Quantitative analysis of energy-dispersive X-ray fluorescence spectroscopy based on machine learning and a generative data enhancement technique

Abstract

This paper proposes a data enhancement technique to generate expanded datasets for machine learning by developing an X-ray fluorescence spectra simulator based on the physical process. The simulator consists of several modules, including the excitation source, the interaction process, and the detection system. The spectra generated by the simulator are subject to dimension reduction through feature selection and feature extraction algorithms, and then serve as the input for the XGBoost (extreme gradient boosting) model. Six elements of metal samples with various content ranges were selected as the research target. The results showed that for simulated data, the R2 value for elements with concentrations ranging from 0% to 100% is greater than 95%, and for elements with concentrations of <0.3%, the R2 value is greater than 85%. The experimental data were predicted by the model trained by the simulated spectra. Therefore, this approach provides reliable results for practical application and can supply additional datasets to obtain reasonable prediction results for machine learning with inadequate reference materials.