TY  - JOUR
AU  - Sremac, Snežana
AU  - Popović, Aleksandar R.
AU  - Todorović, Žaklina
AU  - Čokeša, Đuro
AU  - Onjia, Antonije
PY  - 2008
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/1347
AB  - An interpretative strategy (factorial design experimentation + total resolution analysis + chromatogram simulation) was employed to optimize the separation of 16 polycyclic aromatic hydrocarbons (PAHs) (naphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, chrysene, benzo(a)anthracene, benzo(k)fluoranthene, benzo(b)fluoranthene, benzo(a)pyrene, indeno(1,2,3-c,d)pyrene, dibenzo(a,h)anthracene, benzo(g,h,i)perylene) in temperature-programmed gas chromatography (GC). Also, the retention behavior of PAHs in the same GC system was studied by a feed-forward artificial neural network (ANN). GC separation was investigated as a function of one (linear temperature ramp) or two (linear temperature ramp+the final hold temperature) variables. The applied interpretative approach resulted in rather good agreement between the measured and the predicted retention times for PAHs in both one and two variable modeling. The ANN model, strongly affected by the number of input experiments, was shown to be less effective for one variable used, but quite successful when two input variables were used. All PAHs, including difficult to separate peak pairs (benzo(k)fluoranthene/benzo(b)fluoranthene and indeno(1,2,3-c,d)pyrene/dibenzo(a,h)anthracene), were separated in a standard (5% phenyl-95% climethylpolysiloxane) capillary column at an optimum temperature ramp of 8.0 degrees C/min and final hold temperature in the range of 260-320 degrees C.
PB  - Elsevier, Amsterdam
T2  - Talanta
T1  - Interpretative optimization and artificial neural network modeling of the gas chromatographic separation of polycyclic aromatic hydrocarbons
EP  - 71
IS  - 1
SP  - 66
VL  - 76
DO  - 10.1016/j.talanta.2008.02.004
ER  - 

@article{
author = "Sremac, Snežana and Popović, Aleksandar R. and Todorović, Žaklina and Čokeša, Đuro and Onjia, Antonije",
year = "2008",
abstract = "An interpretative strategy (factorial design experimentation + total resolution analysis + chromatogram simulation) was employed to optimize the separation of 16 polycyclic aromatic hydrocarbons (PAHs) (naphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, chrysene, benzo(a)anthracene, benzo(k)fluoranthene, benzo(b)fluoranthene, benzo(a)pyrene, indeno(1,2,3-c,d)pyrene, dibenzo(a,h)anthracene, benzo(g,h,i)perylene) in temperature-programmed gas chromatography (GC). Also, the retention behavior of PAHs in the same GC system was studied by a feed-forward artificial neural network (ANN). GC separation was investigated as a function of one (linear temperature ramp) or two (linear temperature ramp+the final hold temperature) variables. The applied interpretative approach resulted in rather good agreement between the measured and the predicted retention times for PAHs in both one and two variable modeling. The ANN model, strongly affected by the number of input experiments, was shown to be less effective for one variable used, but quite successful when two input variables were used. All PAHs, including difficult to separate peak pairs (benzo(k)fluoranthene/benzo(b)fluoranthene and indeno(1,2,3-c,d)pyrene/dibenzo(a,h)anthracene), were separated in a standard (5% phenyl-95% climethylpolysiloxane) capillary column at an optimum temperature ramp of 8.0 degrees C/min and final hold temperature in the range of 260-320 degrees C.",
publisher = "Elsevier, Amsterdam",
journal = "Talanta",
title = "Interpretative optimization and artificial neural network modeling of the gas chromatographic separation of polycyclic aromatic hydrocarbons",
pages = "71-66",
number = "1",
volume = "76",
doi = "10.1016/j.talanta.2008.02.004"
}

Sremac, S., Popović, A. R., Todorović, Ž., Čokeša, Đ.,& Onjia, A.. (2008). Interpretative optimization and artificial neural network modeling of the gas chromatographic separation of polycyclic aromatic hydrocarbons. in Talanta
Elsevier, Amsterdam., 76(1), 66-71.
https://doi.org/10.1016/j.talanta.2008.02.004

Sremac S, Popović AR, Todorović Ž, Čokeša Đ, Onjia A. Interpretative optimization and artificial neural network modeling of the gas chromatographic separation of polycyclic aromatic hydrocarbons. in Talanta. 2008;76(1):66-71.
doi:10.1016/j.talanta.2008.02.004 .

