Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/47223
Type of publication: Straipsnis Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or / and Scopus (S1)
Field of Science: Chemija / Chemistry (N003)
Author(s): Kaškonienė, Vilma;Kaškonas, Paulius;Maruška, Audrius;Ragažinskienė, Ona
Title: Essential oils of Bidens tripartita L. collected during period of 3 years composition variation analysis
Is part of: Acta physiologiae plantarum. Heidelberg : Springer, Vol. 35, iss. 4, 2013
Extent: p. 1171-1178
Date: 2013
Note: Online ISSN 1861-1664
Keywords: Bidens tripartita L;Eteriniai aliejai;Augalų vegetacijos periodas;Superkritinė ekstrakcija;Bidens tripartita L;Essential oils;Supercritical fluid extraction;Meteorological data;Chemometric analysis
Abstract: 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)
Internet: http://link.springer.com/article/10.1007%2Fs11738-012-1156-y
http://link.springer.com/article/10.1007%2Fs11738-012-1156-y
Affiliation(s): Biochemijos katedra
Gamtos mokslų fakultetas
Kauno technologijos universitetas
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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