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

"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 differe...

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Autores principales: Morzan, Ezequiel, Stripeikis, Jorge, Goicoechea, Héctor, Tudino, Mabel Beatriz
Formato: Artículo de Publicación Periódica
Lenguaje:Inglés
Publicado: info
Materias:
REDES NEURONALES
ESPECTROMETRIA DE ABSORCION ATOMICA
MAGNESIO
Acceso en línea:https://ri.itba.edu.ar/handle/123456789/4129
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Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-4129
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spelling I32-R138-123456789-41292023-01-05T03:00:30Z 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 REDES NEURONALES ESPECTROMETRIA DE ABSORCION ATOMICA MAGNESIO "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." info:eu-repo/date/embargoEnd/2018-02-15 2023-01-03T17:00:26Z 2023-01-03T17:00:26Z 2016-02 Artículo de Publicación Periódica 0169-7439 https://ri.itba.edu.ar/handle/123456789/4129 en info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2015.11.011 application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic REDES NEURONALES
ESPECTROMETRIA DE ABSORCION ATOMICA
MAGNESIO
spellingShingle REDES NEURONALES
ESPECTROMETRIA DE ABSORCION ATOMICA
MAGNESIO
Morzan, Ezequiel
Stripeikis, Jorge
Goicoechea, Héctor
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
topic_facet REDES NEURONALES
ESPECTROMETRIA DE ABSORCION ATOMICA
MAGNESIO
description "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."
format Artículo de Publicación Periódica
author Morzan, Ezequiel
Stripeikis, Jorge
Goicoechea, Héctor
Tudino, Mabel Beatriz
author_facet Morzan, Ezequiel
Stripeikis, Jorge
Goicoechea, Héctor
Tudino, Mabel Beatriz
author_sort Morzan, Ezequiel
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
title_short A novel combination of experimental design and artificial neural networks as an analytical tool for improving performance in thermospray flame furnace atomic absorption spectrometry
title_full A novel combination of experimental design and artificial neural networks as an analytical tool for improving performance in thermospray flame furnace atomic absorption spectrometry
title_fullStr A novel combination of experimental design and artificial neural networks as an analytical tool for improving performance in thermospray flame furnace atomic absorption spectrometry
title_full_unstemmed A novel combination of experimental design and artificial neural networks as an analytical tool for improving performance in thermospray flame furnace atomic absorption spectrometry
title_sort novel combination of experimental design and artificial neural networks as an analytical tool for improving performance in thermospray flame furnace atomic absorption spectrometry
publishDate info
url https://ri.itba.edu.ar/handle/123456789/4129
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