Optimization of solid-phase extraction using artificial neural networks in combination with experimental design for determination of resveratrol by capillary zone electrophoresis in wines
| Authors | |
|---|---|
| Year of publication | 2005 |
| Type | Article in Periodical |
| Magazine / Source | J. Chromatogr. A. |
| MU Faculty or unit | |
| 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. |
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