CAPILLARY ELECTROPHORESIS CHIRAL SEPARATION MODELLING WITH THE USE OF ARTIFICIAL NEURAL NETWORKS
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F02%3A00007205" target="_blank" >RIV/00216224:14310/02:00007205 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
CAPILLARY ELECTROPHORESIS CHIRAL SEPARATION MODELLING WITH THE USE OF ARTIFICIAL NEURAL NETWORKS
Original language description
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 combinatio
Czech name
CAPILLARY ELECTROPHORESIS CHIRAL SEPARATION MODELLING WITH THE USE OF ARTIFICIAL NEURAL NETWORKS
Czech description
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 combinatio
Classification
Type
D - Article in proceedings
CEP classification
CB - Analytical chemistry, separation
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA203%2F02%2F1103" target="_blank" >GA203/02/1103: Artificial neural networks and experimental design in analytical chemistry, especially in separation methods</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2002
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
CHIRANAL 2002
ISBN
80-86238-24-5
ISSN
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e-ISSN
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Number of pages
1
Pages from-to
65
Publisher name
ALGA PRESS
Place of publication
Olomouc
Event location
24. - 27. 6. 2002, Olomouc
Event date
Jan 1, 2002
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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