A novel combination of experimental design and artificial neural networks as an analytical tool for improving performance in thermospray flame furnace atomic absorption spectrometry

Morzan, Ezequiel; Stripeikis, Jorge; Goicoechea, Héctor; Tudino, Mabel Beatriz

artículo de publicación periódica.page.titleprefix
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.contributor.author	Morzan, Ezequiel
dc.contributor.author	Stripeikis, Jorge
dc.contributor.author	Goicoechea, Héctor
dc.contributor.author	Tudino, Mabel Beatriz
dc.date	info:eu-repo/date/embargoEnd/2018-02-15
dc.date.accessioned	2023-01-03T17:00:26Z
dc.date.available	2023-01-03T17:00:26Z
dc.date.issued	2016-02
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 in water were 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 which was 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 name Multiple Box–Behnken design (MBBD) was given to this new approach. 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."	en
dc.identifier.issn	0169-7439
dc.identifier.uri	https://ri.itba.edu.ar/handle/123456789/4129
dc.language.iso	en
dc.relation	info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2015.11.011
dc.subject	REDES NEURONALES	es
dc.subject	ESPECTROMETRIA DE ABSORCION ATOMICA	es
dc.subject	MAGNESIO	es
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	en
dc.type	Artículo de Publicación Periódica	esd
dspace.entity.type	Artículo de Publicación Periódica
itba.description.filiation	Fil: Morzan, Ezequiel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
itba.description.filiation	Fil: Stripeikis, Jorge. Instituto Tecnológico de Buenos Aires; Argentina.
itba.description.filiation	Fil: Goicoechea, Héctor. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina.
itba.description.filiation	Fil: Goicoechea, Héctor. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
itba.description.filiation	Fil: Tudino, Mabel Beatriz. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.