Artificial neural networks for modeling electrophoretic
mobilities of inorganic cations and organic cationic oximes
used as antidote contra nerve paralytic chemical weapons

J 2002

Artificial neural networks for modeling electrophoretic mobilities of inorganic cations and organic cationic oximes used as antidote contra nerve paralytic chemical weapons

MALOVANÁ, Sabina, Borges FRIAS GARCIA a Josef HAVEL

Základní údaje

Originální název

Artificial neural networks for modeling electrophoretic mobilities of inorganic cations and organic cationic oximes used as antidote contra nerve paralytic chemical weapons

Autoři

MALOVANÁ, Sabina (203 Česká republika), Borges FRIAS GARCIA (724 Španělsko) a Josef HAVEL (203 Česká republika, garant)

Vydání

ELECTROPHORESIS, WEINHEIM, WILEY-V C H VERLAG GMBH, 2002, 0173-0835

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10406 Analytical chemistry

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Impakt faktor

Impact factor: 4.325

Kód RIV

RIV/00216224:14310/02:00007968

Organizační jednotka

Přírodovědecká fakulta

UT WoS

000176857000007

Klíčová slova anglicky

Artificial neural networks; cationic oximes; antidote contra nerve paralytic chemical weapons

Štítky

artificial neural networks, cationic oximes

Změněno: 2. 6. 2003 13:10, prof. RNDr. Josef Havel, DrSc.

Anotace

V originále

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.

Návaznosti

GA203/02/1103, projekt VaV

Název: Umělé neuronové sítě a plánování pokusů v analytické chemii, zejména v separačních metodách

Investor: Grantová agentura ČR, Umělé neuronové sítě a plánování pokusů v analytické chemii, zejména v separačních metodách

Citovat

MALOVANÁ, Sabina, Borges FRIAS GARCIA a Josef HAVEL. Artificial neural networks for modeling electrophoretic mobilities of inorganic cations and organic cationic oximes used as antidote contra nerve paralytic chemical weapons. ELECTROPHORESIS. WEINHEIM: WILEY-V C H VERLAG GMBH, 2002, roč. 23, č. 12, s. 1815-1821. ISSN 0173-0835.

@article{485226,
   author = {Malovaná, Sabina and Frias Garcia, Borges and Havel, Josef},
   article_location = {WEINHEIM},
   article_number = {12},
   keywords = {Artificial neural networks; cationic oximes; antidote contra nerve paralytic chemical weapons},
   language = {eng},
   issn = {0173-0835},
   journal = {ELECTROPHORESIS},
   title = {Artificial neural networks for modeling electrophoretic mobilities of inorganic cations and organic cationic oximes used as antidote contra nerve paralytic chemical weapons},
   volume = {23},
   year = {2002}
}

TY  - JOUR
ID  - 485226
AU  - Malovaná, Sabina - Frias Garcia, Borges - Havel, Josef
PY  - 2002
TI  - Artificial neural networks for modeling electrophoretic mobilities of inorganic cations and organic cationic oximes used as antidote contra nerve paralytic chemical weapons
JF  - ELECTROPHORESIS
VL  - 23
IS  - 12
SP  - 1815-1821
EP  - 1815-1821
PB  - WILEY-V C H VERLAG GMBH
SN  - 01730835
KW  - Artificial neural networks
KW  - cationic oximes
KW  - antidote contra nerve paralytic chemical weapons
N2  - 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.
ER  -

MALOVANÁ, Sabina, Borges FRIAS GARCIA a Josef HAVEL. Artificial neural networks for modeling electrophoretic mobilities of inorganic cations and organic cationic oximes used as antidote contra nerve paralytic chemical weapons. \textit{ELECTROPHORESIS}. WEINHEIM: WILEY-V C H VERLAG GMBH, 2002, roč.~23, č.~12, s.~1815-1821. ISSN~0173-0835.

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