SPANILÁ, Miroslava, Jiří PAZOUREK, Marta FARKOVÁ and Josef HAVEL. Optimization of solid-phase extraction using artificial neural networks in combination with experimental design for determination of resveratrol by capillary zone electrophoresis in wines. J. Chromatogr. A. Amsterdam (The Netherlands): Elsevier Science, 2005, vol. 1084, No 1, p. 180-185. ISSN 0021-9606.
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Basic information
Original name Optimization of solid-phase extraction using artificial neural networks in combination with experimental design for determination of resveratrol by capillary zone electrophoresis in wines
Name in Czech 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 SPANILÁ, Miroslava (203 Czech Republic), Jiří PAZOUREK (203 Czech Republic, guarantor), Marta FARKOVÁ (203 Czech Republic) and Josef HAVEL (203 Czech Republic).
Edition J. Chromatogr. A. Amsterdam (The Netherlands), Elsevier Science, 2005, 0021-9606.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10406 Analytical chemistry
Country of publisher Netherlands
Confidentiality degree is not subject to a state or trade secret
Impact factor Impact factor: 3.138
RIV identification code RIV/00216224:14330/05:00020146
Organization unit Faculty of Informatics
UT WoS 000230862600026
Keywords in English Solid-phase extraction; Artificial neural networks; Experimental design; Single variable approach; Multivariable approach; Capillary electrophoresis; trans-resveratrol
Tags artificial neural networks, Capillary electrophoresis, experimental design, Multivariable approach, Single variable approach, Solid-Phase Extraction, trans-Resveratrol
Tags International impact, Reviewed
Changed by Changed by: RNDr. JUDr. Vladimír Šmíd, CSc., učo 1084. Changed: 6/7/2007 09:40.
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). 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.
Abstract (in Czech)
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.
Links
GA203/02/1103, research and development projectName: Umělé neuronové sítě a plánování pokusů v analytické chemii, zejména v separačních metodách
Investor: Czech Science Foundation, Artificial neural networks and experimental design in analytical chemistry, especially in separation methods
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