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

Abstract

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)). However, if the optimized procedure is complex, there is a danger not to find the real optimum by SVA. Therefore, more advanced optimization approaches should be applied—multivariable approach (MVA). Applying MVA optimization and finding the real optimum, better experimental conditions are obtained and thus, time, chemicals and analytical procedure cost can be served. Nowadays, using artificial neural networks (ANN's) in combination with MVA is rapidly expanding. In this work, the optimization of SPE using relaxation method (SVA) and optimization by ANN's in combination with experimental design (MVA) are compared and 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.