QSRR Model for predicting retention indices of Satureja kitaibelii Wierzb. ex Heuff. essential oil composition
Samo za registrovane korisnike
2020
Članak u časopisu (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
A prediction model of retention indices of compounds from the aboveground parts of Satureja kitaibelii essential oil, obtained by hydrodistillation and analysed by Gas Chromatography coupled with Mass Spectrometry (GC-MS), was the aim of this study. The quantitative structure–retention relationship was employed to predict the retention time using five molecular descriptors selected by a genetic algorithm. The selected descriptors were used as inputs of an artificial neural network. Total of 53 experimentally obtained retention indices (log RI) were used to build a prediction model. The selected descriptors were used as inputs of an artificial neural network model, to build a prediction time predictive quantitative structure-retention relationship model. The coefficient of determination for the training cycle was 0.962, indicating that this model could be used for prediction of retention indices for S. kitaibelii essential oil compounds.
Ključne reči:
artificial neural networks / essential oil / GC-MS / hydrodistillation / QSRR / Satureja kitaibeliiIzvor:
Industrial Crops and Products, 2020, 154, 112752-Izdavač:
- Elsevier
Finansiranje / projekti:
- Ministarstvo nauke, tehnološkog razvoja i inovacija Republike Srbije, institucionalno finansiranje - 200032 (Naučni institut za ratarstvo i povrtarstvo, Novi Sad) (RS-MESTD-inst-2020-200032)
Napomena:
- Supplementary material: https://cherry.chem.bg.ac.rs/handle/123456789/4210
DOI: 10.1016/j.indcrop.2020.112752
ISSN: 0926-6690
WoS: 000554526900121
Scopus: 2-s2.0-85087283113
Kolekcije
Institucija/grupa
Hemijski fakultet / Faculty of ChemistryTY - JOUR AU - Aćimović, Milica G. AU - Pezo, Lato AU - Tešević, Vele AU - Čabarkapa, Ivana AU - Todosijević, Marina PY - 2020 UR - https://cherry.chem.bg.ac.rs/handle/123456789/4209 AB - A prediction model of retention indices of compounds from the aboveground parts of Satureja kitaibelii essential oil, obtained by hydrodistillation and analysed by Gas Chromatography coupled with Mass Spectrometry (GC-MS), was the aim of this study. The quantitative structure–retention relationship was employed to predict the retention time using five molecular descriptors selected by a genetic algorithm. The selected descriptors were used as inputs of an artificial neural network. Total of 53 experimentally obtained retention indices (log RI) were used to build a prediction model. The selected descriptors were used as inputs of an artificial neural network model, to build a prediction time predictive quantitative structure-retention relationship model. The coefficient of determination for the training cycle was 0.962, indicating that this model could be used for prediction of retention indices for S. kitaibelii essential oil compounds. PB - Elsevier T2 - Industrial Crops and Products T1 - QSRR Model for predicting retention indices of Satureja kitaibelii Wierzb. ex Heuff. essential oil composition VL - 154 SP - 112752 DO - 10.1016/j.indcrop.2020.112752 ER -
@article{ author = "Aćimović, Milica G. and Pezo, Lato and Tešević, Vele and Čabarkapa, Ivana and Todosijević, Marina", year = "2020", abstract = "A prediction model of retention indices of compounds from the aboveground parts of Satureja kitaibelii essential oil, obtained by hydrodistillation and analysed by Gas Chromatography coupled with Mass Spectrometry (GC-MS), was the aim of this study. The quantitative structure–retention relationship was employed to predict the retention time using five molecular descriptors selected by a genetic algorithm. The selected descriptors were used as inputs of an artificial neural network. Total of 53 experimentally obtained retention indices (log RI) were used to build a prediction model. The selected descriptors were used as inputs of an artificial neural network model, to build a prediction time predictive quantitative structure-retention relationship model. The coefficient of determination for the training cycle was 0.962, indicating that this model could be used for prediction of retention indices for S. kitaibelii essential oil compounds.", publisher = "Elsevier", journal = "Industrial Crops and Products", title = "QSRR Model for predicting retention indices of Satureja kitaibelii Wierzb. ex Heuff. essential oil composition", volume = "154", pages = "112752", doi = "10.1016/j.indcrop.2020.112752" }
Aćimović, M. G., Pezo, L., Tešević, V., Čabarkapa, I.,& Todosijević, M.. (2020). QSRR Model for predicting retention indices of Satureja kitaibelii Wierzb. ex Heuff. essential oil composition. in Industrial Crops and Products Elsevier., 154, 112752. https://doi.org/10.1016/j.indcrop.2020.112752
Aćimović MG, Pezo L, Tešević V, Čabarkapa I, Todosijević M. QSRR Model for predicting retention indices of Satureja kitaibelii Wierzb. ex Heuff. essential oil composition. in Industrial Crops and Products. 2020;154:112752. doi:10.1016/j.indcrop.2020.112752 .
Aćimović, Milica G., Pezo, Lato, Tešević, Vele, Čabarkapa, Ivana, Todosijević, Marina, "QSRR Model for predicting retention indices of Satureja kitaibelii Wierzb. ex Heuff. essential oil composition" in Industrial Crops and Products, 154 (2020):112752, https://doi.org/10.1016/j.indcrop.2020.112752 . .