ANN Prediction of Migration Behaviour of Metal Complexes in
Capillary Zone Electrophoresis

D 2002

ANN Prediction of Migration Behaviour of Metal Complexes in Capillary Zone Electrophoresis

LUBAL, Přemysl; Josef HAVEL a Marta FARKOVÁ

Základní údaje

Originální název

ANN Prediction of Migration Behaviour of Metal Complexes in Capillary Zone Electrophoresis

Autoři

LUBAL, Přemysl; Josef HAVEL a Marta FARKOVÁ

Vydání

Brno, Book of Abstracts of International Chemometric Conference Chemometrics VI, od s. P14, 1 s. 2002

Nakladatel

Masaryk University Press

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10406 Analytical chemistry

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14310/02:00007261

Organizační jednotka

Přírodovědecká fakulta

ISBN

80-210-2918-8

Klíčová slova anglicky

artificial neural networks; metal complexes; capillary zone electrophoresis

Štítky

artificial neural networks, Capillary Zone Electrophoresis, metal complexes

Příznaky

Mezinárodní význam

Změněno: 25. 2. 2013 12:40, RNDr. Marta Farková, CSc.

Anotace

V originále

The knowledge of migration behaviour is important in order to find the optimal experimental conditions for analyte determination by electromigration techniques, e.g. capillary zone electrophoresis (CZE). The metal ion analysis by means of CZE was possible as a consequence of application metal complexes with various organic and inorganic ligands. The metal complexes are responsible for metal ion speciation in solution and their mobility in environment. Recently, the systematic analysis of relationships between migration parameters and charge and size characteristics of metal complexes was done [1]. This quantitative structure-mobility relationships (QSMR's) are based on a function of electrophoretic mobility using enlarged Stokes-Einstein diffusion model with structural descriptors (metal atom electronegativity or effective charge) as well as the formal charge and ligand number [1]. The migration parameters are also dependent on experimental conditions (ionic strength, temperature, etc.). In this contribution, the method of migration behaviour prediction for different metal organic and inorganic complexes using "soft" modelling with artificial neural networks (ANN's) was examined on data taken from literature [1]. The achieved results were compared with values obtained by "hard" modelling (QSMR's). The proposed methodology allows to predict migration parameters and is not dependent on applied relationship. This alternative model-free approach can be used in practice for optimisation metal ion analysis by means of CZE.

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

LUBAL, Přemysl; Josef HAVEL a Marta FARKOVÁ. ANN Prediction of Migration Behaviour of Metal Complexes in Capillary Zone Electrophoresis. In Book of Abstracts of International Chemometric Conference Chemometrics VI. Brno: Masaryk University Press, 2002, s. P14, 1 s. ISBN 80-210-2918-8.

@inproceedings{483389,
   author = {Lubal, Přemysl and Havel, Josef and Farková, Marta},
   address = {Brno},
   booktitle = {Book of Abstracts of International Chemometric Conference Chemometrics VI},
   keywords = {artificial neural networks; metal complexes; capillary zone electrophoresis},
   language = {eng},
   location = {Brno},
   isbn = {80-210-2918-8},
   pages = {P14},
   publisher = {Masaryk University Press},
   title = {ANN Prediction of Migration Behaviour of Metal Complexes in Capillary Zone Electrophoresis},
   year = {2002}
}

TY  - CONF
ID  - 483389
AU  - Lubal, Přemysl - Havel, Josef - Farková, Marta
PY  - 2002
TI  - ANN Prediction of Migration Behaviour of Metal Complexes in Capillary Zone Electrophoresis
PB  - Masaryk University Press
CY  - Brno
SN  - 8021029188
KW  - artificial neural networks
KW  - metal complexes
KW  - capillary zone electrophoresis
N2  - The knowledge of migration behaviour is important in order to find the optimal experimental conditions for analyte determination by electromigration techniques, e.g. capillary zone electrophoresis (CZE). The metal ion analysis by means of CZE was possible as a consequence of application metal complexes with various organic and inorganic ligands. The metal complexes are responsible for metal ion speciation in solution and their mobility in environment. Recently, the systematic analysis of relationships between migration parameters and charge and size characteristics of metal complexes was done [1]. This quantitative structure-mobility relationships (QSMR's) are based on a function of electrophoretic mobility using enlarged Stokes-Einstein diffusion model with structural descriptors (metal atom electronegativity or effective charge) as well as the formal charge and ligand number [1]. The migration parameters are also dependent on experimental conditions (ionic strength, temperature, etc.). In this contribution, the method of migration behaviour prediction for different metal organic and inorganic complexes using "soft" modelling with artificial neural networks (ANN's) was examined on data taken from literature [1]. The achieved results were compared with values obtained by "hard" modelling (QSMR's). The proposed methodology allows to predict migration parameters and is not dependent on applied relationship. This alternative model-free approach can be used in practice for optimisation metal ion analysis by means of CZE.
ER  -

LUBAL, Přemysl; Josef HAVEL a Marta FARKOVÁ. ANN Prediction of Migration Behaviour of Metal Complexes in Capillary Zone Electrophoresis. In \textit{Book of Abstracts of International Chemometric Conference Chemometrics VI}. Brno: Masaryk University Press, 2002, s.~P14, 1 s. ISBN~80-210-2918-8.

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