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The variation in the content and composition of essential oils of Bidens tripartita L. grown in Lithuania has been studied using supercritical CO₂ extraction followed by GC–MS. Extraction of essential oils was performed at two different pressures (9.1 and 15.1 MPa). Plant material has been harvested at different phenological stages (from June to September). A total of 26 different compounds were identified in the essential oils. The highest content and diversity of compounds was determined during the full-flowering stage. Identified substances were mainly monoterpene hydrocarbons and sesquiterpene hydrocarbons. The main components were α-pinene, ρ-cymene, trans-β-ocimene, and β-elemene. Chemometric analysis, including principal component and hierarchical cluster analyses, showed the dependence of the essential oils qualitative as well as quantitative composition on the phenological stage the plant was collected at. It also revealed that there was no correlation between age of the plant and quantity of the identified compounds.
The aim of this study was to evaluate the variation of essential oils composition of Bidens tripartita L. collected during three successive years (2009–2011). Essential oils were obtained by supercritical CO2 extraction, applying gas chromatographic–mass spectrometric (GC–MS) analysis for identification of volatile compounds afterwards. The essential oils of B. tripartita showed a characteristic chemical composition from year to year, observing both quantitative and qualitative compounds differences. The yield of essential oils was 22 and 35 % higher in 2010 year material than in 2009 and 2011 year, respectively. The main compounds found in the B. tripartita essential oils were α-pinene (3.7–12.1 %), p-cymene (2.8–8.0 %), β-ocimene (40.5–45.9 %), β-elemene (9.9–15.6 %), iso and α-caryophyllenes (4.3–6.8 % and 5.2–8.2 %), and α-bergamotene (3.3–9.4 %). To determine the significance of changes in the identified compounds in the samples, representing different plant collection year, statistical hypothesis testing was applied. For classification of these samples to the groups and evaluation of similarity between them principal component analysis, multivariate analysis of variance and hierarchical cluster analysis techniques were used. The correlation analysis helped to find out the strength of linear relationship between amount of each compound and meteorological data (temperature and precipitations).
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