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 $R^2$ value for elements with concentrations ranging from 0% to 100% is greater than 95%, and for elements with concentrations of ${\lt}{0.3}\%$, the ${{R}^2}$ 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.

© 2023 Optica Publishing Group

Applications of the non-negative least-squares deconvolution method to analyze energy-dispersive x-ray fluorescence spectra

Wei Zhao, Xianyu Ai, Wuyun Xiao, Ye Chen, Jinglun Li, Hui Zhao, and Wenzhuo Chen
Appl. Opt. 62(20) 5556-5564 (2023)

Small-sample stacking model for qualitative analysis of aluminum alloys based on femtosecond laser-induced breakdown spectroscopy

Qing Ma, Ziyuan Liu, Tong Sun, Xun Gao, and YuJia Dai
Opt. Express 31(17) 27633-27653 (2023)

Machine learning in analytical spectroscopy for nuclear diagnostics [Invited]

Ashwin P. Rao, Phillip R. Jenkins, Ryan E. Pinson, John D. Auxier II, Michael B. Shattan, and Anil K. Patnaik
Appl. Opt. 62(6) A83-A109 (2023)

Micro energy dispersive x-ray fluorescence as a powerful complementary technique for the analysis of bimetallic Au/Ag/glass nanolayer composites used in surface plasmon resonance sensors

Mohammad Ghorbanpour and Cavus Falamaki
Appl. Opt. 51(32) 7733-7738 (2012)

Rapid quantitative analysis of calcium in infant formula powder assisted by long short-term memory with variable importance using laser-induced breakdown spectroscopy

Yu Ding, Linyu Yang, Wenjie Chen, Jing Chen, Xingqiang Zhao, Yong Luo, and Wangping Zhou
Appl. Opt. 62(9) 2188-2194 (2023)