Improving laser-induced breakdown spectroscopy regression models <i>via</i> transfer learning-Reference-Cited by-同舟云学术

Improving laser-induced breakdown spectroscopy regression models via transfer learning

Published:2022 Issue:9 Volume:37 Page:1883-1893
ISSN:0267-9477
Container-title:Journal of Analytical Atomic Spectrometry
language:en
Short-container-title:J. Anal. At. Spectrom.

Author:

Képeš Erik¹²^ORCID,Vrábel Jakub¹,Pořízka Pavel¹²,Kaiser Jozef¹²^ORCID

Affiliation:

1. Central European Institute of Technology, Brno University of Technology, Purkyňova 656/123, CZ-61200 Brno, Czech Republic

2. Brno University of Technology, Faculty of Mechanical Engineering, Institute of Physical Engineering, Technická 2, CZ-61669, Brno, Czech Republic

Abstract

Calibration datasets can be extended by using artificial neural networks to transform data collected on different LIBS systems. Hence, regression performance can be improved.

Funder

Grantová Agentura České Republiky

Vysoké Učení Technické v Brně

Publisher

Royal Society of Chemistry (RSC)

Subject

Spectroscopy,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/JA/D2JA00180B

Reference87 articles.

1. Comparing several atomic spectrometric methods to the super stars: special emphasis on laser induced breakdown spectrometry, LIBS, a future super star

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3. Industrial applications of laser-induced breakdown spectroscopy: a review

4. Coal analysis by laser-induced breakdown spectroscopy: a tutorial review

5. Laser Induced Breakdown Spectroscopy compared with conventional plasma optical emission techniques for the analysis of metals – A review of applications and analytical performance

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