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.
TY - JOUR
AU - Jančić-Stojanović, Biljana
AU - Ivanović, D.
AU - Malenović, Anđelija
AU - Medenica, Mirjana
PY - 2009
UR - https://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",
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.. (2009). Artificial neural networks in analysis of indinavir and its degradation products retention. in Talanta
Elsevier Science BV, Amsterdam., 78(1), 107-112.
https://doi.org/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. in Talanta. 2009;78(1):107-112.
doi: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" in Talanta, 78, no. 1 (2009):107-112,
https://doi.org/10.1016/j.talanta.2008.10.066 . .
