Achillea clypeolata Sibth. & Sm. essential oil composition and QSRR model for predicting retention indices

Milica Aćimović; Lato Pezo; Mirjana Cvetković; Jovana Stanković; Ivana Čabarkapa

doi:10.2298/JSC200524008A

Authors

Milica Aćimović Institute of Field and Vegetable Crops Novi Sad, Maksima Gorkog 30, 21000 Novi Sad, Serbia https://orcid.org/0000-0002-5346-1412
Lato Pezo University of Belgrade, Institute of General and Physical Chemistry, Studentski trg 10–12, 1000 Belgrade, Serbia https://orcid.org/0000-0002-0704-3084
Mirjana Cvetković University of Belgrade, Institute of Chemistry, Technology and Metallurgy, Njegoševa 12, 11000 Belgrade, Serbia
Jovana Stanković Institute of Chemistry, Technology and Metallurgy, University of Belgrade, Serbia
Ivana Čabarkapa University of Novi Sad, Institute of Food Technology, Bulevar cara Lazara 1, 21000 Novi Sad, Serbia https://orcid.org/0000-0003-2215-4281

DOI:

https://doi.org/10.2298/JSC200524008A

Keywords:

GC–MS, QSRR, artificial neural networks, essential oil, hydrodistillation

Abstract

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 un–correlated 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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Achillea clypeolata Sibth. & Sm. essential oil composition and QSRR model for predicting retention indices

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