The accuracy evaluation method of baseline estimation algorithms in energy dispersive X-ray fluorescence spectrum analysis

In energy dispersive X-ray fluorescence analysis (EDXRF), many baseline estimation algorithms have been proposed for the accurate characteristic peak area. However, the true value of the characteristic peak area of measured spectrum is unknown and cannot be used to evaluate the accuracy of the baseline estimation algorithms. In this article, an assessment method was proposed based on Monte Carlo simulation, which can obtain the characteristic peak area, and evaluate the accuracy of the baseline estimation algorithms directly. Meanwhile, the accuracy and practicality of four baseline estimation algorithms were evaluated by the assessment method, which include statistics-sensitive nonlinear iterative peak-clipping (SINP), fast Fourier transform (FFT), adaptive iteratively reweighted penalized least squares (AirPLS), and automated iterative moving averaging (AIMA). Comparing the relative error of the characteristic peak area, AirPLS gave the best performance for baseline estimation in EDXRF.

Automated summary

This article proposes an assessment method based on Monte Carlo simulation to directly evaluate the accuracy of baseline estimation algorithms in energy dispersive X-ray fluorescence analysis. Four baseline estimation algorithms were evaluated and compared, and AirPLS performed the best in estimating the characteristic peak area.