Automatic background correction method for laser-induced breakdown spectroscopy

Abstract

In laser-induced breakdown spectroscopy (LIBS), fluctuations in laser energy, fluctuations in laser-sample or laser-plasma interactions, and environmental noise cause the acquired spectral to contain different backgrounds. The background has a substantial impact on the analysis of spectra. The aim of this work is to present a method for automatic estimation of diverse spectral backgrounds. The proposed method utilizes the ideas of window functions and differentiation, combined with a piecewise cubic Hermite interpolating polynomial (Pchip), to achieve automatic removal of spectral backgrounds. In simulation experiments on background correction, it has been observed that this method outperforms asymmetric least squares (ALS) and Model-free background correction in terms of effectiveness. It exhibits the highest signal-to-background ratio (SBR). This method effectively eliminates the baseline that makes the spectrum elevated and some of the white noise. It also demonstrates stability in the background baseline jumps and in the dense region of the characteristic spectral lines. By using this approach to correct the spectral backgrounds of seven different aluminum alloys, there is a notable enhancement in the linear correlation coefficient between spectral intensity and the concentration of Mg element. In the experiment of measuring Mg concentration in aluminum alloys,the linear correlation coefficients between the predicted and actual concentrations of Mg in aluminum alloys were substantially improved after correction of the spectral background. The values of linear correlation coefficients after background correction using asymmetric least squares (ALS) and model-free methods were 0.9913 and 0.9926, respectively. The correlation coefficients between predicted and actual concentrations of Mg in aluminum alloys were improved from 0.9154 to 0.9943 by using the method proposed in this study. In contrast to ALS and Model-free methods, the proposed method showed more significant improvement on this experiment with higher linear correlation and smaller error. Both simulation and quantitative experimental findings affirm that this method achieves high accuracy and can be effectively employed for background correction prior to quantitative analysis.