Spectral wavelength selection and dimension reduction using Elastic Net in spectroscopy analysis

In the use of Fourier transform infrared spectroscopy to build the multi-component gases quantitative analysis model, it is necessary to reduce the dimensions and select characteristics wavelength according to the target gas spectral. Through the regularization algorithm analysis, least absolute shrinkage and selection operator (LASSO) and Elastic Net method were used to do these for seven kinds of mixed gases of methane, ethane, propane, iso-butane, n-butane, iso-pentane and n-pentane. The minimum mean square error (MSE) and prediction deviation were used as the criteria to select LASSO and Elastic Net parameters. Finally, the resolution of 4cm-1 measured spectral data was analyzed. The dimension of spectra were reduced from 2 542 d to 2d and 3d respectively by using LASSO and Elastic Net method under the condition of the MSE of 0.001 9 and 0.002 1. The cross sensitivity of maximum and minimum were 10.271 8% and 1.420 5% by LASSO method. The cross sensitivity of maximum and minimum were 5.494 5% and 0.749 3% by Elastic Net. Results show that the Elastic Net method was better in the characteristic variable selection and the spectral dimension reduction for gas spectral quantitative analysis，and it was foundation to establish the accurate quantitative analysis model.