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Radical scavenging activity and metabolomic profiling study of ylang-ylang essential oils based on high-performance thin-layer chromatography and multivariate statistical analysis

journal contribution
posted on 2023-05-21, 02:21 authored by Leo LebanovLeo Lebanov, Lam, SC, Tedone, L, Sostaric, T, Jason SmithJason Smith, Alireza GhiasvandAlireza Ghiasvand, Brett PaullBrett Paull
Ylang-ylang (YY) essential oil (EO) is distilled from the fresh-mature flowers of the Annonaceae family tropical tree Cananga odorata [Lam.] Hook. f. & Thomson, and is widely used in perfume and cosmetic industries for its fragrant character. Herein, two different metabolomic profiles obtained using high-performance thin-layer chromatography (HPTLC), applying different stains, namely 2,2-diphenyl-1-picrylhydrazyl (DPPH·) and p-anisaldehyde, were used for discrimination of 52 YY samples across geographical origins and distillation grades. The first profile is developed using the DPPH· stain based on the radical scavenging activity (RSA) of YY EOs. Results of the HPTLC-DPPH· assay confirmed that RSA of YY EOs is in proportion to the length of distillation times. Major components contributing to the RSA of YY EOs were tentatively identified as germacrene D and α-farnesene, eugenol and linalool, by gas chromatography-mass spectrometry (GC–MS) and GC-flame ionisation detector (GC-FID). The second profile was developed using the general-purpose p-anisaldehyde stain based on the general chemical composition of YY EOs. Untargeted metabolomic discrimination of YY EOs from different geographical origins was performed based on the HPTLC-p-anisaldehyde profiles, followed by principal component analysis (PCA). A discrimination and prediction model for identification of YY distillation grade was developed using PCA and partial least squares regression (PLS) based on binned HPTLC-ultraviolet (254 nm) profiles, which was successfully applied to distillation grade determination of blended YY Complete EOs.


Publication title

Journal of Chromatography B



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School of Natural Sciences


Elsevier Science Bv

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