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Artificial neural networks for modeling electrophoretic mobilities of inorganic cations and organic cationic oximes used as antidote contra nerve paralytic chemical weapons
Author(s) -
Malovaná Sabina,
FríasGarcía* Sergio,
Havel Josef
Publication year - 2002
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/1522-2683(200206)23:12<1815::aid-elps1815>3.0.co;2-9
Subject(s) - cationic polymerization , artificial neural network , biological system , electrophoresis , chemistry , analyte , nerve agent , methanol , computer science , materials science , chromatography , artificial intelligence , organic chemistry , acetylcholinesterase , biology , enzyme
Electrophoretic mobility of various analytes can be modeled and thus also predicted using artificial neural networks (ANNs) evaluating experiments done according to a suitable experimental design. In contrast to response surfaces modeling which can be used to predict optimal separation conditions, ANNs combined with experimental design were shown to be efficient for modeling and prediction of optimal separation conditions, while no explicit model and any knowledge of the physicochemical constants is needed. Methodology has been developed and demonstrated on separation of inorganic cations and organic oximes while various additives (methanol, complexation agent), pH or buffer concentration were followed. In our approach proposed the number of experiments necessary to find optimal separation conditions can be reduced significantly.