X-ray Fluorescence Spectral Denoising Analysis Based on the Russian Roulette Optimized Wavelet Algorithm

Jun Hao; Fusheng Li; Wanqi Yang; Benyong Yang; Qingya Wang; Jie Cao

doi:10.3788/LOP222994

Laser & Optoelectronics Progress, Volume. 60, Issue 9, 0930006(2023)

X-ray Fluorescence Spectral Denoising Analysis Based on the Russian Roulette Optimized Wavelet Algorithm

Jun Hao^1,2, Fusheng Li^3,4、*, Wanqi Yang^3,4, Benyong Yang⁵, Qingya Wang², and Jie Cao^1,2

¹Engineering Research Center of Nuclear Technology Application, Ministry of Education, East China University of Technology, Nanchang 330013, Jiangxi , China

²State Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang 330013, Jiangxi , China

³Research Center for Intelligent Equipment, School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan , China

⁴Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, Zhejiang , China

⁵Center for Remote Sensing, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, Anhui , China

show less

Abstract Get PDF(in Chinese)

Figures & Tables(8)

Fig. 1. Flow chart of Russian roulette optimized wavelet denoising algorithm

Download full size

Fig. 2. Spectra of actual collection of soil samples by hand-held X-ray fluorescence analyzer. (a) GSS7; (b) GSD28; (c) GBW70006

Download full size

Fig. 3. Variation of objective values during Russian roulette optimization

Download full size

Fig. 4. Changes in the R² of the content inversion models for the eight elements. (a) Cr; (b) Mn; (c) Co; (d) Ni; (e) Cu; (f) Zn; (g) As; (h) Pb

Download full size

Fig. 5. Comparison of X-ray fluorescence spectra of soil samples before and after denoising. (a) GSS7; (b) GSD28; (c) GBW70006

Download full size

Table 1. Main characteristic X-ray lines and energy peak positions of the studied elements in soil samples

View table

Table 1. Main characteristic X-ray lines and energy peak positions of the studied elements in soil samples

Element	Characteristic X-ray	Peak energy /keV	Element	Characteristic X-ray	Peak energy /keV
Cr	CrKα，CrKβ	5.41，5.95	Cu	CuKα，CuKβ	8.04，8.907
Mn	MnKα，MnKβ	5.895，6.49	Zn	ZnKα，ZnKβ	8.63，9.572
Co	CoKα，CoKβ	6.925，7.65	As	AsKα，AsKβ	10.532，11.729
Ni	NiKα，NiKβ	7.47，8.265	Pb	PbLα，PbLβ	10.549，12.61

Table 2. Number of X-fluorescence spectral variables associated with the eight elements selected by competitive adaptive reweighted sampling
View table
Table 2. Number of X-fluorescence spectral variables associated with the eight elements selected by competitive adaptive reweighted sampling
Element Number of variables Element Number of variables
Cr 29 Cu 17
Mn 13 Zn 15
Co 80 As 23
Ni 19 Pb 15

Table 3. R² comparison of quantitative PLS models of eight elements before and after denoising X-fluorescence spectra of soil samples by roulette optimized wavelet algorithm
View table
Table 3. R² comparison of quantitative PLS models of eight elements before and after denoising X-fluorescence spectra of soil samples by roulette optimized wavelet algorithm
Denoising condition Cr Mn Co Ni Cu Zn As Pb
R² before denoising 0.9956 0.9934 0.9299 0.9809 0.8983 0.9959 0.9953 0.9967
R² after denoising 0.9959 0.9935 0.9301 0.9921 0.9661 0.9975 0.9974 0.9979

Tools

Get Citation

Copy Citation Text

Jun Hao, Fusheng Li, Wanqi Yang, Benyong Yang, Qingya Wang, Jie Cao. X-ray Fluorescence Spectral Denoising Analysis Based on the Russian Roulette Optimized Wavelet Algorithm[J]. Laser & Optoelectronics Progress, 2023, 60(9): 0930006

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites

Paper Information

Category: Spectroscopy

Received: Nov. 8, 2022

Accepted: Feb. 8, 2023

Published Online: May. 9, 2023

The Author Email: Li Fusheng (lifusheng@uestc.edu.cn)

DOI:10.3788/LOP222994

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Main characteristic X-ray lines and energy peak positions of the studied elements in soil samples

Table 1. Main characteristic X-ray lines and energy peak positions of the studied elements in soil samples

Table 2. Number of X-fluorescence spectral variables associated with the eight elements selected by competitive adaptive reweighted sampling

Table 2. Number of X-fluorescence spectral variables associated with the eight elements selected by competitive adaptive reweighted sampling

Table 3. R2 comparison of quantitative PLS models of eight elements before and after denoising X-fluorescence spectra of soil samples by roulette optimized wavelet algorithm

Table 3. R2 comparison of quantitative PLS models of eight elements before and after denoising X-fluorescence spectra of soil samples by roulette optimized wavelet algorithm

Table 3. R² comparison of quantitative PLS models of eight elements before and after denoising X-fluorescence spectra of soil samples by roulette optimized wavelet algorithm

Table 3. R² comparison of quantitative PLS models of eight elements before and after denoising X-fluorescence spectra of soil samples by roulette optimized wavelet algorithm