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Artificial neural networks in analysis of indinavir and its degradation products retention

Само за регистроване кориснике
2009
Аутори
Jančić-Stojanović, Biljana
Ivanović, D.
Malenović, Anđelija
Medenica, Mirjana
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документу
Апстракт
Artificial neural networks (ANN) are biologically inspired computer programs designed to simulate the way in which the human brain processes the information. In the past few years, coupling of experimental design (ED) and ANN became useful tool in the method optimization. This paper presents the application of ED-ANN in analysis of chromatographic behavior of indinavir and its degradation products. According to preliminary study, full factorial design 2(4) was chosen to set input variables for network training. Experimental data (inputs) and results for retention factors from experiments (outputs) were used to train the ANN with aim to define correlation among variables. For networks training multi-layer perceptron (MLP) with back propagation (BP) algorithm was used. Network with the lowest root mean square (RMS) had 4-8-3 topology. Predicted data were in good agreement with experimental data (correlation was higher than 0.9713 for training set). Regression statistics confirmed good ab...ility of trained network to predict compounds retention.

Кључне речи:
Experimental design / Artificial neural networks / Liquid chromatography / Indinavir / Degradation products
Извор:
Talanta, 2009, 78, 1, 107-112
Издавач:
  • Elsevier Science BV, Amsterdam
Пројекти:
  • Формулисање и Карактеризација сепарационих система за моделовање ретенционог понашања лековитих супстанција уз хемометријску евалуацију (RS-142077)

DOI: 10.1016/j.talanta.2008.10.066

ISSN: 0039-9140

PubMed: 19174211

WoS: 000263634700016

Scopus: 2-s2.0-58649105768
[ Google Scholar ]
10
11
URI
http://farfar.pharmacy.bg.ac.rs/handle/123456789/1247
Колекције
  • Radovi istraživača / Researchers’ publications
Институција
Pharmacy
TY  - JOUR
AU  - Jančić-Stojanović, Biljana
AU  - Ivanović, D.
AU  - Malenović, Anđelija
AU  - Medenica, Mirjana
PY  - 2009
UR  - http://farfar.pharmacy.bg.ac.rs/handle/123456789/1247
AB  - Artificial neural networks (ANN) are biologically inspired computer programs designed to simulate the way in which the human brain processes the information. In the past few years, coupling of experimental design (ED) and ANN became useful tool in the method optimization. This paper presents the application of ED-ANN in analysis of chromatographic behavior of indinavir and its degradation products. According to preliminary study, full factorial design 2(4) was chosen to set input variables for network training. Experimental data (inputs) and results for retention factors from experiments (outputs) were used to train the ANN with aim to define correlation among variables. For networks training multi-layer perceptron (MLP) with back propagation (BP) algorithm was used. Network with the lowest root mean square (RMS) had 4-8-3 topology. Predicted data were in good agreement with experimental data (correlation was higher than 0.9713 for training set). Regression statistics confirmed good ability of trained network to predict compounds retention.
PB  - Elsevier Science BV, Amsterdam
T2  - Talanta
T1  - Artificial neural networks in analysis of indinavir and its degradation products retention
VL  - 78
IS  - 1
SP  - 107
EP  - 112
DO  - 10.1016/j.talanta.2008.10.066
ER  - 
@article{
author = "Jančić-Stojanović, Biljana and Ivanović, D. and Malenović, Anđelija and Medenica, Mirjana",
year = "2009",
url = "http://farfar.pharmacy.bg.ac.rs/handle/123456789/1247",
abstract = "Artificial neural networks (ANN) are biologically inspired computer programs designed to simulate the way in which the human brain processes the information. In the past few years, coupling of experimental design (ED) and ANN became useful tool in the method optimization. This paper presents the application of ED-ANN in analysis of chromatographic behavior of indinavir and its degradation products. According to preliminary study, full factorial design 2(4) was chosen to set input variables for network training. Experimental data (inputs) and results for retention factors from experiments (outputs) were used to train the ANN with aim to define correlation among variables. For networks training multi-layer perceptron (MLP) with back propagation (BP) algorithm was used. Network with the lowest root mean square (RMS) had 4-8-3 topology. Predicted data were in good agreement with experimental data (correlation was higher than 0.9713 for training set). Regression statistics confirmed good ability of trained network to predict compounds retention.",
publisher = "Elsevier Science BV, Amsterdam",
journal = "Talanta",
title = "Artificial neural networks in analysis of indinavir and its degradation products retention",
volume = "78",
number = "1",
pages = "107-112",
doi = "10.1016/j.talanta.2008.10.066"
}
Jančić-Stojanović B, Ivanović D, Malenović A, Medenica M. Artificial neural networks in analysis of indinavir and its degradation products retention. Talanta. 2009;78(1):107-112
Jančić-Stojanović, B., Ivanović, D., Malenović, A.,& Medenica, M. (2009). Artificial neural networks in analysis of indinavir and its degradation products retention.
TalantaElsevier Science BV, Amsterdam., 78(1), 107-112.
https://doi.org/10.1016/j.talanta.2008.10.066
Jančić-Stojanović Biljana, Ivanović D., Malenović Anđelija, Medenica Mirjana, "Artificial neural networks in analysis of indinavir and its degradation products retention" 78, no. 1 (2009):107-112,
https://doi.org/10.1016/j.talanta.2008.10.066 .

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