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dc.contributor.author
Morzan, Ezequiel Martin
dc.contributor.author
Stripeikis, Jorge Daniel
dc.contributor.author
Goicoechea, Hector Casimiro
dc.contributor.author
Tudino, Mabel Beatriz
dc.date.available
2018-08-17T16:55:20Z
dc.date.issued
2016-02
dc.identifier.citation
Morzan, Ezequiel Martin; Stripeikis, Jorge Daniel; Goicoechea, Hector Casimiro; Tudino, Mabel Beatriz; A novel combination of experimental design and artificial neural networks as an analytical tool for improving performance in thermospray flame furnace atomic absorption spectrometry; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 151; 2-2016; 44-50
dc.identifier.issn
0169-7439
dc.identifier.uri
http://hdl.handle.net/11336/56179
dc.description.abstract
In this work, we present the combined effect of artificial neural networks (ANN) and experimental design as a suitable analytical tool for improving the performance of thermospray flame furnace atomic absorption spectrometry (TS-FFAAS) using Mg as leading case. To this end, mixtures of different amounts of methanol, ethanol, and i-propanol inwaterwere assayed as carriers at different flow rates and different flame stoichiometries (air/acetylene ratios). Different levels of these variables determined the experimental domain, consisting in a cube whichwas divided into eight identical cubical regions that allowed increase in the number of available experimental points. A Box-Behnken design (BBD) was employed in each one of the regions. The nameMultiple Box-Behnken design (MBBD)was given to this newapproach. Then, the features of ANN were exploited to find the optimum conditions for conducting Mg determination by TS-FFAAS. The prediction capability of ANN was examined and compared to the least-squares (LS) fitting when applied to the response surface method (RSM). The suitability of the new approach and the implications on TS-FFAAS analytical performance are discussed.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Ann
dc.subject
Experimental Design
dc.subject
Thermospray Flame Furnace Atomic Absorption Spectrometry
dc.subject.classification
Otras Ciencias Químicas
dc.subject.classification
Ciencias Químicas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
A novel combination of experimental design and artificial neural networks as an analytical tool for improving performance in thermospray flame furnace atomic absorption spectrometry
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-08-16T18:08:31Z
dc.journal.volume
151
dc.journal.pagination
44-50
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Morzan, Ezequiel Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; Argentina
dc.description.fil
Fil: Stripeikis, Jorge Daniel. Instituto Tecnológico de Buenos Aires. Departamento de Ingeniería Química; Argentina
dc.description.fil
Fil: Goicoechea, Hector Casimiro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina
dc.description.fil
Fil: Tudino, Mabel Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; Argentina
dc.journal.title
Chemometrics and Intelligent Laboratory Systems
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://dx.doi.org/10.1016/j.chemolab.2015.11.011
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0169743915003196


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