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Characteristic wavelength selection of volatile organic compounds infrared spectra based on improved interval partial least squares

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Journal of Innovative Optical Health Sciences 2019年第2期12卷 35-53页

作者： Wei Ju Changhua Lu Yujun Zhang Weiwei Jiang Jizhou Wang Yi Bing Lu Feng Hong School of Computer and Information Hefei University of Technology HefeiAnhui 230009P.R.China Anhui Institute of Optics Fine Mechanics Chinese Academy of Sciences HefeiAnhui 230031P.R.China

As important components of air pollutant,volatile organic compounds(VOCs)can cause great harm to environment and human *** concentration change of VOCs should be focused on in real-time environment monitoring *** order to solve the problem of wavelength redundancy in full spectrum partial least squares(PLS)modeling for VOCs concentration analysis,a new method based on improved interval PLS(iPLS)integrated with Monte-Carlo sampling,called iPLS-MC method,was proposed to select optimal characteristic wavelengths of VOCs *** method uses iPLS modeling to preselect the characteristic wavebands of the spectra and generates random wavelength combinations from the selected wavebands by Monte-Carlo *** wavelength combination with the best prediction result in regression model is selected as the characteristic wavelengths of the *** wavelength selection methods were built,respectively,on Fourier transform infrared(FTIR)spectra of ethylene and ethanol gas at different concentrations obtained in the *** the interval number of iPLS model is set to 30 and the Monte-Carlo sampling runs 1000 times,the characteristic wavelengths selected by iPLS-MC method can reduce from 8916 to 10,which occupies only 0.22%of the full spectrum *** the RMSECV and correlation coefficient(Rc)for ethylene are 0.2977 and 0.9999 ppm,and those for ethanol gas are 0.2977 ppm and *** experimental results show that the iPLS-MC method can select the optimal characteristic wavelengths of VOCs FTIR spectra stably and effectively,and the prediction performance of the regression model can be significantly improved and simplified by using characteristic wavelengths.

关键词： Ambient air monitoring Fourier transform infrared spectra analysis variable selection interval partial least square Monte-Carlo sampling

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