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Artificial neural network prediction of quantitative structure: Retention relationships of polycyclic aromatic hydocarbons in gas chromatography
- Source :
- Journal of the Serbian Chemical Society, Vol 70, Iss 11, Pp 1291-1300 (2005)
- Publication Year :
- 2005
- Publisher :
- Serbian Chemical Society, 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 %).
- Subjects :
- retention index
gc
ann
pahs
qsrr
molecular descriptors
Chemistry
QD1-999
Subjects
Details
- Language :
- English
- ISSN :
- 03525139 and 18207421
- Volume :
- 70
- Issue :
- 11
- Database :
- Directory of Open Access Journals
- Journal :
- Journal of the Serbian Chemical Society
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.f97aadb5b3a40209f9b1e0e1f15dff1
- Document Type :
- article
- Full Text :
- https://doi.org/10.2298/JSC0511291S