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 and Marta FARKOVÁ

Basic information

Original name

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

Authors

HAVEL, Josef (203 Czech Republic) and Marta FARKOVÁ (203 Czech Republic, guarantor)

Edition

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

Publisher

ALGA PRESS

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10406 Analytical chemistry

Country of publisher

Czech Republic

Confidentiality degree

není předmětem státního či obchodního tajemství

RIV identification code

RIV/00216224:14310/02:00007205

Organization unit

Faculty of Science

ISBN

80-86238-24-5

Keywords in English

artificial neural networks; capillary electrophoresis; chiral separation

Abstract

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.

Links

GA203/02/1103, research and development project

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

Investor: Czech Science Foundation, Artificial neural networks and experimental design in analytical chemistry, especially in separation methods

Citovat

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

Detailed Information on Publication Record