Sremac, Snežana, Popović, Aleksandar R., Todorović, Žaklina, Čokeša, Đuro, Onjia, Antonije, "Interpretative optimization and artificial neural network modeling of the gas chromatographic separation of polycyclic aromatic hydrocarbons" in Talanta, 76, no. 1 (2008):66-71,
https://doi.org/10.1016/j.talanta.2008.02.004 . .

TY  - JOUR
AU  - Sremac, Snežana
AU  - Skrbić, Biljana
AU  - Onjia, Antonije
PY  - 2005
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/841
AB  - A feed-forward artificial neural network (ANN) model was used to link molecular structures (boiling points, connectivity indices and molecular weights) and retention indices of polycyclic aromatic hydrocarbons (PAHs) in linear temperature-programmed gas chromatography. A randomly taken subset of PAH retention data reported by Lee et al, [Anal. Chem. 51 (1979) 768], containing retention index data for 30 PAHs, was used to make the ANN model. The prediction ability of the trained ANN was tested on unseen data for 18 PAHs from the same article, as well as on the retention data for 7 PAHs experimentally obtained in this work. In addition, two different data sets with known retention indices taken from the literature were analyzed by the same ANN model. It has been shown that the relative accuracy as the degree of agreement between the measured and the predicted retention indices in all testing sets, for most of the studied PAHs, were within the experimental error margins (3 %).
PB  - Srpsko hemijsko društvo, Beograd
T2  - Journal of the Serbian Chemical Society
T1  - Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography
EP  - 1300
IS  - 11
SP  - 1291
VL  - 70
DO  - 10.2298/JSC0511291S
ER  - 

@article{
author = "Sremac, Snežana and Skrbić, Biljana and Onjia, Antonije",
year = "2005",
abstract = "A feed-forward artificial neural network (ANN) model was used to link molecular structures (boiling points, connectivity indices and molecular weights) and retention indices of polycyclic aromatic hydrocarbons (PAHs) in linear temperature-programmed gas chromatography. A randomly taken subset of PAH retention data reported by Lee et al, [Anal. Chem. 51 (1979) 768], containing retention index data for 30 PAHs, was used to make the ANN model. The prediction ability of the trained ANN was tested on unseen data for 18 PAHs from the same article, as well as on the retention data for 7 PAHs experimentally obtained in this work. In addition, two different data sets with known retention indices taken from the literature were analyzed by the same ANN model. It has been shown that the relative accuracy as the degree of agreement between the measured and the predicted retention indices in all testing sets, for most of the studied PAHs, were within the experimental error margins (3 %).",
publisher = "Srpsko hemijsko društvo, Beograd",
journal = "Journal of the Serbian Chemical Society",
title = "Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography",
pages = "1300-1291",
number = "11",
volume = "70",
doi = "10.2298/JSC0511291S"
}

Sremac, S., Skrbić, B.,& Onjia, A.. (2005). Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography. in Journal of the Serbian Chemical Society
Srpsko hemijsko društvo, Beograd., 70(11), 1291-1300.
https://doi.org/10.2298/JSC0511291S

Sremac S, Skrbić B, Onjia A. Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography. in Journal of the Serbian Chemical Society. 2005;70(11):1291-1300.
doi:10.2298/JSC0511291S .

Sremac, Snežana, Skrbić, Biljana, Onjia, Antonije, "Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography" in Journal of the Serbian Chemical Society, 70, no. 11 (2005):1291-1300,
https://doi.org/10.2298/JSC0511291S . .

TY  - CONF
AU  - Sremac, Snežana
AU  - Todorović, Žaklina
AU  - Popović, Aleksandra
AU  - Onjia, Antonije
PY  - 2004
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/7236
AB  - This paper describes the application of artificial neural networks (ANNs) method
to the modeling of 13 polycyclic aromatic hydrocarbons (PAHs)retentionsin temperature - programmed gas chromatography.The ANN method used resulted in relatively
good agreement (RMStesting = 0.018) between the measured and the predicted retention
times for 13 PAHs. Somewhat higher discrepancy in prediction was observed for the
late – eluted PAHs at lower temperature ramps.
PB  - Belgrade : The Society of Physical Chemists of Serbia
C3  - Physical Chemistry 2004 : proceedings of the 7th International Conference on Fundamental and Applied Aspects of Physical Chemistry, September 21-23, 2004, Belgrade
T1  - Neural network prediction of the gas chromatographic separation of polycyclic aromatic hydrocarbons
EP  - 867
SP  - 865
VL  - 2
UR  - https://hdl.handle.net/21.15107/rcub_technorep_7236
ER  - 

