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A wavelet-based Gaussian method for energy dispersive X-ray fluorescence spectrum.

Pan Liu1, Xiaoyan Deng1,2, Xin Tang1

  • 1College of Science, Huazhong Agricultural University, Wuhan, 430070 China.

Heliyon
|June 14, 2017
PubMed
Summary

This study introduces a wavelet-based Gaussian method (WGM) for energy dispersive X-ray fluorescence (EDXRF) peak intensity estimation. The WGM accurately estimates peak intensity without background analysis and resolves overlapping peaks in EDXRF spectra.

Keywords:
Analytical chemistryApplied mathematicsStatistical physics

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Signal Processing

Background:

  • Accurate peak intensity estimation is crucial for quantitative analysis in energy dispersive X-ray fluorescence (EDXRF).
  • Traditional methods often require background estimation, which can introduce errors.
  • Resolving overlapping peaks remains a challenge in complex EDXRF spectra.

Purpose of the Study:

  • To develop a novel wavelet-based Gaussian method (WGM) for direct peak intensity estimation in EDXRF.
  • To establish a relationship between Gaussian peak parameters and Mexican hat wavelet coefficients.
  • To demonstrate the method's ability to handle overlapping peaks and eliminate background dependency.

Main Methods:

  • Establishing the relationship between Gaussian curve parameters and Mexican hat wavelet coefficients.
  • Developing a local Gaussian estimation method using detail wavelet coefficients at the peak point.
  • Testing the WGM with simulated and measured EDXRF spectra.

Main Results:

  • The WGM accurately calculates Gaussian parameters from two wavelet coefficients at a known peak point.
  • The method directly estimates EDXRF peak intensity, independent of background information.
  • The WGM effectively distinguishes and resolves overlapping peaks in EDXRF spectra.

Conclusions:

  • The proposed wavelet-based Gaussian method offers a robust and direct approach for EDXRF peak intensity estimation.
  • WGM provides an effective solution for spectral deconvolution, particularly for overlapping peaks.
  • This method enhances the accuracy and efficiency of quantitative EDXRF analysis.