Automated detection of element-specific features in LIBS spectra

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dc.contributor.authorGajarská, Zuzanacs
dc.contributor.authorFaruzelová, Annacs
dc.contributor.authorKépeš, Erikcs
dc.contributor.authorProchazka, Davidcs
dc.contributor.authorPořízka, Pavelcs
dc.contributor.authorKaiser, Jozefcs
dc.contributor.authorLohninger, Hanscs
dc.contributor.authorLimbeck, Andreascs
dc.coverage.issue12cs
dc.coverage.volume39cs
dc.date.accessioned2025-04-11T08:56:18Z
dc.date.available2025-04-11T08:56:18Z
dc.date.issued2024-11-27cs
dc.description.abstractThis work introduces a novel semi-automatic approach to identify elemental lines in spectra obtained via laser-induced breakdown spectroscopy (LIBS). The algorithm is based on unique spectral fingerprints of individual elements that are configured into comb-like filters. The element-specific filters are then correlated with measured spectra for semi-supervised qualitative analysis of samples. Spectral variations are accommodated by adjusting the micro-parameters of the comb filter. This step ensures accurate results despite minor deviations from the instrument's ideal calibration due to instrumental fluctuations, e.g., drift in spectral calibration or line broadening. Additionally, the algorithm can autonomously detect spectral interference regions, aiding the analyst in verifying spectral lines where such interference may occur. The paper presents a comprehensive overview of the algorithm and discusses the main concepts, parameters, optimization steps, and limitations using Echelle spectra of two standard reference materials with different complexity: borosilicate glass (NIST 1411) and low-alloyed steel (SUS1R). Furthermore, the transferability of the approach to different scenarios and real-life applications is demonstrated using a single-channel Czerny-Turner spectrum of an amalgam filling extracted from a hyperspectral image of a human tooth. A demo of the algorithm is publicly available for non-commercial purposes.en
dc.formattextcs
dc.format.extent11cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationJournal of Analytical Atomic Spectrometry. 2024, vol. 39, issue 12, 11 p.en
dc.identifier.doi10.1039/d4ja00247dcs
dc.identifier.issn1364-5544cs
dc.identifier.orcid0000-0001-8296-3828cs
dc.identifier.orcid0000-0002-7086-2613cs
dc.identifier.orcid0000-0001-6792-6500cs
dc.identifier.orcid0000-0002-8604-7365cs
dc.identifier.orcid0000-0002-7397-125Xcs
dc.identifier.other191235cs
dc.identifier.researcheridKIE-3874-2024cs
dc.identifier.researcheridF-2136-2018cs
dc.identifier.researcheridG-9453-2014cs
dc.identifier.researcheridG-9463-2014cs
dc.identifier.researcheridD-6800-2012cs
dc.identifier.scopus57990461800cs
dc.identifier.scopus57190620988cs
dc.identifier.scopus55312098800cs
dc.identifier.scopus7402184758cs
dc.identifier.urihttps://hdl.handle.net/11012/250885
dc.language.isoencs
dc.publisherROYAL SOC CHEMISTRYcs
dc.relation.ispartofJournal of Analytical Atomic Spectrometrycs
dc.relation.urihttps://pubs.rsc.org/en/content/articlelanding/2024/ja/d4ja00247dcs
dc.rightsCreative Commons Attribution-NonCommercial 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-nc/3.0/cs
dc.subjectInduced breakdown spectroscopyen
dc.subjectIdentificationen
dc.subjectPlasmaen
dc.titleAutomated detection of element-specific features in LIBS spectraen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.grantNumberinfo:eu-repo/grantAgreement/GA0/GF/GF23-05186Kcs
sync.item.dbidVAV-191235en
sync.item.dbtypeVAVen
sync.item.insts2025.04.11 10:56:18en
sync.item.modts2025.04.11 10:32:57en
thesis.grantorVysoké učení technické v Brně. Středoevropský technologický institut VUT. Pokročilé instrumentace a metody pro charakterizace materiálůcs
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Ústav fyzikálního inženýrstvícs
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