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dc.rights.licensehttp://creativecommons.org/licenses/by-nc-sa/3.0/ve/
dc.contributor.authorHernández C., Edwin A.es_VE
dc.contributor.authorPascu de Burguera, Marcelaes_VE
dc.contributor.authorBurguera, José Luises_VE
dc.contributor.authorÁvila G., Rita M.es_VE
dc.contributor.authorRivas E., Francklin I.es_VE
dc.date2006-01-31es_VE
dc.date.accessioned2006-01-31T09:00:00Z
dc.date.available2006-01-31T09:00:00Z
dc.date.created2003-11-01es_VE
dc.date.issued2006-01-31T09:00:00Zes_VE
dc.identifier.otherT016300002612/0es_VE
dc.identifier.urihttp://www.saber.ula.ve/handle/123456789/16007
dc.description.abstractIncreasing the working calibration range by means of artificial neural networks for the determination of cadmium by graphite furnace atomic absorption spectrometry (Hernández C., Edwin A.; Ávila G., Rita M.; Rivas E., Francklin I.; Burguera, Marcela y Burguera, José Luis) Abstract Feed-forward artificial neural networks (ANNs), trained with the generalized delta rule, were evaluated for modeling the non-linear behavior of calibration curves and increasing the working range for the determination of cadmium by graphite furnace atomic absorption spectrometry (GFAAS). Selection of this analyte was made on the basis of its short linear range (up to 4.0 µg l-1). Two-layer neural networks, comprising one node in the input layer (linear transfer function); a variable number of neurons in the hidden layer (sigmoid transfer functions), and a single neuron (linear transfer function) in the output layer were assessed for such a purpose. The (1:2:1) neural network was selected on the basis of its capacity to adequately model the working calibration curve in the range of study (0-22.0 µg l-1 Cd). The latter resulted in a nearly six fold increase in the working range. Cadmium was determined in the certified reference material "Trace Elements in DrinkingWater" (High Purity Standards, Lot No. 490915) at four concentration levels (2.0, 4.0, 8.0 and 12.0 µg l-1 Cd), which were experimentally within and above the linear dynamic range (LDR). No significant differences (P < 0.05) were found between the expected concentrations and the results obtained by means of the neural network. The proposed method was compared with the conventional "dilution" approach, and with fitting the working calibration curve by means of a second-order polynomial. Modeling by means of an ANN represents an alternative calibration technique, for its use helps in reducing sample manipulation (due to the extension of the working calibration range), and may provide higher accuracy of the determinations in the non-linear portion of the curve (as a result of the better fitness of the model). Artículo publicado en: Talanta 63 (2004) 425-431es_VE
dc.format.extent114851es_VE
dc.language.isoeses_VE
dc.publisherSABER ULAes_VE
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleIncreasing the working calibration range by means of artificial neural networks for the determination of cadmium by graphite furnace atomic absorption spectrometryes_VE
dc.typeinfo:eu-repo/semantics/article
dc.description.emailehernandez@ucla.edu.vees_VE
dc.description.emailpascu@ula.vees_VE
dc.description.emailburguera@ula.vees_VE
dc.description.emailritaavila@ucla.edu.vees_VE
dc.description.emailrivas@ula.vees_VE
dc.description.tiponivelNivel monográficoes_VE
dc.subject.escuelaEscuela de Ingeniería de Sistemases_VE
dc.subject.facultadFacultad de Ciencias.es_VE
dc.subject.facultadFacultad de Ingenieríaes_VE
dc.subject.keywordsCadmiumes_VE
dc.subject.keywordsCalibration curveses_VE
dc.subject.keywordsGraphite furnace atomic absorption spectrometry (GFAAS)es_VE
dc.subject.keywordsArtificial neural networks (ANNs)es_VE
dc.subject.keywordsBackpropagationes_VE
dc.subject.laboratorioLaboratorio de Investigación de Sistemas Inteligentes (LABSIULA)es_VE
dc.subject.tipoArtículoses_VE


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