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

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.

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

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10406 Analytical chemistry
• Country of publisher: Czech Republic
• Confidentiality degree: is not subject to a state or trade secret
• 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
• Tags: artificial neural networks, Capillary electrophoresis, chiral separation

Abstract

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