Artificial neural network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy
| hal.structure.identifier | Laboratoire Ondes et Matière d'Aquitaine [LOMA] | |
| dc.contributor.author | EL HADDAD, Josette | |
| hal.structure.identifier | IVEA Solution | |
| dc.contributor.author | VILLOT-KADRI, M. | |
| hal.structure.identifier | IVEA Solution | |
| dc.contributor.author | ISMAEL, Amina | |
| hal.structure.identifier | IVEA Solution | |
| dc.contributor.author | GALLOU, G. | |
| hal.structure.identifier | Bureau de Recherches Géologiques et Minières [BRGM] | |
| dc.contributor.author | MICHEL, Karine | |
| hal.structure.identifier | Bureau de Recherches Géologiques et Minières [BRGM] | |
| dc.contributor.author | BRUYÈRE, Delphine | |
| hal.structure.identifier | Bureau de Recherches Géologiques et Minières [BRGM] | |
| dc.contributor.author | LAPERCHE, Valérie | |
| hal.structure.identifier | Laboratoire Ondes et Matière d'Aquitaine [LOMA] | |
| dc.contributor.author | CANIONI, Lionel | |
| hal.structure.identifier | Laboratoire Ondes et Matière d'Aquitaine [LOMA] | |
| dc.contributor.author | BOUSQUET, Bruno | |
| dc.date.created | 2012-09-13 | |
| dc.date.issued | 2013-02-01 | |
| dc.identifier.issn | 0584-8547 | |
| dc.description.abstractEn | Nowadays, due to environmental concerns, fast on-site quantitative analyses of soils are required. Laser induced breakdown spectroscopy is a serious candidate to address this challenge and is especially well suited for multi-elemental analysis of heavy metals. However, saturation and matrix effects prevent from a simple treatment of the LIBS data, namely through a regular calibration curve. This paper details the limits of this approach and consequently emphasizes the advantage of using artificial neural networks well suited for non-linear and multi-variate calibration. This advanced method of data analysis is evaluated in the case of real soil samples and on-site LIBS measurements. The selection of the LIBS data as input data of the network is particularly detailed and finally, resulting errors of prediction lower than 20% for aluminum, calcium, copper and iron demonstrate the good efficiency of the artificial neural networks for on-site quantitative LIBS of soils. | |
| dc.language.iso | en | |
| dc.publisher | Elsevier | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/ | |
| dc.subject.en | Laser-induced breakdown spectroscopy (LIBS) | |
| dc.subject.en | Soil | |
| dc.subject.en | Quantitative analysis | |
| dc.subject.en | Artificial neural network | |
| dc.title.en | Artificial neural network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy | |
| dc.type | Article de revue | |
| dc.identifier.doi | 10.1016/j.sab.2012.11.007 | |
| dc.subject.hal | Physique [physics]/Physique [physics]/Optique [physics.optics] | |
| bordeaux.journal | Spectrochimica Acta Part B: Atomic Spectroscopy | |
| bordeaux.page | 51-57 | |
| bordeaux.volume | 78-79 | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-00788952 | |
| hal.version | 1 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-00788952v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Spectrochimica%20Acta%20Part%20B:%20Atomic%20Spectroscopy&rft.date=2013-02-01&rft.volume=78-79&rft.spage=51-57&rft.epage=51-57&rft.eissn=0584-8547&rft.issn=0584-8547&rft.au=EL%20HADDAD,%20Josette&VILLOT-KADRI,%20M.&ISMAEL,%20Amina&GALLOU,%20G.&MICHEL,%20Karine&rft.genre=article |
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