Interpretative optimization and artificial neural network modeling of the gas chromatographic separation of polycyclic aromatic hydrocarbons
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.
Keywords:
PAHs / factorial design / ANN / GC / resolution productSource:
Talanta, 2008, 76, 1, 66-71
DOI: 10.1016/j.talanta.2008.02.004
ISSN: 0039-9140
PubMed: 18585242
WoS: 000256934200012
Scopus: 2-s2.0-43649100022
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Institution/Community
VinčaTY - 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 . .