Artificial neural networks for computer-aided modelling and optimisation in micellar electrokinetic chromatography
Language English Country Netherlands Media print
Document type Journal Article, Research Support, Non-U.S. Gov't
PubMed
10457496
DOI
10.1016/s0021-9673(99)00634-2
PII: S0021-9673(99)00634-2
Knihovny.cz E-resources
- MeSH
- Chromatography, Micellar Electrokinetic Capillary methods MeSH
- Edetic Acid analogs & derivatives chemistry MeSH
- Metals analysis MeSH
- Neural Networks, Computer * MeSH
- Computer Simulation MeSH
- Research Design MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- CDTA MeSH Browser
- Edetic Acid MeSH
- Metals MeSH
The separation process in capillary micellar electrochromatography (MEKC) can be modelled using artificial neural networks (ANNs) and optimisation of MEKC methods can be facilitated by combining ANNs with experimental design. ANNs have shown attractive possibilities for non-linear modelling of response surfaces in MEKC and it was demonstrated that by combining ANN modelling with experimental design, the number of experiments necessary to search and find optimal separation conditions can be reduced significantly. A new general approach for computer-aided optimisation in MEKC has been proposed which, because of its general validity, can also be applied in other separation techniques.
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