Artificial neural networks for modeling electrophoretic mobilities of inorganic cations and organic cationic oximes used as antidote contra nerve paralytic chemical weapons.
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.
Department of Analytical Chemistry, Faculty of Science, Masaryk University, Brno, Czech Republic.
Sabina
S
Malovaná
Sergio
S
Frías-García
Josef
J
Havel
D000931:Antidotes / Q000737:chemistry*; D002412:Cations; D002619:Chemical Warfare Agents / Q000009:adverse effects; D003198:Computer Simulation; D019075:Electrophoresis, Capillary; D008958:Models, Molecular; D016571:Neural Networks, Computer; D010091:Oximes / Q000737:chemistry*; D010243:Paralysis / Q000139:chemically induced; D011220:Pralidoxime Compounds / Q000737:chemistry*
D000931:Antidotes; D002412:Cations; D002619:Chemical Warfare Agents; D010091:Oximes; D011220:Pralidoxime Compounds; C028797:pralidoxime