Abstract

X-ray fluorescence (XRF) spectrometry is a popular method for noninvasive elemental analysis of a wide variety of sample types. Unfortunately, applications of XRF are complicated by the occurrence of interelement effects, which make the measured intensities complex functions of multiple-element concentrations. In this work, an implicit modeling method, partial least-squares (PLS), was used to correct for interelement effects in x-ray fluorescence analysis. Instead of fitting XRF peak intensities and concentrations to classical explicit models, the XRF spectra of a set of standard samples were used to build PLS models, implicitly relating the XRF spectra to the concentrations. Methods for the pretreatment of spectra are compared for a set of nickel alloy samples containing Fe, Cr, Ni, Mn, Mo, Ti, and Si. The PLS calibration method using appropriately preprocessed spectra gives the best results, when compared to the results from the literature and from a conventional explicit calibration model (MLR method).

Applications of the non-negative least-squares deconvolution method to analyze energy-dispersive x-ray fluorescence spectra

Wei Zhao, Xianyu Ai, Wuyun Xiao, Ye Chen, Jinglun Li, Hui Zhao, and Wenzhuo Chen
Appl. Opt. 62(20) 5556-5564 (2023)

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

Xiaoyu Jiang, Fusheng Li, Qingya Wang, Jie Luo, Jun Hao, and Muqiang Xu
Appl. Opt. 60(19) 5707-5715 (2021)

Multiple kinds of pesticide residue detection using fluorescence spectroscopy combined with partial least-squares models

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Appl. Opt. 59(6) 1524-1528 (2020)

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X-Ray Image Enhancement by Least-Mean-Square Estimation

David G. Cain
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