Laser & Optoelectronics Progress, Volume. 60, Issue 9, 0930006(2023)
X-ray Fluorescence Spectral Denoising Analysis Based on the Russian Roulette Optimized Wavelet Algorithm
Compared with other denoising algorithms, wavelet denoising is preferable. The wavelet function and decomposition level greatly influence the quality of denoising; however, determining the wavelet function and decomposition level is challenging in actual X-fluorescence spectral denoising. This paper proposes a wavelet algorithm based on Russian roulette optimization for X-ray spectral denoising to address this problem. The summation of the coefficients of determination of the quantitative models (Cr, Mn, Co, Ni, Cu, Zn, As, and Pb for soil samples) R2 is considered as the optimization objective. The Russian roulette optimization strategy updates the wavelet function and decomposition level. Subsequently, after the number of iterations is selected, the optimal wavelet function and decomposition level of each soil sample spectrum are selected. The approach is validated on the X-ray fluorescence spectra of 55 certified reference soil samples. The R2 value of all eight heavy metals is higher after optimization, and the sum of the R2 values of the quantitative models of the eight elements increases from 7.8383 to 7.8704. This technique can be used as an alternative for wavelet denoising applied in rapid elemental measurements.
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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
Category: Spectroscopy
Received: Nov. 8, 2022
Accepted: Feb. 8, 2023
Published Online: May. 9, 2023
The Author Email: Li Fusheng (lifusheng@uestc.edu.cn)