An improved PD-AsLS method for baseline estimation in EDXRF analysis

Abstract

Baseline correction is an important step in energy-dispersive X-ray fluorescence analysis. The asymmetric least squares method (AsLS), adaptive iteratively reweighted penalized least squares method (airPLS), and asymmetrically reweighted penalized least squares method (arPLS) are widely used to automatically select the data points for the baseline. Considering the parametric sensitivity of the aforementioned methods and the statistical characteristics of the X-ray energy spectrum, this paper proposes an asymmetrically reweighted penalized least squares method based on the Poisson distribution (PD-AsLS) to automatically correct the baseline of X-ray spectra. Monte Carlo (MC) simulation is used to obtain the background spectrum, and PD-AsLS is used to estimate the baseline of the background. The relative error and the absolute error between the simulated background and PD-AsLS estimated background are used to determine the accuracy of PD-AsLS. The correlation coefficient (COR) and the root mean square error (RMSE) between the estmated baseline and the real baseline are calculated, and results of PD-AsLS are compared with results of three other classical methods (arPLS, airPLS and AsLS) to evaluate the reliability of PD-AsLS. The results of PD-AsLS show that the COR is above 0.95 and RMSE is less than 6. The stability and the practicability of PD-AsLS are also evaluated in experiments. A sample is measured five time to get its X-ray energy spectra, and the coefficient of variation (CV) of the estimated baseline is smaller than that of measured spectra. Experiments show that PD-AsLS can estimate baselines better than arPLS without any overestimation. Those results indicate that PD-AsLS can reliably estimate the baselines of X-ray spectra and effectively suppress the statistical fluctuation.