JPC - Journal of Planar Chromatography - Modern TLC
Volume/Issue:
Volume 24: Issue 1
Use of genetic algorithms and artificial neural networks to predict the resolution of amino acids in thin-layer chromatography
Authors:
Tomislav RolichUniversity of Zagreb Department of Fundamental Natural and Engineering Sciences, Faculty of Textile Technology Prilaz baruna Filipovića 28a 10000 Zagreb Croatia

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Iva RezićUniversity of Zagreb Laboratory of Analytical Chemistry, Department of Applied Chemistry, Faculty of Textile Technology Prilaz baruna Filipovića 28a 10000 Zagreb Croatia

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Pages:
16–22
Online Publication Date:
15 Feb 2011
Publication Date:
01 Feb 2011
Article Category:
Research Article
DOI:
https://doi.org/10.1556/jpc.24.2011.1.3
Keywords:
Thin-layer chromatography; Amino acids; Optimization; Artificial neural networks; Genetic algorithms
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A novel method is proposed for optimization of simultaneous thinlayer chromatographic separation of seven amino acids. For this purpose we used a useful combination of genetic algorithms (GA) with artificial neural networks (ANN). Methods investigated in this work were successfully used for prediction of resolution (RS) and optimization of the thin-layer chromatographic separation of model solutions containing the seven compound. Very good correlation was achieved between predicted and calculated RS data, and low absolute and relative errors were obtained.

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Cover JPC - Journal of Planar Chromatography - Modern TLC
JPC - Journal of Planar Chromatography - Modern TLC
Print ISSN:
0933-4173
Online ISSN:
1789-0993

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Manuscript Submission: HERE

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Journal of Planar Chromatography - Modern TLC
Language English
Size A4
Year of
Foundation		 1988
Volumes
per Year		 1
Issues
per Year		 6
Founder Akadémiai Kiadó
Founder's
Address		 H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Springer Nature Switzerland AG
Publisher's
Address		 H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
CH-6330 Cham, Switzerland Gewerbestrasse 11.
Responsible
Publisher		 Chief Executive Officer, Akadémiai Kiadó
ISSN 0933-4173 (Print)
ISSN 1789-0993 (Online)

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