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

Autor: Wei Ju, Changhua Lu, Yujun Zhang, Weiwei Jiang, Jizhou Wang, Yi Bing Lu, Feng Hong
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: Journal of Innovative Optical Health Sciences, Vol 12, Iss 2, Pp 1950005-1-1950005-19 (2019)
Druh dokumentu: article
ISSN: 1793-5458
1793-7205
17935458
DOI: 10.1142/S1793545819500056
Popis: As important components of air pollutant, volatile organic compounds (VOCs) can cause great harm to environment and human body. The concentration change of VOCs should be focused on in real-time environment monitoring system. In 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 spectra. This method uses iPLS modeling to preselect the characteristic wavebands of the spectra and generates random wavelength combinations from the selected wavebands by Monte-Carlo sampling. The wavelength combination with the best prediction result in regression model is selected as the characteristic wavelengths of the spectrum. Different wavelength selection methods were built, respectively, on Fourier transform infrared (FTIR) spectra of ethylene and ethanol gas at different concentrations obtained in the laboratory. When 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 wavelengths. While the RMSECV and correlation coefficient (Rc) for ethylene are 0.2977 and 0.9999ppm, and those for ethanol gas are 0.2977 ppm and 0.9999. The 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.
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje