Artificial neural networks for quantification in unresolved
capillary electrophoresis peaks

J 2002

Artificial neural networks for quantification in unresolved capillary electrophoresis peaks

BOCAZ-BENEVENTI, Gaston, Rosa LATORRE, Marta FARKOVÁ and Josef HAVEL

Basic information

Original name

Artificial neural networks for quantification in unresolved capillary electrophoresis peaks

Authors

BOCAZ-BENEVENTI, Gaston (152 Chile), Rosa LATORRE (724 Spain), Marta FARKOVÁ (203 Czech Republic) and Josef HAVEL (203 Czech Republic, guarantor)

Edition

Analytica Chimica Acta, Amsterdam, Elsevier Science Publishers, 2002, 0003-2670

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10406 Analytical chemistry

Country of publisher

Netherlands

Confidentiality degree

není předmětem státního či obchodního tajemství

Impact factor

Impact factor: 2.114

RIV identification code

RIV/00216224:14310/02:00005586

Organization unit

Faculty of Science

UT WoS

000173613100006

Keywords in English

capillary zone electrophoresis; unresolved peaks; experimental design; normalization; artificial neural networks; quantitation

Abstract

V originále

The application of the combination of experimental design (ED) and artificial neural networks (ANNs) for the quantification of overlapped peaks in capillary zone electrophoresis is described. When the total separation cannot be achieved by separation techniques, the use of ED-ANN can be a suitable approach. The unstability of EOF causes peak shift that has to be corrected in order to apply ED-ANN methods. In this work, normalization procedure of electropherograms with consequent application of ANNs for quantification purpose was developed. Both, spectra and electropherograms can be used as multivariate data. In general, both kinds of data were found to be suitable for unresolved peaks quantification by ED-ANN approach.

Links

MSM 143100011, plan (intention)

Name: Struktura a vazebné poměry, vlastnosti a analýza syntetických a přírodních molekulových ansamblů

Investor: Ministry of Education, Youth and Sports of the CR, Structure and character of bonding, properties and analysis of synthetic and natural molecular ensembles

Citovat

BOCAZ-BENEVENTI, Gaston, Rosa LATORRE, Marta FARKOVÁ and Josef HAVEL. Artificial neural networks for quantification in unresolved capillary electrophoresis peaks. Analytica Chimica Acta. Amsterdam: Elsevier Science Publishers, 2002, vol. 452, No 1, p. 47-63. ISSN 0003-2670.

@article{400391,
   author = {BocazandBeneventi, Gaston and Latorre, Rosa and Farková, Marta and Havel, Josef},
   article_location = {Amsterdam},
   article_number = {1},
   keywords = {capillary zone electrophoresis; unresolved peaks; experimental design; normalization; artificial neural networks; quantitation},
   language = {eng},
   issn = {0003-2670},
   journal = {Analytica Chimica Acta},
   title = {Artificial neural networks for quantification in unresolved capillary electrophoresis peaks},
   volume = {452},
   year = {2002}
}

TY  - JOUR
ID  - 400391
AU  - Bocaz-Beneventi, Gaston - Latorre, Rosa - Farková, Marta - Havel, Josef
PY  - 2002
TI  - Artificial neural networks for quantification in unresolved capillary electrophoresis peaks
JF  - Analytica Chimica Acta
VL  - 452
IS  - 1
SP  - 47
EP  - 47
PB  - Elsevier Science Publishers
SN  - 00032670
KW  - capillary zone electrophoresis
KW  - unresolved peaks
KW  - experimental design
KW  - normalization
KW  - artificial neural networks
KW  - quantitation
N2  - The application of the combination of experimental design (ED) and artificial neural networks (ANNs) for the quantification of overlapped peaks in capillary zone electrophoresis is described. When the total separation cannot be achieved by separation techniques, the use of ED-ANN can be a suitable approach. The unstability of EOF causes peak shift that has to be corrected in order to apply ED-ANN methods. In this work, normalization procedure of electropherograms with consequent application of ANNs for quantification purpose was developed. Both, spectra and electropherograms can be used as multivariate data. In general, both kinds of data were found to be suitable for unresolved peaks quantification by ED-ANN approach.
ER  -

BOCAZ-BENEVENTI, Gaston, Rosa LATORRE, Marta FARKOVÁ and Josef HAVEL. Artificial neural networks for quantification in unresolved capillary electrophoresis peaks. \textit{Analytica Chimica Acta}. Amsterdam: Elsevier Science Publishers, 2002, vol.~452, No~1, p.~47-63. ISSN~0003-2670.

Detailed Information on Publication Record