as training samples and 50 couples are used as testing samples. Experimental results show that the accurate recognition rate between Fimbristylis miliacea （L.） Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed is respectively 96% and 98% by using the proposed method.展开更多
文摘Horizontal attenuation total reflection-Fourier transform infrared spectroscopy （HATR-FTIR） is used to measure the FTIR of Fimbristylis miliacea （L.） Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed. In order to extrude the difference between Fimbristylis miliacea （L.） Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed, continuous wavelet transform （CWT） is used to decompose the FFIR of Fimbristylis miliacea （L.） Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed. Three main scales are selected as the feature extracting space in the CWT domain. According to the distribution of FTIR of Fimbristylis miliacea （L.） Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed, 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 radial basis function neural network （RBFNN） to train so as to accurately classify the Fimbristylis miliacea （L.） Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed. 110 couples of FI＇IR are used to train and test the proposed method, where 60 couples are used as training samples and 50 couples are used as testing samples. Experimental results show that the accurate recognition rate between Fimbristylis miliacea （L.） Vahl seed and Fimbristhlis stauntonii Debeaux et Franch. seed is respectively 96% and 98% by using the proposed method.
