Optimization of solid-phase extraction using artificial neural networks in combination with experimental design for determination of resveratrol by capillary zone electrophoresis in wines

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Authors	SPANILÁ Miroslava PAZOUREK Jiří FARKOVÁ Marta HAVEL Josef
Year of publication	2005
Type	Article in Periodical
Magazine / Source	J. Chromatogr. A.
MU Faculty or unit	Faculty of Informatics
Citation
Field	Analytic chemistry
Keywords	Solid-phase extraction; Artificial neural networks; Experimental design; Single variable approach; Multivariable approach; Capillary electrophoresis; trans-resveratrol
Description	Solid-phase extraction (SPE) is often used for preconcentration of analytes from biological samples. Such an analytical step requires optimization for obtaining reliable results. Optimization in analytical chemistry is traditionally still often done with relaxation method, when an optimal value of a single variable is searched for (single variable approach, SVA). Nowadays, using artificial neural networks (ANN) as a multivariable approach (MVA) in optimization is rapidly expanding. In this work, the optimization of SPE using relaxation method (SVA) and optimization by ANN in combination with experimental design (MVA) are compared and the latter approach is practically illustrated. Advantages of MVA over SVA for optimization are discussed. The prediction of the optimal SPE conditions for determination cis- and trans-resveratrol in Australian wines by capillary zone electrophoresis is described and the improvement of efficiency of SPE using MVA is confirmed.
Related projects:	Artificial neural networks and experimental design in analytical chemistry, especially in separation methods