A feature selection method combined with ridge regression and recursive feature elimination in quantitative analysis of laser induced breakdown spectroscopy

In the spectral analysis of laser-induced breakdown spectroscopy, abundant characteristic spectral lines and severe interference information exist simultaneously in the original spectral data. Here, a feature selection method called recursive feature elimination based on ridge regression (Ridge-RFE) for the original spectral data is recommended to make full use of the valid information of spectra. In the Ridge-RFE method, the absolute value of the ridge regression coefficient was used as a criterion to screen spectral characteristic, the feature with the absolute value of minimum weight in the input subset features was removed by recursive feature elimination (RFE), and the selected features were used as inputs of the partial least squares regression (PLS) model. The Ridge-RFE method based PLS model was used to measure the Fe, Si, Mg, Cu, Zn and Mn for 51 aluminum alloy samples, and the results showed that the root mean square error of prediction decreased greatly compared to the PLS model with full spectrum as input. The overall results demonstrate that the Ridge-RFE method is more efficient to extract the redundant features, make PLS model for better quantitative analysis results and improve model generalization ability.