Interpretative optimization and artificial neural network modeling of the gas chromatographic separation of polycyclic aromatic hydrocarbons
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2008
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Metapodaci
Prikaz svih podataka o dokumentuApstrakt
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.
Ključne reči:
PAHs / factorial design / ANN / GC / resolution productIzvor:
Talanta, 2008, 76, 1, 66-71Izdavač:
- Elsevier Science Bv, Amsterdam
Finansiranje / projekti:
- Nove metode i tehnike za separaciju i specijaciju hemijskih elemenata u tragovima, organskih supstanci i radionuklida i identifikaciju njihovih izvora (RS-MESTD-MPN2006-2010-142039)
DOI: 10.1016/j.talanta.2008.02.004
ISSN: 0039-9140
PubMed: 18585242
WoS: 000256934200012
Scopus: 2-s2.0-43649100022
Kolekcije
Institucija/grupa
Hemijski fakultet / Faculty of ChemistryTY - 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 . .