Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography
Апстракт
A feed-forward artificial neural network (ANN) model was used to link molecular structures (boiling points, connectivity indices and molecular weights) and retention indices of polycyclic aromatic hydrocarbons (PAHs) in linear temperature-programmed gas chromatography. A randomly taken subset of PAH retention data reported by Lee et al, [Anal. Chem. 51 (1979) 768], containing retention index data for 30 PAHs, was used to make the ANN model. The prediction ability of the trained ANN was tested on unseen data for 18 PAHs from the same article, as well as on the retention data for 7 PAHs experimentally obtained in this work. In addition, two different data sets with known retention indices taken from the literature were analyzed by the same ANN model. It has been shown that the relative accuracy as the degree of agreement between the measured and the predicted retention indices in all testing sets, for most of the studied PAHs, were within the experimental error margins (3 %).
Кључне речи:
retention index / GC / ANN / PAHs / QSRR / molecular descriptorsИзвор:
Journal of the Serbian Chemical Society, 2005, 70, 11, 1291-1300Издавач:
- Srpsko hemijsko društvo, Beograd
DOI: 10.2298/JSC0511291S
ISSN: 0352-5139
WoS: 000234277000007
Scopus: 2-s2.0-31544473772
Институција/група
Tehnološko-metalurški fakultetTY - JOUR AU - Sremac, Snežana AU - Skrbić, Biljana AU - Onjia, Antonije PY - 2005 UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/841 AB - A feed-forward artificial neural network (ANN) model was used to link molecular structures (boiling points, connectivity indices and molecular weights) and retention indices of polycyclic aromatic hydrocarbons (PAHs) in linear temperature-programmed gas chromatography. A randomly taken subset of PAH retention data reported by Lee et al, [Anal. Chem. 51 (1979) 768], containing retention index data for 30 PAHs, was used to make the ANN model. The prediction ability of the trained ANN was tested on unseen data for 18 PAHs from the same article, as well as on the retention data for 7 PAHs experimentally obtained in this work. In addition, two different data sets with known retention indices taken from the literature were analyzed by the same ANN model. It has been shown that the relative accuracy as the degree of agreement between the measured and the predicted retention indices in all testing sets, for most of the studied PAHs, were within the experimental error margins (3 %). PB - Srpsko hemijsko društvo, Beograd T2 - Journal of the Serbian Chemical Society T1 - Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography EP - 1300 IS - 11 SP - 1291 VL - 70 DO - 10.2298/JSC0511291S ER -
@article{ author = "Sremac, Snežana and Skrbić, Biljana and Onjia, Antonije", year = "2005", abstract = "A feed-forward artificial neural network (ANN) model was used to link molecular structures (boiling points, connectivity indices and molecular weights) and retention indices of polycyclic aromatic hydrocarbons (PAHs) in linear temperature-programmed gas chromatography. A randomly taken subset of PAH retention data reported by Lee et al, [Anal. Chem. 51 (1979) 768], containing retention index data for 30 PAHs, was used to make the ANN model. The prediction ability of the trained ANN was tested on unseen data for 18 PAHs from the same article, as well as on the retention data for 7 PAHs experimentally obtained in this work. In addition, two different data sets with known retention indices taken from the literature were analyzed by the same ANN model. It has been shown that the relative accuracy as the degree of agreement between the measured and the predicted retention indices in all testing sets, for most of the studied PAHs, were within the experimental error margins (3 %).", publisher = "Srpsko hemijsko društvo, Beograd", journal = "Journal of the Serbian Chemical Society", title = "Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography", pages = "1300-1291", number = "11", volume = "70", doi = "10.2298/JSC0511291S" }
Sremac, S., Skrbić, B.,& Onjia, A.. (2005). Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography. in Journal of the Serbian Chemical Society Srpsko hemijsko društvo, Beograd., 70(11), 1291-1300. https://doi.org/10.2298/JSC0511291S
Sremac S, Skrbić B, Onjia A. Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography. in Journal of the Serbian Chemical Society. 2005;70(11):1291-1300. doi:10.2298/JSC0511291S .
Sremac, Snežana, Skrbić, Biljana, Onjia, Antonije, "Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography" in Journal of the Serbian Chemical Society, 70, no. 11 (2005):1291-1300, https://doi.org/10.2298/JSC0511291S . .