Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Serbian Chemical Society
2005-11-01
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| Series: | Journal of the Serbian Chemical Society |
| Subjects: | |
| Online Access: | http://www.shd.org.yu/HtDocs/SHD/vol70/No11/JSCS_V70_No11-07.pdf |
| _version_ | 1811316930068348928 |
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| author | SNEZANA SREMAC BILJANA SKRBIC ANTONIJE ONJIA |
| author_facet | SNEZANA SREMAC BILJANA SKRBIC ANTONIJE ONJIA |
| author_sort | SNEZANA SREMAC |
| collection | DOAJ |
| description | 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%). |
| first_indexed | 2024-04-13T11:57:51Z |
| format | Article |
| id | doaj.art-1caaa430279d433db20cd08e63b77e5b |
| institution | Directory Open Access Journal |
| issn | 0352-5139 |
| language | English |
| last_indexed | 2024-04-13T11:57:51Z |
| publishDate | 2005-11-01 |
| publisher | Serbian Chemical Society |
| record_format | Article |
| series | Journal of the Serbian Chemical Society |
| spelling | doaj.art-1caaa430279d433db20cd08e63b77e5b2022-12-22T02:47:52ZengSerbian Chemical SocietyJournal of the Serbian Chemical Society0352-51392005-11-01701112911300Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatographySNEZANA SREMACBILJANA SKRBICANTONIJE ONJIAA 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%).http://www.shd.org.yu/HtDocs/SHD/vol70/No11/JSCS_V70_No11-07.pdfretention indexGCANNPAHsQSRRmolecular descriptors. |
| spellingShingle | SNEZANA SREMAC BILJANA SKRBIC ANTONIJE ONJIA Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography Journal of the Serbian Chemical Society retention index GC ANN PAHs QSRR molecular descriptors. |
| title | Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography |
| title_full | Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography |
| title_fullStr | Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography |
| title_full_unstemmed | Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography |
| title_short | Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography |
| title_sort | artificial neural network prediction of quantitative structure retention relationships of polycyclic aromatic hydocarbons in gas chromatography |
| topic | retention index GC ANN PAHs QSRR molecular descriptors. |
| url | http://www.shd.org.yu/HtDocs/SHD/vol70/No11/JSCS_V70_No11-07.pdf |
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