Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/47152
DC FieldValueLanguage
dc.contributor.authorSuárez Araujo, Carmen Pazen_US
dc.contributor.authorGarcía Báez, Patricioen_US
dc.contributor.authorSánchez Rodríguez, Álvaroen_US
dc.contributor.authorSantana Rodríguez, José Juanen_US
dc.date.accessioned2018-11-23T11:15:18Z-
dc.date.available2018-11-23T11:15:18Z-
dc.date.issued2009en_US
dc.identifier.issn1618-2642en_US
dc.identifier.urihttp://hdl.handle.net/10553/47152-
dc.description.abstractIn this paper, we approach, using neural computation and ensemble systems, a pattern classification problem in fluorescence spectrometry, the resolution of difficult multi-fungicide mixtures (overlapping), specifically the benzimidazole fungicides, benomyl, carbendazim, thiabendazole and fuberidazole. These fungicides are compounds of an important environmental interest. Because of this, from an analytical point of view, it is interesting to develop sensitive, selective and simple methods for their determination. Fluorescence spectrometry has proven to be a sensitive and selective technique for determination of many compounds of environmental interest, but in some cases it is not enough. HUMANN is a hierarchical, unsupervised, modular, adaptive neural net with high biological plausibility, which has shown to be suitable for identification of these fungicides and organochlorinated compounds of environmental interest. We propose two modular artificial intelligent systems, with a structure of pre-processing and processing stage, a multi-input HUMANN-based system, using multi-fluorescence spectra as input to the system, and a HUMANN-ensemble system. We analyze the optimal configuration of inputs and the ensemble in order to obtain better results. We study such figures as precision and sensitivity of the method. Our proposal is a smart, flexible and effective complementary method, which allows reducing the analytical and/or computational complexity of the analysis.en_US
dc.languageengen_US
dc.relation.ispartofAnalytical and Bioanalytical Chemistryen_US
dc.sourceAnalytical and Bioanalytical Chemistry [ISSN 1618-2642], v. 394 (4), p. 1059-1072en_US
dc.subject120304 Inteligencia artificialen_US
dc.subject3308 Ingeniería y tecnología del medio ambienteen_US
dc.subject.otherBenzimidazole fungicidesen_US
dc.subject.otherEnsemble systemen_US
dc.subject.otherEnvironmenten_US
dc.subject.otherFluorescence spectrometryen_US
dc.subject.otherHUMANNen_US
dc.subject.otherUnsupervised artificial neural networken_US
dc.titleHUMANN-based system to identify benzimidazole fungicides using multi-synchronous fluorescence spectra: an ensemble approachen_US
dc.typeinfo:eu-repo/semantics/Articlees
dc.typeArticlees
dc.relation.conference13th International Symposium on Luminescence Spectrometry
dc.identifier.doi10.1007/s00216-009-2654-7
dc.identifier.scopus67349156664-
dc.identifier.isi000266160400016-
dc.contributor.authorscopusid6603605708-
dc.contributor.authorscopusid6506952458-
dc.contributor.authorscopusid26039375100-
dc.contributor.authorscopusid56248783900-
dc.identifier.eissn1618-2650-
dc.description.lastpage1072-
dc.identifier.issue4-
dc.description.firstpage1059-
dc.relation.volume394-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngid1776211
dc.contributor.daisngid2362390
dc.contributor.daisngid6474544
dc.contributor.daisngid13402121
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Araujo, CPS
dc.contributor.wosstandardWOS:Baez, PG
dc.contributor.wosstandardWOS:Rodriguez, AS
dc.contributor.wosstandardWOS:Rodriguez, JJS
dc.date.coverdateJunio 2009
dc.identifier.conferenceidevents120672
dc.identifier.ulpgces
dc.description.jcr3,48
dc.description.jcrqQ1
dc.description.scieSCIE
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptGIR IUCES: Computación inteligente, percepción y big data-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.deptGIR IUCES: Computación inteligente, percepción y big data-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.deptGIR Energía, Corrosión, Residuos y Agua-
crisitem.author.deptDepartamento de Ingeniería de Procesos-
crisitem.author.orcid0000-0002-8826-0899-
crisitem.author.orcid0000-0002-9973-5319-
crisitem.author.orcid0000-0002-3030-2195-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.parentorgDepartamento de Ingeniería Electrónica y Automática-
crisitem.author.fullNameSuárez Araujo, Carmen Paz-
crisitem.author.fullNameGarcía Baez, Patricio-
crisitem.author.fullNameSantana Rodríguez, Juan José-
crisitem.event.eventsstartdate07-09-2008-
crisitem.event.eventsenddate11-09-2008-
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