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/Title ({Prediction of the GC-MS retention time for terpenoids detected in sage \(Salvia officinalis L.\) essential oil using QSRR approach})
/Keywords (QSRR,artificial neural networks.,herbal dust,supercritical fluid extraction,terpenoids)
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QSRR
artificial neural networks.
herbal dust
supercritical fluid extraction
terpenoids
2020-01
{Prediction of the GC-MS retention time for terpenoids detected in sage (Salvia officinalis L.) essential oil using QSRR approach}
bibtex/number/01
bibtex/file/:C$backslash$:/Users/Dekanski/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Pavlic et al. - 2020 - Prediction of the GC-MS retention time for terpenoids detected in sage (Salvia officinalis L.) essential oil usin.pdf;:02_8262_5279.pdf:PDF
bibtex/journal/Journal of the Serbian Chemical Society
bibtex/pages/9--23
bibtex/url/https://www.shd-pub.org.rs/index.php/JSCS/article/view/8262 http://www.doiserbia.nb.rs/ft.aspx?id=0352-51391900097P
bibtex/volume/85
bibtex/issn/0352-5139
bibtex/month/jan
bibtex/bibtexkey/Pavlic2020
Branimir Pavlic
Nemanja Teslic
Predrag Kojic
Lato Pezo
This work aimed to obtain a validated model for prediction of retention time of terpenoids isolated from sage herbal dust using supercritical fluid extraction. In total 32 experimentally obtained retention time of terpenes, which were separated and detected by GC-MS were further used to build a prediction model. The quantitative structure–retention relationship was employed to predict the retention time of essential oil compounds obtained in GC-MS analysis, using six molecular descriptors selected by a genetic algorithm. The selected descriptors were used as inputs of an artificial neural network, to build a retention time predictive quantitative structure–retention relationship model. The coefficient of determination for training cycle was 0.837, indicating that this model could be used for prediction of retention time values for essential oil compounds in sage herbal dust extracts obtained by supercritical fluid extraction due to low prediction error and moderately high r 2 . Results suggested that a 2D autocorrelation descriptor AATS0v was the most influential parameter with an approximately relative importance of 25.1 {\%}.
Serbian Chemical Society
10.2298/jsc190522097p
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