Näytä suppeat kuvailutiedot

dc.contributor.authorRomppanen, Sari
dc.contributor.authorPölönen, Ilkka
dc.contributor.authorHäkkänen, Heikki
dc.contributor.authorKaski, Saara
dc.date.accessioned2021-11-17T07:08:55Z
dc.date.available2021-11-17T07:08:55Z
dc.date.issued2023
dc.identifier.citationRomppanen, S., Pölönen, I., Häkkänen, H., & Kaski, S. (2023). Optimization of spodumene identification by statistical approach for laser-induced breakdown spectroscopy data of lithium pegmatite ores. <i>Applied Spectroscopy Reviews</i>, <i>58</i>(5), 297-317. <a href="https://doi.org/10.1080/05704928.2021.1963977" target="_blank">https://doi.org/10.1080/05704928.2021.1963977</a>
dc.identifier.otherCONVID_99344527
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/78680
dc.description.abstractMapping with laser-induced breakdown spectroscopy (LIBS) can offer more than just the spatial distribution of elements: the rich spectral information also enables mineral recognition. In the present study, statistical approaches were used for the recognition of the spodumene from lithium pegmatite ores. A broad spectral range (280–820 nm) with multiple lines was first used to establish the methods based on vertex component analysis (VCA) and K-means and DBSCAN clusterings. However, with a view to potential on-site applications, the dimensions of the datasets must be reduced in order to accomplish fast analysis. Therefore, the capability of the methods in mineral identification was tested with a limited spectral range (560–815 nm) using Li-pegmatites with various mineralogical characters.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherTaylor & Francis
dc.relation.ispartofseriesApplied Spectroscopy Reviews
dc.rightsCC BY-NC 4.0
dc.subject.otherlithium pegmatite ore
dc.subject.otherLIBS
dc.subject.otherVCA
dc.subject.otherK-means
dc.subject.otherDBSCAN
dc.titleOptimization of spodumene identification by statistical approach for laser-induced breakdown spectroscopy data of lithium pegmatite ores
dc.typeresearch article
dc.identifier.urnURN:NBN:fi:jyu-202111175691
dc.contributor.laitosBio- ja ympäristötieteiden laitosfi
dc.contributor.laitosKemian laitosfi
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosDepartment of Biological and Environmental Scienceen
dc.contributor.laitosDepartment of Chemistryen
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineSolu- ja molekyylibiologiafi
dc.contributor.oppiaineNanoscience Centerfi
dc.contributor.oppiaineFysikaalinen kemiafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.contributor.oppiaineCell and Molecular Biologyen
dc.contributor.oppiaineNanoscience Centeren
dc.contributor.oppiainePhysical Chemistryen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange297-317
dc.relation.issn0570-4928
dc.relation.numberinseries5
dc.relation.volume58
dc.type.versionacceptedVersion
dc.rights.copyright© 2021 Taylor & Francis Group, LLC
dc.rights.accesslevelopenAccessfi
dc.type.publicationarticle
dc.subject.ysooptimointi
dc.subject.ysospektroskopia
dc.subject.ysotilastomenetelmät
dc.subject.ysoalkuaineanalyysi
dc.subject.ysomalmimineraalit
dc.subject.ysomineraalit
dc.subject.ysolitium
dc.subject.ysopegmatiitit
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p13477
jyx.subject.urihttp://www.yso.fi/onto/yso/p10176
jyx.subject.urihttp://www.yso.fi/onto/yso/p3127
jyx.subject.urihttp://www.yso.fi/onto/yso/p10172
jyx.subject.urihttp://www.yso.fi/onto/yso/p12444
jyx.subject.urihttp://www.yso.fi/onto/yso/p2368
jyx.subject.urihttp://www.yso.fi/onto/yso/p29475
jyx.subject.urihttp://www.yso.fi/onto/yso/p39189
dc.rights.urlhttps://creativecommons.org/licenses/by-nc/4.0/
dc.relation.doi10.1080/05704928.2021.1963977
jyx.fundinginformationThis study was financially supported by the K.H. Renlund’s Foundation (S.R.).
dc.type.okmA1


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