Machine learning in laser-induced breakdown spectroscopy as a novel approach towards experimental parameter optimization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F22%3APU143915" target="_blank" >RIV/00216305:26620/22:PU143915 - isvavai.cz</a>
Alternative codes found
RIV/00216224:14310/22:00128810
Result on the web
<a href="https://pubs.rsc.org/en/content/articlelanding/2022/JA/D1JA00389E" target="_blank" >https://pubs.rsc.org/en/content/articlelanding/2022/JA/D1JA00389E</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1039/d1ja00389e" target="_blank" >10.1039/d1ja00389e</a>
Alternative languages
Result language
angličtina
Original language name
Machine learning in laser-induced breakdown spectroscopy as a novel approach towards experimental parameter optimization
Original language description
Similar to other analytical techniques, the performance of laser-induced breakdown spectroscopy (LIBS) is significantly influenced by the selection of optimal experimental parameters. The optimization of LIBS is challenging because the laser-matter interaction and subsequent plasma formation are influenced not only by selected experimental parameters but also by the physical and mechanical properties of the sample. The goal of this work is to develop an artificial neural network (ANN) that is able to predict the signal-to-noise ratio (SNR) of selected spectral lines based on specific experimental parameters (laser pulse energy and gate delay) and on the sample's physical and mechanical properties. The ANN training was based on input data obtained from a high number of measurements of three certified materials with highly different mechanical and physical properties (low alloyed steel, glass, and aluminium alloy) with 2079 combinations of experimental parameters - gate delay (GD) and laser pulse energy (E). The ANN was optimized in terms of the number of neurons and hidden layers. The minimal number of input data points was studied with emphasis on the ANN prediction accuracy expressed as the determination coefficient R-2 (predicted vs. measured values). The number of input data points was studied from three points of view - a minimal number of experimental parameters for one matrix, a minimal amount of data from different matrices, and a minimal number of different spectral lines. It has been shown that at least 20 different combinations of experimental parameters are necessary for one matrix to obtain reasonable performance of the ANN. However, only ten combinations are needed when a new matrix is added to the working model. It has also been shown that the prediction accuracy is poor for spectral lines which were not part of the training data. Finally, the ANN was utilized to predict the SNR of selected spectral lines in a specific range of experimental parameters.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10406 - Analytical chemistry
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Analytical Atomic Spectrometry
ISSN
0267-9477
e-ISSN
1364-5544
Volume of the periodical
2022
Issue of the periodical within the volume
37
Country of publishing house
GB - UNITED KINGDOM
Number of pages
10
Pages from-to
603-612
UT code for WoS article
000758343300001
EID of the result in the Scopus database
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