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 different amo...
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_01697439_v151_n_p44_Morzan |
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todo:paper_01697439_v151_n_p44_Morzan2023-10-03T15:07:16Z 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, E. Stripeikis, J. Goicoechea, H. Tudino, M. ANN Experimental design Thermospray flame furnace atomic absorption spectrometry acetylene alcohol magnesium methanol propanol water analytic method Article artificial neural network atomic absorption spectrometry experimental design flow rate intermethod comparison predictive value priority journal regression analysis response surface method stoichiometry 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 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. © 2015 Elsevier B.V. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_01697439_v151_n_p44_Morzan |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
ANN Experimental design Thermospray flame furnace atomic absorption spectrometry acetylene alcohol magnesium methanol propanol water analytic method Article artificial neural network atomic absorption spectrometry experimental design flow rate intermethod comparison predictive value priority journal regression analysis response surface method stoichiometry thermospray flame furnace atomic absorption spectrometry |
| spellingShingle |
ANN Experimental design Thermospray flame furnace atomic absorption spectrometry acetylene alcohol magnesium methanol propanol water analytic method Article artificial neural network atomic absorption spectrometry experimental design flow rate intermethod comparison predictive value priority journal regression analysis response surface method stoichiometry thermospray flame furnace atomic absorption spectrometry Morzan, E. Stripeikis, J. Goicoechea, H. Tudino, M. 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 |
ANN Experimental design Thermospray flame furnace atomic absorption spectrometry acetylene alcohol magnesium methanol propanol water analytic method Article artificial neural network atomic absorption spectrometry experimental design flow rate intermethod comparison predictive value priority journal regression analysis response surface method stoichiometry thermospray flame furnace atomic absorption spectrometry |
| 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 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. © 2015 Elsevier B.V. |
| format |
JOUR |
| author |
Morzan, E. Stripeikis, J. Goicoechea, H. Tudino, M. |
| author_facet |
Morzan, E. Stripeikis, J. Goicoechea, H. Tudino, M. |
| author_sort |
Morzan, E. |
| 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 |
| url |
http://hdl.handle.net/20.500.12110/paper_01697439_v151_n_p44_Morzan |
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1807319169538981888 |