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Interpretative optimization and artificial neural network modeling of the gas chromatographic separation of polycyclic aromatic hydrocarbons

Authorized Users Only
2008
Authors
Sremac, Snežana
Popović, Aleksandar R.
Todorović, Žaklina
Čokeša, Đuro
Onjia, Antonije
Article (Published version)
Metadata
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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.

Keywords:
PAHs / factorial design / ANN / GC / resolution product
Source:
Talanta, 2008, 76, 1, 66-71
Publisher:
  • Elsevier, Amsterdam
Funding / projects:
  • Nove metode i tehnike za separaciju i specijaciju hemijskih elemenata u tragovima, organskih supstanci i radionuklida i identifikaciju njihovih izvora (RS-142039)

DOI: 10.1016/j.talanta.2008.02.004

ISSN: 0039-9140

PubMed: 18585242

WoS: 000256934200012

Scopus: 2-s2.0-43649100022
[ Google Scholar ]
16
15
URI
http://TechnoRep.tmf.bg.ac.rs/handle/123456789/1347
Collections
  • Radovi istraživača / Researchers’ publications (TMF)
Institution/Community
Tehnološko-metalurški fakultet
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
UR  - conv_3026
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",
url = "conv_3026"
}
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
conv_3026
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
conv_3026 .
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 .,
conv_3026 .

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