@conference{
author = "Sremac, Snežana and Todorović, Žaklina and Popović, Aleksandra and Onjia, Antonije",
year = "2004",
abstract = "This paper describes the application of artificial neural networks (ANNs) method
to the modeling of 13 polycyclic aromatic hydrocarbons (PAHs)retentionsin temperature - programmed gas chromatography.The ANN method used resulted in relatively
good agreement (RMStesting = 0.018) between the measured and the predicted retention
times for 13 PAHs. Somewhat higher discrepancy in prediction was observed for the
late – eluted PAHs at lower temperature ramps.",
publisher = "Belgrade : The Society of Physical Chemists of Serbia",
journal = "Physical Chemistry 2004 : proceedings of the 7th International Conference on Fundamental and Applied Aspects of Physical Chemistry, September 21-23, 2004, Belgrade",
title = "Neural network prediction of the gas chromatographic separation of polycyclic aromatic hydrocarbons",
pages = "867-865",
volume = "2",
url = "https://hdl.handle.net/21.15107/rcub_technorep_7236"
}

Sremac, S., Todorović, Ž., Popović, A.,& Onjia, A.. (2004). Neural network prediction of the gas chromatographic separation of polycyclic aromatic hydrocarbons. in Physical Chemistry 2004 : proceedings of the 7th International Conference on Fundamental and Applied Aspects of Physical Chemistry, September 21-23, 2004, Belgrade
Belgrade : The Society of Physical Chemists of Serbia., 2, 865-867.
https://hdl.handle.net/21.15107/rcub_technorep_7236

Sremac S, Todorović Ž, Popović A, Onjia A. Neural network prediction of the gas chromatographic separation of polycyclic aromatic hydrocarbons. in Physical Chemistry 2004 : proceedings of the 7th International Conference on Fundamental and Applied Aspects of Physical Chemistry, September 21-23, 2004, Belgrade. 2004;2:865-867.
https://hdl.handle.net/21.15107/rcub_technorep_7236 .

Sremac, Snežana, Todorović, Žaklina, Popović, Aleksandra, Onjia, Antonije, "Neural network prediction of the gas chromatographic separation of polycyclic aromatic hydrocarbons" in Physical Chemistry 2004 : proceedings of the 7th International Conference on Fundamental and Applied Aspects of Physical Chemistry, September 21-23, 2004, Belgrade, 2 (2004):865-867,
https://hdl.handle.net/21.15107/rcub_technorep_7236 .

TY  - CONF
AU  - Sremac, Snežana
AU  - Đurišić, Nataša
AU  - Skrbić, Biljana
AU  - Onjia, Antonije
PY  - 2003
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/6533
PB  - Serbian Chemical Society
C3  - II Regional Symposium "Chemistry and the Environment", Kruševac, Serbia
T1  - Analysis of polycyclic aromatic hydrocarbons (PAHs) in soil by soft independent modeling of class analogy (SIMCA)
UR  - https://hdl.handle.net/21.15107/rcub_technorep_6533
ER  - 

@conference{
author = "Sremac, Snežana and Đurišić, Nataša and Skrbić, Biljana and Onjia, Antonije",
year = "2003",
publisher = "Serbian Chemical Society",
journal = "II Regional Symposium "Chemistry and the Environment", Kruševac, Serbia",
title = "Analysis of polycyclic aromatic hydrocarbons (PAHs) in soil by soft independent modeling of class analogy (SIMCA)",
url = "https://hdl.handle.net/21.15107/rcub_technorep_6533"
}

Sremac, S., Đurišić, N., Skrbić, B.,& Onjia, A.. (2003). Analysis of polycyclic aromatic hydrocarbons (PAHs) in soil by soft independent modeling of class analogy (SIMCA). in II Regional Symposium "Chemistry and the Environment", Kruševac, Serbia
Serbian Chemical Society..
https://hdl.handle.net/21.15107/rcub_technorep_6533

Sremac S, Đurišić N, Skrbić B, Onjia A. Analysis of polycyclic aromatic hydrocarbons (PAHs) in soil by soft independent modeling of class analogy (SIMCA). in II Regional Symposium "Chemistry and the Environment", Kruševac, Serbia. 2003;.
https://hdl.handle.net/21.15107/rcub_technorep_6533 .

Sremac, Snežana, Đurišić, Nataša, Skrbić, Biljana, Onjia, Antonije, "Analysis of polycyclic aromatic hydrocarbons (PAHs) in soil by soft independent modeling of class analogy (SIMCA)" in II Regional Symposium "Chemistry and the Environment", Kruševac, Serbia (2003),
https://hdl.handle.net/21.15107/rcub_technorep_6533 .