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

作者：Morzan Ezequiel; Stripeikis Jorge; Goicoechea Hector; Tudino Mabel

来源：Chemometrics and Intelligent Laboratory Systems, 2016, 151: 44-50.

DOI：10.1016/j.chemolab.2015.11.011

• ANN
• Experimental design
• 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 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.

• 出版日期2016-2-15