Prediction of chiral separations using combination of experimental designs and artificial neural networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F99%3A00002118" target="_blank" >RIV/00216224:14310/99:00002118 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Prediction of chiral separations using combination of experimental designs and artificial neural networks

Original language description

In this work the advantages of using artificial neural networks (ANNs) combined with experimental design (ED) to optimize the separation of amino acids enantiomers, with a-cyclodextrin as chiral selector, were demonstrated. The results obtained with theED-ANN approach were compared with those of either partial least squares (PLS) method or response surface methodology where experimental design and the regression equation were used. The ANN approach is quite general, no explicit model is needed and theamount of experimental work can be decreased considerably.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

CB - Analytical chemistry, separation

OECD FORD branch

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Project

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Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

1999

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Chirality

ISSN

0899-0042

e-ISSN

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Volume of the periodical

11

Issue of the periodical within the volume

8

Country of publishing house

US - UNITED STATES

Number of pages

6

Pages from-to

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UT code for WoS article

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EID of the result in the Scopus database

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