Neural network-based matrix effect correction in EDXRF analysis

Xianguo TUO; Bo CHENG; Keliang MU; Zhe LI

doi:10.1016/S1001-8042(09)60004-X

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Neural network-based matrix effect correction in EDXRF analysis

LOW ENERGY ACCELERATOR, RAY TECHNOLOGY AND APPLICATIONS | Updated：2021-01-29

- Neural network-based matrix effect correction in EDXRF analysis
- Nuclear Science and Techniques Vol. 19, Issue 5, (2008)
- Affiliations：
  
  1.Chengdu University of Technology, Chengdu 610059, China
- Author bio：
  
  Corresponding author. E-mail address: txg@cdut.edu.cn
- Funds：
- DOI：10.1016/S1001-8042(09)60004-X
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Xianguo TUO, Bo CHENG, Keliang MU, et al. Neural network-based matrix effect correction in EDXRF analysis. [J]. Nuclear Science and Techniques 19(5):278-281(2008)
DOI：

Xianguo TUO, Bo CHENG, Keliang MU, et al. Neural network-based matrix effect correction in EDXRF analysis. [J]. Nuclear Science and Techniques 19(5):278-281(2008) DOI： 10.1016/S1001-8042(09)60004-X.

摘要

Abstract

In this paper we discuss neural network-based matrix effect correction in energy dispersive X-ray fluorescence (EDXRF) analysis, with detailed algorithm to classify the samples. The method can correct the matrix effect effectively through classifying the samples automatically, and influence of X-ray absorption and enhancement by major elements of the samples is reduced. Experiments for the complex matrix effect correction in EDXRF analysis of samples in Pangang showed improved accuracy of the elemental analysis result.

关键词

Keywords

Self-organizing mapping neural networkCluster analysisMatrix effectSinter mineral

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