Evaluation of calibration data in capillary electrophoresis
using artificial neural networks to increase precision of
analysis

J 2002

Evaluation of calibration data in capillary electrophoresis using artificial neural networks to increase precision of analysis

POLÁŠKOVÁ, Pavla, Gaston Guillermo BOCAZ BENEVENTI, Hua LI and Josef HAVEL

Basic information

Original name

Evaluation of calibration data in capillary electrophoresis using artificial neural networks to increase precision of analysis

Authors

POLÁŠKOVÁ, Pavla (203 Czech Republic), Gaston Guillermo BOCAZ BENEVENTI (152 Chile), Hua LI (156 China) and Josef HAVEL (203 Czech Republic, guarantor)

Edition

JOURNAL OF CHROMATOGRAPHY A, AMSTERDAM, ELSEVIER SCIENCE BV, 2002, 0021-9673

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: 3.098

RIV identification code

RIV/00216224:14310/02:00007964

Organization unit

Faculty of Science

UT WoS

000179406700007

Keywords in English

calibration data; capillary electrophoresis; artificial neural networks

Abstract

V originále

Increase of precision in capillary electrophoresis can be achieved applying suitable markers and evaluating calibration curves and data analysis with artificial neural networks. They are able to account for errors in both x- and y-axes, nonlinear response of detector and non-linearity of calibration curves eventually. A comparison of the artificial neural networks approach with ordinary least-squares (OLS) and bivariate least-squares regression (BLS) was done. While OLS and BLS give similar results, the method proposed and tested in analysis of several pharmaceutical products yields lower prediction errors than traditional linear least-squares methods and the precision of analysis was found in the range 0.5-1.5% relative. (C) 2002 Elsevier Science B.V. All rights reserved.

Links

GA203/02/1103, research and development project

Name: Umělé neuronové sítě a plánování pokusů v analytické chemii, zejména v separačních metodách

Investor: Czech Science Foundation, Artificial neural networks and experimental design in analytical chemistry, especially in separation methods

Citovat

POLÁŠKOVÁ, Pavla, Gaston Guillermo BOCAZ BENEVENTI, Hua LI and Josef HAVEL. Evaluation of calibration data in capillary electrophoresis using artificial neural networks to increase precision of analysis. JOURNAL OF CHROMATOGRAPHY A. AMSTERDAM: ELSEVIER SCIENCE BV, 2002, vol. 979, 1-2, p. 59-67, 8Increase. ISSN 0021-9673.

@article{485220,
   author = {Polášková, Pavla and Bocaz Beneventi, Gaston Guillermo and Li, Hua and Havel, Josef},
   article_location = {AMSTERDAM},
   article_number = {1-2},
   keywords = {calibration data; capillary electrophoresis; artificial neural networks},
   language = {eng},
   issn = {0021-9673},
   journal = {JOURNAL OF CHROMATOGRAPHY A},
   title = {Evaluation of calibration data in capillary electrophoresis using artificial neural networks to increase precision of analysis},
   volume = {979},
   year = {2002}
}

TY  - JOUR
ID  - 485220
AU  - Polášková, Pavla - Bocaz Beneventi, Gaston Guillermo - Li, Hua - Havel, Josef
PY  - 2002
TI  - Evaluation of calibration data in capillary electrophoresis using artificial neural networks to increase precision of analysis
JF  - JOURNAL OF CHROMATOGRAPHY A
VL  - 979
IS  - 1-2
SP  - 59-67
EP  - 59-67
PB  - ELSEVIER SCIENCE BV
SN  - 00219673
KW  - calibration data
KW  - capillary electrophoresis
KW  - artificial neural networks
N2  - Increase of precision in capillary electrophoresis can be achieved applying suitable markers and evaluating calibration curves and data analysis with artificial neural networks. They are able to account for errors in both x- and y-axes, nonlinear response of detector and non-linearity of calibration curves eventually. A comparison of the artificial neural networks approach with ordinary least-squares (OLS) and bivariate least-squares regression (BLS) was done. While OLS and BLS give similar results, the method proposed and tested in analysis of several pharmaceutical products yields lower prediction errors than traditional linear least-squares methods and the precision of analysis was found in the range 0.5-1.5% relative. (C) 2002 Elsevier Science B.V. All rights reserved.
ER  -

POLÁŠKOVÁ, Pavla, Gaston Guillermo BOCAZ BENEVENTI, Hua LI and Josef HAVEL. Evaluation of calibration data in capillary electrophoresis using artificial neural networks to increase precision of analysis. \textit{JOURNAL OF CHROMATOGRAPHY A}. AMSTERDAM: ELSEVIER SCIENCE BV, 2002, vol.~979, 1-2, p.~59-67, 8Increase. ISSN~0021-9673.

Detailed Information on Publication Record