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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

  • DOI - Digital Object Identifier

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 &quot;soft&quot; 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 &quot;soft&quot; 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 &quot;soft&quot; 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 &quot;soft&quot; 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

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

  • e-ISSN

  • 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