QSRR Model for predicting retention indices of Satureja kitaibelii Wierzb. ex Heuff. essential oil composition
Само за регистроване кориснике
2020
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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.
Кључне речи:
Satureja kitaibelii / Essential oil / Hydrodistillation / GC-MS / QSRR / Artificial neural networksИзвор:
Industrial Crops and Products, 2020, 154Издавач:
- Elsevier, Amsterdam
Финансирање / пројекти:
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200032 (Научни институт за ратарство и повртарство, Нови Сад) (RS-MESTD-inst-2020-200032)
DOI: 10.1016/j.indcrop.2020.112752
ISSN: 0926-6690
WoS: 000554526900121
Scopus: 2-s2.0-85087283113
Колекције
Институција/група
FiVeRTY - JOUR AU - Aćimović, Milica AU - Pezo, Lato AU - Tešević, Vele AU - Čabarkapa, Ivana AU - Todosijević, Marina PY - 2020 UR - http://fiver.ifvcns.rs/handle/123456789/1994 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, Amsterdam T2 - Industrial Crops and Products T1 - QSRR Model for predicting retention indices of Satureja kitaibelii Wierzb. ex Heuff. essential oil composition VL - 154 DO - 10.1016/j.indcrop.2020.112752 ER -
@article{ author = "Aćimović, Milica 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, Amsterdam", journal = "Industrial Crops and Products", title = "QSRR Model for predicting retention indices of Satureja kitaibelii Wierzb. ex Heuff. essential oil composition", volume = "154", doi = "10.1016/j.indcrop.2020.112752" }
Aćimović, M., 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, Amsterdam., 154. https://doi.org/10.1016/j.indcrop.2020.112752
Aćimović M, 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. doi:10.1016/j.indcrop.2020.112752 .
Aćimović, Milica, 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), https://doi.org/10.1016/j.indcrop.2020.112752 . .