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Optimization of artificial neural network for retention modeling in high-performance liquid chromatography

Authorized Users Only
2004
Authors
Vasiljević, Tatjana
Onjia, Antonije
Čokeša, Đuro
Laušević, Mila
Article (Published version)
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Abstract
An artificial neural network (ANN) model for the prediction of retention times in high-performance liquid chromatography (HPLC) was developed and optimized. A three-layer feed-forward ANN has been used to model retention behavior of nine phenols as a function of mobile phase composition (methanol-acetic acid mobile phase). The number of hidden layer nodes, number of iteration steps and the number of experimental data points used for training set were optimized. By using a relatively small amount of experimental data (25 experimental data points in the training set), a very accurate prediction of the retention (percentage normalized differences between the predicted and the experimental data less than 0.6%) was obtained. It was shown that the prediction ability of ANN model linearly decreased with the reduction of number of experiments for the training data set. The results obtained demonstrate that ANN offers a straightforward way for retention modeling in isocratic HPLC separation of ...a complex mixture of compounds widely different in pK(a) and log K-ow values.

Keywords:
HPLC / phenols / experimental design / ANN / back-propagation
Source:
Talanta, 2004, 64, 3, 785-790
Publisher:
  • Elsevier, Amsterdam

DOI: 10.1016/j.talanta.2004.03.032

ISSN: 0039-9140

PubMed: 18969673

WoS: 000224285300031

Scopus: 2-s2.0-4544266488
[ Google Scholar ]
31
25
URI
http://TechnoRep.tmf.bg.ac.rs/handle/123456789/702
Collections
  • Radovi istraživača / Researchers’ publications (TMF)
Institution/Community
Tehnološko-metalurški fakultet
TY  - JOUR
AU  - Vasiljević, Tatjana
AU  - Onjia, Antonije
AU  - Čokeša, Đuro
AU  - Laušević, Mila
PY  - 2004
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/702
AB  - An artificial neural network (ANN) model for the prediction of retention times in high-performance liquid chromatography (HPLC) was developed and optimized. A three-layer feed-forward ANN has been used to model retention behavior of nine phenols as a function of mobile phase composition (methanol-acetic acid mobile phase). The number of hidden layer nodes, number of iteration steps and the number of experimental data points used for training set were optimized. By using a relatively small amount of experimental data (25 experimental data points in the training set), a very accurate prediction of the retention (percentage normalized differences between the predicted and the experimental data less than 0.6%) was obtained. It was shown that the prediction ability of ANN model linearly decreased with the reduction of number of experiments for the training data set. The results obtained demonstrate that ANN offers a straightforward way for retention modeling in isocratic HPLC separation of a complex mixture of compounds widely different in pK(a) and log K-ow values.
PB  - Elsevier, Amsterdam
T2  - Talanta
T1  - Optimization of artificial neural network for retention modeling in high-performance liquid chromatography
EP  - 790
IS  - 3
SP  - 785
VL  - 64
DO  - 10.1016/j.talanta.2004.03.032
ER  - 
@article{
author = "Vasiljević, Tatjana and Onjia, Antonije and Čokeša, Đuro and Laušević, Mila",
year = "2004",
abstract = "An artificial neural network (ANN) model for the prediction of retention times in high-performance liquid chromatography (HPLC) was developed and optimized. A three-layer feed-forward ANN has been used to model retention behavior of nine phenols as a function of mobile phase composition (methanol-acetic acid mobile phase). The number of hidden layer nodes, number of iteration steps and the number of experimental data points used for training set were optimized. By using a relatively small amount of experimental data (25 experimental data points in the training set), a very accurate prediction of the retention (percentage normalized differences between the predicted and the experimental data less than 0.6%) was obtained. It was shown that the prediction ability of ANN model linearly decreased with the reduction of number of experiments for the training data set. The results obtained demonstrate that ANN offers a straightforward way for retention modeling in isocratic HPLC separation of a complex mixture of compounds widely different in pK(a) and log K-ow values.",
publisher = "Elsevier, Amsterdam",
journal = "Talanta",
title = "Optimization of artificial neural network for retention modeling in high-performance liquid chromatography",
pages = "790-785",
number = "3",
volume = "64",
doi = "10.1016/j.talanta.2004.03.032"
}
Vasiljević, T., Onjia, A., Čokeša, Đ.,& Laušević, M.. (2004). Optimization of artificial neural network for retention modeling in high-performance liquid chromatography. in Talanta
Elsevier, Amsterdam., 64(3), 785-790.
https://doi.org/10.1016/j.talanta.2004.03.032
Vasiljević T, Onjia A, Čokeša Đ, Laušević M. Optimization of artificial neural network for retention modeling in high-performance liquid chromatography. in Talanta. 2004;64(3):785-790.
doi:10.1016/j.talanta.2004.03.032 .
Vasiljević, Tatjana, Onjia, Antonije, Čokeša, Đuro, Laušević, Mila, "Optimization of artificial neural network for retention modeling in high-performance liquid chromatography" in Talanta, 64, no. 3 (2004):785-790,
https://doi.org/10.1016/j.talanta.2004.03.032 . .

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