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

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Published: Apr 29, 2021
DOI: https://doi.org/10.2298/JSC200524008A
Keywords:
GC–MS, artificial neural networks, hydrodistillation

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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

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 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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How to Cite
[1]
M. Aćimović, L. Pezo, M. Cvetković, J. Stanković, and I. Čabarkapa, “Achillea clypeolata Sibth. & Sm. essential oil composition and QSRR model for predicting retention indices: Scientific paper”, J. Serb. Chem. Soc., vol. 86, no. 4, pp. 355-366, Apr. 2021.
Issue
Vol. 86 No. 4 (2021)
Section
Organic Chemistry
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