Machine learning in laser-induced breakdown spectroscopy as a novel approach towards experimental parameter optimization-Reference-Cited by-同舟云学术

Machine learning in laser-induced breakdown spectroscopy as a novel approach towards experimental parameter optimization

Published:2022 Issue:3 Volume:37 Page:603-612
ISSN:0267-9477
Container-title:Journal of Analytical Atomic Spectrometry
language:en
Short-container-title:J. Anal. At. Spectrom.

Author:

Prochazka David¹²^ORCID,Pořízka Pavel¹²³,Hruška Jakub⁴^ORCID,Novotný Karel⁵^ORCID,Hrdlička Aleš⁵^ORCID,Kaiser Jozef¹²³^ORCID

Affiliation:

1. Materials Characterization and Advanced Coatings, Central European Institute of Technology, Purkyňova 656/123, Brno, Czech Republic

2. Faculty of Mechanical Engineering, Brno University of Technology, Technická 2, Brno, Czech Republic

3. Lightigo s.r.o., Renneská třída 329/13, 63900 Brno, Czech Republic

4. Faculty of Informatics, Masaryk University, Botanická 68a, 602 00 Brno, Czech Republic

5. Department of Chemistry, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic

Abstract

Samples with different mechanical and physical properties were measured by LIBS under diverse experimental conditions. The results were used to train a neural network. By means of the neural network, the optimisation process was significantly reduced.

Funder

Vysoké Učení Technické v Brně

Masarykova Univerzita

Publisher

Royal Society of Chemistry (RSC)

Subject

Spectroscopy,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/JA/D1JA00389E

Reference35 articles.

1. Laser-Induced Breakdown Spectroscopy (LIBS), Part II: Review of Instrumental and Methodological Approaches to Material Analysis and Applications to Different Fields

2. LIBS analyses for industrial applications – an overview of developments from 2014 to 2018