基于迭代小波变换的光谱信号本底扣除方法研究

本底会对光谱分析结果产生很大的干扰作用,为获取特征峰的有效信息,必须首先去除本底。该文提出了一种基于小波变换的本底扣除算法,通过对光谱及后续光谱迭代进行小波变换,利用逼近系数估计本底,直到本底收敛。提出了判断多次估计的本底最大误差是否足够小的收敛准则。利用该算法去除本底后,即可进行特征峰信息的提取。分别利用仿真光谱和实验能量色散X射线荧光光谱对算法进行了验证,并与传统小波变换和多项式拟合法进行了对比。结果表明,该算法能够更准确扣除光谱本底,对其他光谱的本底扣除也具有借鉴意义。

A Background Removing Method for Spectrum Signal Based on Iterative Wavelet Transform

A background removing algorithm based on wavelet transform was proposed in this paper as background leads to overestimate of peak intensity in various spectroscopy analysis,which needs to be removed before further data processing.The spectrum and its descendants were decomposed by wavelet transform iteratively until the estimated backgrounds converged.A criterion to determine the optimal iteration times was proposed by comparing errors of consecutive estimated backgrounds.By removing the converged estimated backgrounds,the spectrum was ready for peak intensities estimation.This method was evaluated with simulated and experimental Energy Dispersive X ray Fluorescence(EDXRF) spectra.The proposed method was compared with traditional wavelet transform and polynomial fitting method.The analyzed results showed that the proposed method could be used for the accurate elimination of spectral background,which was applicable for other kinds of spectral pre processing.