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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
Citation
Lebanov, L and Chatterjee, S and Tedone, L and Chapman, SC and Linford, MR and Paull, B, 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, The Journal of Chromatography A, 1618 Article 460853. ISSN 0021-9673 (2020) [Refereed Article]
Copyright Statement
©2020 Elsevier B.V.
DOI: doi:10.1016/j.chroma.2020.460853
Abstract
Analyses 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.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | average mass spectrum, gas chromatography mass spectrometry, ylang-ylang essential oil, multivariate statistical analysis, quality control |
| Research Division: | Chemical Sciences |
| Research Group: | Analytical chemistry |
| Research Field: | Metabolomic chemistry |
| Objective Division: | Manufacturing |
| Objective Group: | Processed non-food agriculture products (excl. wood, paper and fibre) |
| Objective Field: | Essential oils |
| UTAS Author: | Lebanov, L (Mr Leo Lebanov) |
| UTAS Author: | Tedone, L (Ms Laura Tedone) |
| UTAS Author: | Paull, B (Professor Brett Paull) |
| ID Code: | 148568 |
| Year Published: | 2020 |
| Web of Science® Times Cited: | 6 |
| Deposited By: | Chemistry |
| Deposited On: | 2022-01-25 |
| Last Modified: | 2022-08-26 |
| Downloads: | 0 |
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