A XRF Determination Method of Trace Elements Based on Iterative Discrete Wavelet Background Subtraction

Abstract

The invention discloses an XRF determination method for trace elements based on iterative discrete wavelet background subtraction. The original detection spectral line signal of the sample to be measured is decomposed by L-layer one-dimensional discrete wavelet to obtain the primary low-frequency approximation coefficient of each layer, and select the most The optimal decomposition layer and the corresponding first-order low-frequency approximation coefficient a v ; Then for a low-frequency approximation coefficient a v Carry out iterative discrete wavelet decomposition. When the difference between the results of two consecutive iterations of N consecutive iterations is less than the preset accuracy, the iteration is stopped, and the result of the latest iteration is used as the approximate background signal, and then the signal after subtracting the background is obtained; The quantitative analysis value of the target element is obtained after approximate processing of the scattering intensity of the Puton peak and the characteristic X-ray fluorescence intensity of the target element. The method of the invention can effectively avoid the influence of the peak shift and the peak area of ​​the original spectrum signal, improve the quantitative detection accuracy of trace elements, increase the detection signal-to-noise ratio by more than three times, and reduce the detection limit of trace elements.

Description

technical field [0001] The invention relates to the technical improvement field of analysis and processing of spectral data, in particular to an XRF determination method of trace elements based on iterative discrete wavelet background subtraction. Background technique [0002] Energy dispersive X-ray fluorescence spectrometry detection technology can meet various needs of trace element detection due to its almost no need for sample pretreatment, no pollution, and fast and convenient analysis capabilities. Spectrometer detection spectrum contains background information, characteristic X-rays, escape peaks , Overlay peak and other information. Affected by many factors such as the instrument system, the detection environment, and the nature of the sample itself, there are always different degrees of background interference in the spectral signal. In addition, the parts containing metal elements on each instrument will affect the spectrum during the air test, such as the metal ...

AI Technical Summary

Problems solved by technology

Using the peak stripping method to subtract the background can better maintain the position of the original peak, but its calculation process will have a certain impact on the peak area of ​​the original spectral line, and it largely depends on the shape of the original spectral line

Polynomial fitting often distorts the sample spectrum due to overfitting

The classic Fourier transform can reflect the overall connotation of the signal, but its expression is often not intuitive enough, and noise will complicate the spectrogram

The traditional background subtraction method will lead to peak shift and affect the peak area of ​​the original spectrum signal, and the detection accuracy is not high. For this, the present invention proposes a trace element XRF (X Ray Fluorescence, XRF) based on iterative discrete wavelet background subtraction X-ray fluorescence spectrometry) determination method

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