Improvement of Sensitivity in Total Reflection X-ray Fluorescence Spectrometry by Machine Learning-Reference-Cited by-同舟云学术

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Improvement of Sensitivity in Total Reflection X-ray Fluorescence Spectrometry by Machine Learning

Published:2020-09-05 Issue:9 Volume:69 Page:463-470
ISSN:0525-1931
Container-title:BUNSEKI KAGAKU
language:en
Short-container-title:Bunseki kagaku

Author:

KIKUTA Shinya¹,YAMAGAMI Motoyuki¹,KONO Hiroshi¹,DOI Makoto¹

Affiliation:

1. Rigaku Corporation

Publisher

Japan Society for Analytical Chemistry

Subject

Analytical Chemistry

Link

https://www.jstage.jst.go.jp/article/bunsekikagaku/69/9/69_463/_pdf

Reference10 articles.

1. 1) H. Aiginger : Spectrochim. Acta Part B, 46, 1313 (1991).

2. 2) H. Schwenke, P. A. Beaven, J. Knoth : J. Anal. Chem., 365, 19 (1999).

3. 3) D. Hellin, S. D. Gendt, N. Valckx, P. W. Mertens, C. Vinckier : Spectrochim. Acta Part B, 61, 496 (2006).

4. 4) Y. Mori : Adv. X-ray Anal., 45, 253 (2002).

5. 6) A. Krizhevsky, I. Sutskever, G. E. Hilton : Pcoceedings of the 25the international conference on neural information processing systems, 1, 1097 (2012).

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Spectrum prediction in X-ray fluorescence analysis using Bayesian estimation;Spectrochimica Acta Part B: Atomic Spectroscopy;2023-01

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