Artificial neural networks for computer-aided modelling and optimisation in micellar electrokinetic chromatography

被引：37
|
作者：
Havel, J
Breadmore, M
Macka, M
Haddad, PR
机构：
[1] Univ Tasmania, Sch Chem, Hobart, Tas 7001, Australia
[2] Masaryk Univ, Fac Sci, Dept Analyt Chem, Brno 61137, Czech Republic
来源：
JOURNAL OF CHROMATOGRAPHY A | 1999年 / 850卷 / 1-2期
关键词：
neural networks; artificial; optimisation; computer modelling; experimental design; metal complexes;
D O I：
10.1016/S0021-9673(99)00634-2
中图分类号：
Q5 [生物化学];
学科分类号：
071010 ; 081704 ;
摘要：
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 neu; 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. (C) 1999 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：345 / 353
页数：9
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