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[Progress in knowledge-based X-ray fluorescence spectrometry].

L Q Luo1, G Z Ma

  • 1National Research Center of Geoanalysis, Beijing 100037, China.

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|September 10, 2003
PubMed
Summary
This summary is machine-generated.

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This review explores expert systems and knowledge engineering for X-ray fluorescence (XRF) spectrometry. It highlights strategies for spectral interpretation, pattern recognition, and quantitative analysis, advancing XRF applications.

Area of Science:

  • Analytical Chemistry
  • Spectrometry
  • Artificial Intelligence

Background:

  • X-ray fluorescence (XRF) spectrometry is a powerful analytical technique.
  • Expert systems and knowledge engineering offer potential for enhancing XRF data analysis.
  • Current challenges include complex spectral interpretation and quantitative determination.

Purpose of the Study:

  • To review the application of expert systems and knowledge engineering in X-ray fluorescence spectrometry.
  • To consolidate knowledge on various approaches for spectral interpretation and quantitative analysis.
  • To identify key areas of research and development in AI-driven XRF analysis.

Main Methods:

  • Literature review of expert systems and knowledge engineering in XRF.

Related Experiment Videos

  • Analysis of knowledge-controlled strategies, fuzzy logic, and pattern recognition for spectral interpretation.
  • Examination of certainty factor-based expert systems for qualitative analysis.
  • Inclusion of studies on spectra identification, decision-making, quantitative determination, and standardless analysis.

    • Main Results:

    • Expert systems provide effective strategies for knowledge-controlled XRF analysis.
    • Fuzzy logic and pattern recognition enhance XRF spectral interpretation.
    • Certainty factors aid in qualitative interpretation of XRF spectra.
    • Integrated approaches combining theoretical coefficients and neural networks improve quantitative determination.

    Conclusions:

    • Expert systems and knowledge engineering significantly advance XRF analysis capabilities.
    • AI-driven methods offer robust solutions for spectral identification, pattern recognition, and quantitative analysis.
    • Further research in standardless XRF analysis using AI is warranted.