Essential oil profile of Origanum vulgare subsp. vulgare native population from Rtanj via chemometrics tools

Chemia Naissensis Volume 3, No.2 (2020) (стр. 100-116) 

АУТОР(И) / AUTHOR(S): Milica Aćimović, Lato Pezo, Stefan Ivanović, Katarina Simić, Jovana Ljujic

Е-АДРЕСА / E-MAIL: milica.acimovic@ifvcns.ns.ac.rs

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DOI: 10.46793/ChemN3.2.100A

САЖЕТАК / ABSTRACT:

The aim of this study was to predict the retention indices of chemical compounds found in the aerial parts of Origanum vulgare subsp. vulgare essential oil, obtained by hydrodistillation and analyzed by GC-MS. A total number of 28 compounds were detected in the essential oil. The compounds with the highest relative concentrations were germacrene D (21.5%), 1,8-cineole (14.2%), sabinene (14.0%) and trans-caryophyllene (13.4%). The retention time was predicted by using the quantitative structure–retention relationship, using seven molecular descriptors chosen by factor analysis and genetic algorithm. The chosen descriptors were mutually uncorrelated, and they were used to develop an artificial neural network model. A total number of 28 experimentally obtained retention indices (log RI) were used to set up a predictive quantitative structure-retention relationship model. The coefficient of determination for the training cycle was 0.998, indicating that this model could be used for predicting retention indices for O. vulgare subsp. vulgare essential oil compounds.

КЉУЧНЕ РЕЧИ / KEYWORDS:

The aim of this study was to predict the retention indices of chemical compounds found in the aerial parts of Origanum vulgare subsp. vulgare essential oil, obtained by hydrodistillation and analyzed by GC-MS. A total number of 28 compounds were detected in the essential oil. The compounds with the highest relative concentrations were germacrene D (21.5%), 1,8-cineole (14.2%), sabinene (14.0%) and trans-caryophyllene (13.4%). The retention time was predicted by using the quantitative structure–retention relationship, using seven molecular descriptors chosen by factor analysis and genetic algorithm. The chosen descriptors were mutually uncorrelated, and they were used to develop an artificial neural network model. A total number of 28 experimentally obtained retention indices (log RI) were used to set up a predictive quantitative structure-retention relationship model. The coefficient of determination for the training cycle was 0.998, indicating that this model could be used for predicting retention indices for O. vulgare subsp. vulgare essential oil compounds.

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