CAPILLARY ELECTROPHORESIS CHIRAL SEPARATION MODELLING WITH THE
USE OF ARTIFICIAL NEURAL NETWORKS

D 2002

CAPILLARY ELECTROPHORESIS CHIRAL SEPARATION MODELLING WITH THE USE OF ARTIFICIAL NEURAL NETWORKS

HAVEL, Josef a Marta FARKOVÁ

Základní údaje

Originální název

CAPILLARY ELECTROPHORESIS CHIRAL SEPARATION MODELLING WITH THE USE OF ARTIFICIAL NEURAL NETWORKS

Autoři

HAVEL, Josef (203 Česká republika) a Marta FARKOVÁ (203 Česká republika, garant)

Vydání

1. vyd. Olomouc, CHIRANAL 2002, s. 65-65, 2002

Nakladatel

ALGA 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

Kód RIV

RIV/00216224:14310/02:00007205

Organizační jednotka

Přírodovědecká fakulta

ISBN

80-86238-24-5

Klíčová slova anglicky

artificial neural networks; capillary electrophoresis; chiral separation

Štítky

artificial neural networks, Capillary electrophoresis, chiral separation

Změněno: 13. 5. 2003 09:26, RNDr. Marta Farková, CSc.

Anotace

V originále

Recent development and future trends of enantioseparations in capillary electrophoresis have been reviewed by Chankvetadze et al. On the base of exact physicochemical description using e.g. CELET program the stability constants of either chiral or non-chiral inclusion complexes can be calculated. As for review we refer to Vespalec et al. Recently, we have shown that "soft" modelling of achiral CE separation processes is possible using a combination of artificial neural networks (ANN) and experimental design. Possibility of enantiomers quantification from unresolved peaks was also demonstrated. In this work we are examining possibility of chiral separation "soft" modelling with ANN. It was found that, using suitable ANN architecture, the description of chiral separation is possible with sufficient accuracy. The advantage is that it is not necessary to know or determine chiral selector - enantiomers stability constants and/or the separation mechanism. Using combination of suitable experimental design and ANN architecture, the prediction of optimal conditions for the separation of enantiomers is possible.

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

HAVEL, Josef a Marta FARKOVÁ. CAPILLARY ELECTROPHORESIS CHIRAL SEPARATION MODELLING WITH THE USE OF ARTIFICIAL NEURAL NETWORKS. In CHIRANAL 2002. 1. vyd. Olomouc: ALGA PRESS, 2002, s. 65. ISBN 80-86238-24-5.

@inproceedings{483227,
   author = {Havel, Josef and Farková, Marta},
   address = {Olomouc},
   booktitle = {CHIRANAL 2002},
   edition = {1.},
   keywords = {artificial neural networks; capillary electrophoresis; chiral separation},
   language = {eng},
   location = {Olomouc},
   isbn = {80-86238-24-5},
   pages = {65-65},
   publisher = {ALGA PRESS},
   title = {CAPILLARY ELECTROPHORESIS CHIRAL SEPARATION MODELLING WITH THE USE OF ARTIFICIAL NEURAL NETWORKS},
   year = {2002}
}

TY  - JOUR
ID  - 483227
AU  - Havel, Josef - Farková, Marta
PY  - 2002
TI  - CAPILLARY ELECTROPHORESIS CHIRAL SEPARATION MODELLING WITH THE USE OF ARTIFICIAL NEURAL NETWORKS
PB  - ALGA PRESS
CY  - Olomouc
SN  - 8086238245
KW  - artificial neural networks
KW  - capillary electrophoresis
KW  - chiral separation
N2  - Recent development and future trends of enantioseparations in capillary electrophoresis have been reviewed by Chankvetadze et al. On the base of exact physicochemical description using e.g. CELET program the stability constants of either chiral or non-chiral inclusion complexes can be calculated. As for review we refer to Vespalec et al. Recently, we have shown that "soft" modelling of achiral CE separation processes is possible using a combination of artificial neural networks (ANN) and experimental design. Possibility of enantiomers quantification from unresolved peaks was also demonstrated. In this work we are examining possibility of chiral separation "soft" modelling with ANN. It was found that, using suitable ANN architecture, the description of chiral separation is possible with sufficient accuracy. The advantage is that it is not necessary to know or determine chiral selector - enantiomers stability constants and/or the separation mechanism. Using combination of suitable experimental design and ANN architecture, the prediction of optimal conditions for the separation of enantiomers is possible.
ER  -

HAVEL, Josef a Marta FARKOVÁ. CAPILLARY ELECTROPHORESIS CHIRAL SEPARATION MODELLING WITH THE USE OF ARTIFICIAL NEURAL NETWORKS. In \textit{CHIRANAL 2002}. 1. vyd. Olomouc: ALGA PRESS, 2002, s.~65. ISBN~80-86238-24-5.

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