Determination of low Z elements concentrations in geological samples by energy dispersive X-ray fluorescence with a back propagation neural network

Due to complex scattering from the sample dark matrix, absorption in the detector window and the competing Auger effect with higher cross-section for low Z elements (Z 0.95. It implied that the modeling approaches significantly overcome matrix effects between the concentrations of low Z elements and...

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Bibliographic Details
Published in:Spectrochimica acta. Part B: Atomic spectroscopy 2022-10, Vol.196, p.106518, Article 106518
Main Authors: Shao, Jinfa, Li, Rongwu, Pan, Qiuli, Cheng, Lin
Format: Article
Language:English
Subjects:
Backpropagation neural network
Compton scatter
Energy dispersive X-ray fluorescence
Geological samples
Low Z elements
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Summary:Due to complex scattering from the sample dark matrix, absorption in the detector window and the competing Auger effect with higher cross-section for low Z elements (Z 0.95. It implied that the modeling approaches significantly overcome matrix effects between the concentrations of low Z elements and Compton scatter peaks. So, the method has the potential for being widely used in the analysis of samples rich in low Z elements. [Display omitted] •A BPNN quantitative analysis model of low Z elements in geological samples was developed.•The Compton scatter data ware used for model training.•The model prediction performance is improved by training on correlated elements concentration.•The application of K-fold cross validation improves model accuracy.
ISSN:0584-8547
1873-3565
DOI:10.1016/j.sab.2022.106518