Baseline correction method based on improved adaptive iteratively reweighted penalized least squares for the x-ray fluorescence spectrum

To solve the problem of baseline drift in the detection of soil samples by x-ray fluorescence spectrum, an improved adaptive iterative weighted penalized least squares (IairPLS) method is proposed to estimate the baseline of x-ray fluorescence spectrum signals. We improved the original exponential weight function to solve the problem of baseline underestimation caused by adaptive iterative weighted penalized least squares. The improved function effectively reduces the risk of baseline underestimation and speeds up the weighting process, achieving good results. In this paper, the MC simulation spectrum and soil real analysis spectrum are used to verify the performance of the algorithm. Finally, the algorithm is compared with previous penalized least squares methods (asymmetric least squares, adaptive iterative reweighted penalized least squares, and multiple constrained reweighted penalized least squares), with the results showing that the proposed method has the least root-mean-square error after baseline correction for optimal smoothing parameters A. and the best relative error of baseline estimation accuracy. Meanwhile, the IairPLS method can effectively improve the quantitative analysis ability of the x-ray fluorescence spectrum. The proposed method can be successfully applied to the actual x-ray fluorescence spectrum, which provides a powerful basis for quantitative analysis. (C) 2021 Optical Society of America

Automated summary

The proposed IairPLS method effectively addresses baseline underestimation caused by adaptive iterative weighted penalized least squares, improving baseline estimation accuracy and enhancing the quantitative analysis ability of X-ray fluorescence spectrum.