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Comprehensive characterisation of ylang-ylang essential oils according to distillation time, origin, and chemical composition using a multivariate approach applied to average mass spectra and segmented average mass spectral data
journal contribution
posted on 2023-05-21, 05:18 authored by Leo LebanovLeo Lebanov, Chatterjee, S, Tedone, L, Chapman, SC, Linford, MR, Brett PaullBrett PaullAnalyses of the complex essential oil samples using gas chromatography hyphenated with mass spectrometry (GC–MS) generate large three-way data arrays. Processing such large data sets and extracting meaningful information in the metabolic studies of natural products requires application of multivariate statistical techniques (MSTs). From the GC–MS raw data several different input data sets for the MSTs can be created, including total chromatogram average mass spectra (TCAMS), segmented average mass spectra (SAMS) and chemical composition. Herein, we compared the performance of MSTs on average mass spectrum based data sets, TCAMS and SAMS, against chemical composition and attenuated total reflectance - Fourier transformation infrared (ATR-FTIR) spectroscopy in the evaluation of quality of ylang-ylang essential oils, based on their grade, geographical origin and chemical composition, using principal component analysis (PCA), partial least squares regression (PLS) and discriminatory analysis (PLS-DA). PCA based on TCAMS, SAMS and chemical composition showed clear trends amongst the samples based on increase in grade (distillation time). PLS-DA applied to TCAMS, SAMS and ATR-FTIR discriminated between all geographical origins. Predicted relative abundances of the 18 most important compounds, using PLS regression models on TCAMS, SAMS and ATR-FTIR, were successfully applied to ylang-ylang essential oil quality assessment based on comparison with the ISO 3063:2004 standard, where the SAMS data set showed superior performance, compared to other data sets.
Funding
Australian Institute of Mathematics & Science Pty Ltd
History
Publication title
The Journal of Chromatography AVolume
1618Article number
460853Number
460853ISSN
0021-9673Department/School
School of Natural SciencesPublisher
Elsevier Science BvPlace of publication
Po Box 211, Amsterdam, Netherlands, 1000 AeRights statement
©2020 Elsevier B.V.Repository Status
- Restricted