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/Author (Acimovic, Milica and Pezo, Lato and Cvetkovic, Mirjana and Stankovic, Jovana and Cabarkapa, Ivana)
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/Title ({Achillea clypeolata Sibth. & Sm. essential oil composition and QSRR model for predicting retention indices})
/Keywords (GC-MS,artificial neural networks,hydrodistillation)
/bibtex#2Fyear (2021)
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/bibtex#2Fpages (355--366)
/Subject (The aim of this study was the prediction model of retention indices of compounds from the aboveground parts of Achillea clypeolata Sibth. & Sm. essential oil, obtained by hydrodistillation and analysed by GC-MS. The quantitative structure-retention relationship analysis was applied in order to anticipate the retention time of the obtained compounds. The selection of the seven molecular descriptors was done by a genetic algorithm. The chosen descriptors were uncorrelated and were used to construct an artificial neural network. A total of 40 experimentally obtained retention indices was used to build this prediction model. The coefficient of determination for the training, testing and validation cycles were: 0.950, 0.825 and 1.000, respectively, indicating that this model could be used for prediction of retention indices for A. clypeolata, essential oil compounds.)
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GC-MS
artificial neural networks
hydrodistillation
2021-04
{Achillea clypeolata Sibth. & Sm. essential oil composition and QSRR model for predicting retention indices}
bibtex/number/04
bibtex/file/:D\:/OneDrive/Mendeley/Journal of the Serbian Chemical Society/2021/Acimovic et al. - 2021 - Achillea clypeolata Sibth. & Sm. essential oil composition and QSRR model for predicting retention indices.pdf:pdf;:02_9536_5426.pdf:PDF
bibtex/journal/J. Serbian Chem. Soc.
bibtex/pages/355--366
bibtex/url/https://www.shd-pub.org.rs/index.php/JSCS/article/view/9536
bibtex/volume/86
bibtex/issn/0352-5139
bibtex/month/apr
bibtex/bibtexkey/Acimovic2021
Milica Acimovic
Lato Pezo
Mirjana Cvetkovic
Jovana Stankovic
Ivana Cabarkapa
The aim of this study was the prediction model of retention indices of compounds from the aboveground parts of Achillea clypeolata Sibth. & Sm. essential oil, obtained by hydrodistillation and analysed by GC-MS. The quantitative structure-retention relationship analysis was applied in order to anticipate the retention time of the obtained compounds. The selection of the seven molecular descriptors was done by a genetic algorithm. The chosen descriptors were uncorrelated and were used to construct an artificial neural network. A total of 40 experimentally obtained retention indices was used to build this prediction model. The coefficient of determination for the training, testing and validation cycles were: 0.950, 0.825 and 1.000, respectively, indicating that this model could be used for prediction of retention indices for A. clypeolata, essential oil compounds.
Serbian Chemical Society
10.2298/jsc200524008a
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