Simultaneous optimization by neuro-genetic approach for analysis of plant materials by laser induced breakdown spectroscopy

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dc.contributor.authorNunes, Lidiane Cristina
dc.contributor.authorSilva, Gilmare Antonia da
dc.contributor.authorTrevizan, Lilian Cristina
dc.contributor.authorSantos Junior, Dario [UNIFESP]
dc.contributor.authorPoppi, Ronei Jesus
dc.contributor.authorKrug, Francisco Jose
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.contributor.institutionUniv Fed Ouro Preto
dc.contributor.institutionUniversidade Federal de São Paulo (UNIFESP)
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.date.accessioned2016-01-24T13:52:36Z
dc.date.available2016-01-24T13:52:36Z
dc.date.issued2009-06-01
dc.description.abstractA simultaneous optimization strategy based on a neuro-genetic approach is proposed for selection of laser induced breakdown spectroscopy operational conditions for the simultaneous determination of macronutrients (Ca, Mg and P), micro-nutrients (B, Cu, Fe, Mn and Zn), Al and Si in plant samples. A laser induced breakdown spectroscopy system equipped with a 10 Hz Q-switched Nd:YAG laser (12 ns, 532 nm, 140 mJ) and an Echelle spectrometer with intensified coupled-charge device was used. Integration time gate, delay time, amplification gain and number of pulses were optimized. Pellets of spinach leaves (NIST 1570a) were employed as laboratory samples. in order to find a model that could correlate laser induced breakdown spectroscopy operational conditions with compromised high peak areas of all elements simultaneously, a Bayesian Regularized Artificial Neural Network approach was employed. Subsequently, a genetic algorithm was applied to find optimal conditions for the neural network model, in an approach called neuro-genetic, A single laser induced breakdown spectroscopy working condition that maximizes peak areas of all elements simultaneously, was obtained with the following optimized parameters: 9.0 mu s integration time gate, 1.1 mu s delay time, 225 (a.u.) amplification gain and 30 accumulated laser pulses. the proposed approach is a useful and a suitable tool for the optimization process of such a complex analytical problem. (C) 2009 Elsevier B.V. All rights reserved.en
dc.description.affiliationUniv São Paulo, Ctr Energia Nucl Agr, BR-13416000 Piracicaba, SP, Brazil
dc.description.affiliationUniv Fed Sao Carlos, Dept Quim, BR-13565905 Sao Carlos, SP, Brazil
dc.description.affiliationUniv Fed Ouro Preto, Dept Quim, BR-35400000 Ouro Preto, MG, Brazil
dc.description.affiliationUniversidade Federal de São Paulo, BR-09972270 Diadema, SP, Brazil
dc.description.affiliationUniv Estadual Campinas, Inst Quim, BR-13084971 Campinas, SP, Brazil
dc.description.affiliationUnifespUniversidade Federal de São Paulo, BR-09972270 Diadema, SP, Brazil
dc.description.sourceWeb of Science
dc.format.extent565-572
dc.identifierhttp://dx.doi.org/10.1016/j.sab.2009.05.002
dc.identifier.citationSpectrochimica Acta Part B-atomic Spectroscopy. Oxford: Pergamon-Elsevier B.V., v. 64, n. 6, p. 565-572, 2009.
dc.identifier.doi10.1016/j.sab.2009.05.002
dc.identifier.issn0584-8547
dc.identifier.urihttp://repositorio.unifesp.br/handle/11600/31576
dc.identifier.wosWOS:000269417400017
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofSpectrochimica Acta Part B-atomic Spectroscopy
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.subjectLaser induced breakdown spectroscopy (LIBS)en
dc.subjectPlant analysisen
dc.subjectBayesian regularized neural networken
dc.subjectGenetic algorithmen
dc.subjectSimultaneous optimizationen
dc.titleSimultaneous optimization by neuro-genetic approach for analysis of plant materials by laser induced breakdown spectroscopyen
dc.typeinfo:eu-repo/semantics/article
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