TY  - JOUR
AU  - Sremac, Snežana
AU  - Popović, Aleksandar R.
AU  - Todorović, Žaklina
AU  - Čokeša, Đuro
AU  - Onjia, Antonije E.
PY  - 2008
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/3468
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.
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, Snežana and Popović, Aleksandar R. and Todorović, Žaklina and Čokeša, Đuro 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.",
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., Čokeša, Đ.,& Onjia, A. E.. (2008). Interpretative optimization and artificial neural network modeling of the gas chromatographic separation of polycyclic aromatic hydrocarbons. in Talanta, 76(1), 66-71.
https://doi.org/10.1016/j.talanta.2008.02.004

Sremac S, Popović AR, Todorović Ž, Čokeša Đ, 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, Snežana, Popović, Aleksandar R., Todorović, Žaklina, Čokeša, Đuro, 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 . .

TY  - JOUR
AU  - Ražić, Slavica
AU  - Čokeša, Đuro
AU  - Sremac, Snežana
PY  - 2007
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/3347
AB  - The contents of five metals (Cu, Mn, Fe, Cd, and Pb) in several red and white wines originating from different regions of Serbia were determined by name and graphite furnace atomic absorption spectrometry. The data were processed using chemometric techniques. Principal component and factor analysis were applied in order to highlight the relations between the elements and, after data reduction, three main factors controlling variability were identified. Application of hierarchical cluster analysis to the studied wines indicated differentiation of the samples belonging to different origins. No discrimination between red and white wines was found.
T2  - Journal of the Serbian Chemical Society
T1  - Multivariate data visualization methods based on elemental analysis of wines by atomic absorption spectrometry
VL  - 72
IS  - 12
SP  - 1487
EP  - 1492
DO  - 10.2298/JSC0712487R
ER  - 

@article{
author = "Ražić, Slavica and Čokeša, Đuro and Sremac, Snežana",
year = "2007",
abstract = "The contents of five metals (Cu, Mn, Fe, Cd, and Pb) in several red and white wines originating from different regions of Serbia were determined by name and graphite furnace atomic absorption spectrometry. The data were processed using chemometric techniques. Principal component and factor analysis were applied in order to highlight the relations between the elements and, after data reduction, three main factors controlling variability were identified. Application of hierarchical cluster analysis to the studied wines indicated differentiation of the samples belonging to different origins. No discrimination between red and white wines was found.",
journal = "Journal of the Serbian Chemical Society",
title = "Multivariate data visualization methods based on elemental analysis of wines by atomic absorption spectrometry",
volume = "72",
number = "12",
pages = "1487-1492",
doi = "10.2298/JSC0712487R"
}

Ražić, S., Čokeša, Đ.,& Sremac, S.. (2007). Multivariate data visualization methods based on elemental analysis of wines by atomic absorption spectrometry. in Journal of the Serbian Chemical Society, 72(12), 1487-1492.
https://doi.org/10.2298/JSC0712487R

Ražić S, Čokeša Đ, Sremac S. Multivariate data visualization methods based on elemental analysis of wines by atomic absorption spectrometry. in Journal of the Serbian Chemical Society. 2007;72(12):1487-1492.
doi:10.2298/JSC0712487R .

Ražić, Slavica, Čokeša, Đuro, Sremac, Snežana, "Multivariate data visualization methods based on elemental analysis of wines by atomic absorption spectrometry" in Journal of the Serbian Chemical Society, 72, no. 12 (2007):1487-1492,
https://doi.org/10.2298/JSC0712487R . .