Detailed Information on Publication Record
2005
Optimization of solid-phase extraction using artificial neural networks in combination with experimental design for determination of resveratrol by capillary zone electrophoresis in wines
SPANILÁ, Miroslava, Jiří PAZOUREK, Marta FARKOVÁ and Josef HAVELBasic 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
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
10406 Analytical chemistry
Country of publisher
Netherlands
Confidentiality degree
není předmětem státního či obchodního tajemství
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
Tags
International impact, Reviewed
Změněno: 6/7/2007 09:40, RNDr. JUDr. Vladimír Šmíd, CSc.
V originále
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.
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 project |
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