Abstract
Total-reflection X-ray fluorescence (TXRF) has achieved remarkable success with the advantages of simultaneous multi-element analysis capability, decreased background noise, no matrix effects, wide dynamic range, ease of operation, and potential of trace analysis. Simultaneous quantitative online analysis of trace heavy metals is urgently required by dynamic environmental monitoring and management, and TXRF has potential in this application domain. However, it calls for an online analysis scheme based on TXRF as well as a robust and rapid quantification method, which have not been well explored yet. Besides, spectral overlapping and background effects may lead to loss of accuracy or even faulty results during practical quantitative TXRF analysis. This paper proposes an intelligent, multi-element quantification method according to the established online TXRF analysis platform. In the intelligent quantification method, collected characteristic curves of all existing elements and a pre-estimated background curve in the whole spectrum scope are used to approximate the measured spectrum. A novel hybrid algorithm, PSO-RBFN-SA, is designed to solve the curve-fitting problem, with offline global optimization and fast online computing. Experimental results verify that simultaneous quantification of trace heavy metals, including Cr, Mn, Fe, Co, Ni, Cu and Zn, is realized on the online TXRF analysis platform, and both high measurement precision and computational efficiency are obtained.
Highlights
Total-reflection X-ray fluorescence (TXRF) analysis has made great progress in recent years, applied in diverse fields such as environmental, medical, industrial, biological, and food analysis [1,2,3].TXRF analysis is an energy-dispersive X-ray fluorescence (EDXRF) technology adopting unique excitation geometry
Since global optimization is time consuming and short computing time is a critical demand of online analysis, we propose a particle swarm optimization (PSO)-radial basis function network (RBFN)-simulated annealing (SA) algorithm
We mainly focus on the quantitative determination of trace heavy metals, including Cr, Mn, Fe, Co, Ni, Cu and Zn, while still 18 elements, including Si, S, Cl, Ca, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Rb, Zr, Ag, Hg, Pb and Bi, are considered in the spectral decomposition framework
Summary
Total-reflection X-ray fluorescence (TXRF) analysis has made great progress in recent years, applied in diverse fields such as environmental, medical, industrial, biological, and food analysis [1,2,3]. Based on the online TXRF analysis platform, we present an intelligent quantification method In this method, a new spectral decomposition approach of measured spectra is utilized, taking into account the characteristic curves of possible elements in the whole spectrum scope, as well as the pre-estimated spectral background. Considering the benefits of particle swarm optimization (PSO) in solving combinatorial optimization problems [4,5], the benefits of radial basis function network (RBFN) in approximating non-linear function and performing fast computation [6], and the benefits of simulated annealing (SA) in finding the local optimal result [7], a novel hybrid algorithm, PSO-RBFN-SA, is well designed for online TXRF analysis to ensure measurement accuracy and save computing time The framework of this method is scalable and new elements can be added if needed.
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