Correction of overlapping peaks of Pb and As spectrum based on a chaotic particle swarm optimization–Gaussian mixture statistical model

Abstract

Energy‐dispersive X‐ray fluorescence spectroscopy has been effectively applied to detect heavy metals in soil because of its fast detection speed, low cost, and high accuracy. However, overlapping peaks appear in the detection of some heavy metals, such as Pb and As, resulting in significant errors in the detection. Therefore, it is impossible to accurately predict the content of heavy metals in soil. To solve this problem, a Gaussian mixture statistical model (GMSM) is applied based on physical characteristics randomly formed by X‐rays combined with statistical ideas. Subsequently, when estimating the parameters of the GMSM, performing particle swarm optimization (PSO) causes the parameters to fall into the local optima easily. Chaos theory is introduced to the PSO algorithm to promote its weight update strategy, and the chaotic PSO (CPSO) with an anti‐premature mechanism is proposed to achieve global convergence. When using CPSO‐GMSM to analyze the overlapping peaks, the relative error compared with the actual single metal sample is less than 0.0693, and the error compared with the actual characteristic peak position is less than 0.0133. The overlapping peaks are corrected effectively, providing a foundation for the accurate quantitative analysis of heavy metals in soil.

Atomic spectrometry update – a review of advances in environmental analysis

“…A general overestimation (>20%) of recoveries was attributed to large particle sizes in Si-containing samples and peak overlap. Wu et al 190 corrected the overlap of As and Pb peaks by applying a Gaussian mixture statistical model and chaotic-particle-swarm optimisation, thereby providing a mechanism for more accurate element quantification. In a paper in Chinese, spectra obtained in the field in the determination of Pb were corrected 191 for noise by applying harmonic analysis at different Pb concentrations and smoothed pseudo Wigner–Ville distribution.…”

Heavy metal content prediction based on Random Forest and Sparrow Search Algorithm

“…In this study, all the collected soil was sifted through a 200‐mesh (75 μm) sieve; hence, the influence of soil particle size can be ignored. Through the above analysis, the input variables for modeling are moisture content of soil and fluorescence intensity of peak 16 . The output of the model is heavy metal content of samples.…”

“…Through the above analysis, the input variables for modeling are moisture content of soil and fluorescence intensity of peak K α . 16 The output of the model is heavy metal content of samples. Take Element Zn as an example, the fluorescence intensity of the spectrum changes with content of Zn and moisture content of soil is shown in Figure 2A,B.…”