Spectral library transfer between distinct Laser-Induced Breakdown Spectroscopy systems trained on simultaneous measurements

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dc.contributor.authorVrábel, Jakubcs
dc.contributor.authorKépeš, Erikcs
dc.contributor.authorNedělník, Pavelcs
dc.contributor.authorBuday, Jakubcs
dc.contributor.authorCempírek, Jancs
dc.contributor.authorPořízka, Pavelcs
dc.contributor.authorKaiser, Jozefcs
dc.coverage.issue4cs
dc.coverage.volume38cs
dc.date.issued2023-04-05cs
dc.description.abstractThe mutual incompatibility of distinct spectroscopic systems is among the most limiting factors in Laser-Induced Breakdown Spectroscopy (LIBS). The cost related to setting up a new LIBS system is increased, as its extensive calibration is required. Solving the problem would enable inter-laboratory reference measurements and shared spectral libraries, which are fundamental for other spectroscopic techniques. We study a simplified version of this challenge where LIBS systems differ only in used spectrometers and collection optics but share all other parts of the apparatus, and collect spectra simultaneously from the same plasma plume. Extensive datasets measured as hyperspectral images of heterogeneous rock sample are used to train machine learning models that can transfer spectra between systems. The transfer is realized by a composed model that consists of a variational autoencoder (VAE) and a multilayer perceptron (MLP). The VAE is used to create a latent representation of spectra from the Primary system. Subsequently, spectra from the Secondary system are mapped to corresponding locations in the latent space by the MLP. The transfer is evaluated by several figures of merit (Euclidean and cosine distances, both spatially resolved; k-means clustering of transferred spectra). We demonstrate the viability of the method and compare it to several baseline approaches of varying complexity.en
dc.formattextcs
dc.format.extent841-853cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationJournal of Analytical Atomic Spectrometry. 2023, vol. 38, issue 4, p. 841-853.en
dc.identifier.doi10.1039/D2JA00406Bcs
dc.identifier.issn1364-5544cs
dc.identifier.orcid0000-0001-5629-3314cs
dc.identifier.orcid0000-0002-7086-2613cs
dc.identifier.orcid0000-0003-1453-5068cs
dc.identifier.orcid0000-0002-8604-7365cs
dc.identifier.orcid0000-0002-7397-125Xcs
dc.identifier.other182920cs
dc.identifier.researcheridF-2136-2018cs
dc.identifier.researcheridG-9463-2014cs
dc.identifier.researcheridD-6800-2012cs
dc.identifier.scopus57190620988cs
dc.identifier.scopus55312098800cs
dc.identifier.scopus7402184758cs
dc.identifier.urihttp://hdl.handle.net/11012/209508
dc.language.isoencs
dc.publisherRoyal Society of Chemistrycs
dc.relation.ispartofJournal of Analytical Atomic Spectrometrycs
dc.relation.urihttps://pubs.rsc.org/en/content/articlelanding/2023/ja/d2ja00406bcs
dc.rightsCreative Commons Attribution 3.0 Unportedcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1364-5544/cs
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/cs
dc.subjectspectroscopic dataen
dc.subjectlibrary transferen
dc.subjectmachine learningen
dc.subjectartificial neural networksen
dc.subjectautoencoderen
dc.titleSpectral library transfer between distinct Laser-Induced Breakdown Spectroscopy systems trained on simultaneous measurementsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-182920en
sync.item.dbtypeVAVen
sync.item.insts2024.03.01 18:46:20en
sync.item.modts2024.03.01 18:14:44en
thesis.grantorVysoké učení technické v Brně. Středoevropský technologický institut VUT. Pokročilé instrumentace a metody pro charakterizace materiálůcs
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Pokročilé instrumentace a metody pro charakterizace materiálů