Rapid Identification of Atmospheric Gaseous Pollutants Using Fourier-Transform Infrared Spectroscopy Combined with Independent Component Analysis

Fourier-transform infrared (FTIR) spectroscopy is a rapid and nondestructive technology for monitoring atmospheric quality. The identification of each component from the FTIR spectra is a prerequisite for the accurate quantitative analysis of gaseous pollutants. Due to the overlap of different gas absorption peaks and the interference of water vapor in the actual measurement, the existing identification methods of gas spectra have drawbacks of low identification rate and the inability to carry out real-time online analysis in atmospheric quality monitoring. In this work, independent component analysis (ICA) is applied to the spectral separation of heavily overlapped spectra of gaseous pollutants. The proposed method is validated by the analysis of mixture spectra obtained in laboratory and actual atmospheric spectra collected from stationary source. The average time consumption of separation process is less than 0.2 seconds, and the identification rate of experimental gases is up to 100%, as shown by the results of peak searching and the analysis of the correction coefficient between the separated spectra and the standard spectra database. The identification results of actual atmospheric spectra demonstrated that the proposed method can effectively identify the gaseous pollutants whose concentration changes in the measured spectra, and it is a promising qualitative spectral analysis tool that can shorten the identification time, as well as increase the identification rate. Therefore, this method can be a useful alternative to traditional qualitative identification methods for real-time online atmospheric pollutant detection

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

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

Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS)

The MC-UVE-SPA method is commonly proposed as a variable selection approach for multivariate calibration. However, the SPA tends to select wavelength variables that are sparsely distributed over the wavelength ranges of the variables selected by the MC-UVE algorithm, and the MC-UVE-SPA cascade cannot improve the problem of wavelength point discontinuity. It is addressed in this paper by proposing a moving-window- (MW-) improved MC-UVE-SPA wavelength selection algorithm. The proposed algorithm improves the continuity of the selected wavelength variables and thereby better exploits the advantages of the MC-UVE algorithm and the SPA to obtain regression models with high prediction accuracy. The MC-UVE, MC-UVE-SPA, and MC-UVE-SPA-MW algorithms are applied for conducting wavelength variable selection for the NIR spectral absorbance data of corn, diesel fuel, and ethylene. Here, partial least squares regression (PLSR) models reflecting the oil content of corn, the boiling point of diesel fuel, and the ethylene concentration are established after conducting wavelength selection using the MC-UVE algorithm, and corresponding multiple linear regression (MLR) models are established after conducting wavelength selection using the MC-UVE-SPA and MC-UVE-SPA-MW algorithms. Experimental results demonstrate that the progressive elimination of uncorrelated and collinear variables generates increasingly simplified partial-spectrum models with greater prediction accuracy than the full-spectrum model. Among the three wavelength selection algorithms, the MC-UVE-SPA selected the least number of wavelength variables, while the proposed MC-UVE-SPA-MW algorithm provided models with the greatest prediction accuracy