# Nuclear Science and Techniques

《核技术》(英文版) ISSN 1001-8042 CN 31-1559/TL     2019 Impact factor 1.556

Nuclear Science and Techniques ›› 2019, Vol. 30 ›› Issue (3): 52

• ACCELERATOR, RAY AND APPLICATIONS •

### Quantitative energy-dispersive X-ray fluorescence analysis for unknown samples using full-spectrum least-squares regression

Yong-Li Liu1 • Qing-Xian Zhang1 • Jian Zhang1 • Hai-Tao Bai1 • Liang-Quan Ge1

1. 1 The College of Applied Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu 610059, China
• Received:2018-05-03 Revised:2018-08-08 Accepted:2018-08-10
• Contact: Qing-Xian Zhang E-mail:Zhangqingxian06@cdut.cn
• Supported by:
This work was supported by the National Key R&D Project of China (No. 2017YFC0602100), the National Natural Science Foundation of China (No. 41774147) and Sichuan Science and Technology Support Program (No. 2015GZ0272).
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Yong-Li Liu, Qing-Xian Zhang, Jian Zhang, Hai-Tao Bai, Liang-Quan Ge. Quantitative energy-dispersive X-ray fluorescence analysis for unknown samples using full-spectrum least-squares regression.Nuclear Science and Techniques, 2019, 30(3): 52
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Abstract: The full-spectrum least-squares (FSLS) method is introduced to perform quantitative energy-dispersive X-ray fluorescence analysis for unknown solid samples. Based on the conventional least-squares principle, this spectrum evaluation method is able to obtain the background- corrected and interference-free net peaks, which is significant for quantization analyses. A variety of analytical parameters and functions to describe the features of the fluorescence spectra of pure elements are used and established, such as the mass absorption coefficient, the Gi factor, and fundamental fluorescence formulas. The FSLS iterative program was compiled in the C language. The content of each component should reach the convergence criterion at the end of the calculations. After a basic theory analysis and experimental preparation, 13 national standard soil samples were detected using a spectrometer to test the feasibility of using the algorithm. The results show that the calculated contents of Ti, Fe, Ni, Cu, and Zn have the same changing tendency as the corresponding standard content in the 13 reference samples. Accuracies of 0.35% and 14.03% are obtained, respectively, for Fe and Ti, whose standard concentrations are 8.82% and 0.578%, respectively. However, the calculated results of trace elements (only tens of μg/g) deviate from the standard values. This may be because of measurement