Artificial neural networks for computer-aided modelling and optimisation in micellar electrokinetic chromatography
Jazyk angličtina Země Nizozemsko Médium print
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
10457496
DOI
10.1016/s0021-9673(99)00634-2
PII: S0021-9673(99)00634-2
Knihovny.cz E-zdroje
- MeSH
- chromatografie micelární elektrokinetická kapilární metody MeSH
- EDTA analogy a deriváty chemie MeSH
- kovy analýza MeSH
- neuronové sítě * MeSH
- počítačová simulace MeSH
- výzkumný projekt MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- CDTA MeSH Prohlížeč
- EDTA MeSH
- kovy 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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