TY - JOUR
T1 - The application of GC-MS combined with chemometrics for the identification of antimicrobial compounds from selected commercial essential oils
AU - Maree, Joanet
AU - Kamatou, Guy
AU - Gibbons, Simon
AU - Viljoen, Alvaro
AU - Van Vuuren, Sandy
PY - 2014/1/15
Y1 - 2014/1/15
N2 - Essential oils are produced by plants for many reasons including protection against various bacterial, fungal and viral infections. Numerous essential oils and their major constituents are known to exhibit promising antimicrobial activity and can therefore be a good source of biologically active molecules and/or fractions. It is generally accepted that a crude phytomedicine needs to be evaluated holistically and the research method best suited for this approach is metabolomics. In this study a non-targeted metabolomic approach was followed to explore the antimicrobial activity and chemistry of various commercial essential oils. The antimicrobial activity of the essential oils was determined against three Gram-positive and two Gram-negative bacterial organisms as well as two yeasts. The essential oil composition was determined by gas chromatography coupled with mass spectrometry (GC-MS) analyses and the resulting chromatograms were exported to MarkerLynx™ application manager software for peak selection and alignment. Orthogonal projection to latent structures discriminant analysis (OPLS-DA) models were constructed and used to filter out putative retention time mass (RTM) pairs responsible for the separation of the two defined classes. The selected RTM pairs were used to identify the corresponding biomarkers. Eugenol was identified as a biomarker attributing to the good antibacterial activity of the samples observed against all tested bacteria and Candida albicans. In contrast, α-pinene, limonene and sabinene, as a mixture or independently, and limonene and α-phellandrene were identified as compounds responsible for samples displaying poorer antibacterial and antifungal activity respectively. The proposed method of using chemometric analysis to evaluate GC-MS chromatograms in combination with biological activity was successfully applied to identify putative biomarkers. © 2013 Elsevier B.V.
AB - Essential oils are produced by plants for many reasons including protection against various bacterial, fungal and viral infections. Numerous essential oils and their major constituents are known to exhibit promising antimicrobial activity and can therefore be a good source of biologically active molecules and/or fractions. It is generally accepted that a crude phytomedicine needs to be evaluated holistically and the research method best suited for this approach is metabolomics. In this study a non-targeted metabolomic approach was followed to explore the antimicrobial activity and chemistry of various commercial essential oils. The antimicrobial activity of the essential oils was determined against three Gram-positive and two Gram-negative bacterial organisms as well as two yeasts. The essential oil composition was determined by gas chromatography coupled with mass spectrometry (GC-MS) analyses and the resulting chromatograms were exported to MarkerLynx™ application manager software for peak selection and alignment. Orthogonal projection to latent structures discriminant analysis (OPLS-DA) models were constructed and used to filter out putative retention time mass (RTM) pairs responsible for the separation of the two defined classes. The selected RTM pairs were used to identify the corresponding biomarkers. Eugenol was identified as a biomarker attributing to the good antibacterial activity of the samples observed against all tested bacteria and Candida albicans. In contrast, α-pinene, limonene and sabinene, as a mixture or independently, and limonene and α-phellandrene were identified as compounds responsible for samples displaying poorer antibacterial and antifungal activity respectively. The proposed method of using chemometric analysis to evaluate GC-MS chromatograms in combination with biological activity was successfully applied to identify putative biomarkers. © 2013 Elsevier B.V.
U2 - 10.1016/j.chemolab.2013.11.004
DO - 10.1016/j.chemolab.2013.11.004
M3 - Article
VL - 130
SP - 172
EP - 181
JO - Chemometrics and Intelligent Laboratory Systems
JF - Chemometrics and Intelligent Laboratory Systems
SN - 0169-7439
ER -