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) R-2 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 R-2 value of all eight heavy metals is higher after optimization, and the sum of the R-2 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.

Automated summary

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.