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Application of a series of artificial neural networks to on-site quantitative analysis of lead into real soil samples by laser induced breakdown spectroscopy

hal.structure.identifierLaboratoire Ondes et Matière d'Aquitaine [LOMA]
dc.contributor.authorEL HADDAD, Josette
hal.structure.identifierBureau de Recherches Géologiques et Minières [BRGM]
dc.contributor.authorBRUYÈRE, Delphine
hal.structure.identifierIVEA Solution
dc.contributor.authorISMAËL, Amina
hal.structure.identifierIVEA Solution
dc.contributor.authorGALLOU, G.
hal.structure.identifierBureau de Recherches Géologiques et Minières [BRGM]
dc.contributor.authorLAPERCHE, Valérie
hal.structure.identifierBureau de Recherches Géologiques et Minières [BRGM]
dc.contributor.authorMICHEL, Karine
hal.structure.identifierLaboratoire Ondes et Matière d'Aquitaine [LOMA]
dc.contributor.authorCANIONI, Lionel
hal.structure.identifierLaboratoire Ondes et Matière d'Aquitaine [LOMA]
dc.contributor.authorBOUSQUET, Bruno
dc.date.created2014-01-25
dc.date.issued2014-07-01
dc.identifier.issn0584-8547
dc.description.abstractEnArtificial neural networks were applied to process data from on-site LIBS analysis of soil samples. A first artificial neural network allowed retrieving the relative amounts of silicate, calcareous and ores matrices into soils. As a consequence, each soil sample was correctly located inside the ternary diagram characterized by these three matrices, as verified by ICP-AES. Then a series of artificial neural networks were applied to quantify lead into soil samples. More precisely, two models were designed for classification purpose according to both the type of matrix and the range of lead concentrations. Then, three quantitative models were locally applied to three data subsets. This complete approach allowed reaching a relative error of prediction close to 20%, considered as satisfying in the case of on-site analysis.
dc.language.isoen
dc.publisherElsevier
dc.rights.urihttp://creativecommons.org/licenses/by-nc/
dc.subject.enArtificial neural network
dc.subject.enLIBS
dc.subject.enSoil
dc.subject.enLead
dc.subject.enMatrix
dc.title.enApplication of a series of artificial neural networks to on-site quantitative analysis of lead into real soil samples by laser induced breakdown spectroscopy
dc.typeArticle de revue
dc.identifier.doi10.1016/j.sab.2014.04.014
dc.subject.halPhysique [physics]/Physique [physics]/Optique [physics.optics]
dc.subject.halPlanète et Univers [physics]/Sciences de la Terre
bordeaux.journalSpectrochimica Acta Part B: Atomic Spectroscopy
bordeaux.page57-64
bordeaux.volume97
bordeaux.peerReviewedoui
hal.identifierhal-01025457
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01025457v1
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