TY  - JOUR
AU  - Sremac, Snezana
AU  - Popović, Aleksandar R.
AU  - Todorović, Žaklina
AU  - Cokesa, Duro
AU  - Onjia, Antonije E.
PY  - 2008
UR  - https://cherry.chem.bg.ac.rs/handle/123456789/947
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. (C) 2008 Elsevier B.V. All rights reserved.
PB  - Elsevier Science Bv, Amsterdam
T2  - Talanta
T1  - Interpretative optimization and artificial neural network modeling of the gas chromatographic separation of polycyclic aromatic hydrocarbons
VL  - 76
IS  - 1
SP  - 66
EP  - 71
DO  - 10.1016/j.talanta.2008.02.004
ER  - 

@article{
author = "Sremac, Snezana and Popović, Aleksandar R. and Todorović, Žaklina and Cokesa, Duro and Onjia, Antonije E.",
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. (C) 2008 Elsevier B.V. All rights reserved.",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Talanta",
title = "Interpretative optimization and artificial neural network modeling of the gas chromatographic separation of polycyclic aromatic hydrocarbons",
volume = "76",
number = "1",
pages = "66-71",
doi = "10.1016/j.talanta.2008.02.004"
}

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

Sremac S, Popović AR, Todorović Ž, Cokesa D, Onjia AE. 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, Snezana, Popović, Aleksandar R., Todorović, Žaklina, Cokesa, Duro, Onjia, Antonije E., "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 . .