Gas chromatography electron impact ionization mass spectrometry (GC-EI-MS) has been, and remains, the most widely applied analytical technique for metabolomic studies of essential oils. GC-EI-MS analysis of complex samples, such as essential oils, creates a large volume of data. Creating predictive models for such samples and observing patterns within complex data sets presents a significant challenge and requires application of robust data handling and data analysis methods. Accordingly, a wide variety of software and algorithms has been investigated and developed for this purpose over the years. This review provides an overview and summary of that research effort, and attempts to classify and compare different data handling and data analysis procedures that have been reported to-date in the metabolomic study of essential oils using GC-EI-MS.

Item Type:	Refereed Article
Keywords:	gas chromatography, electron impact ionization mass spectrometry, data handling, multivariate statistical analysis, essential oils
Research Division:	Chemical Sciences
Research Group:	Analytical chemistry
Research Field:	Separation science
Objective Division:	Expanding Knowledge
Objective Group:	Expanding knowledge
Objective Field:	Expanding knowledge in the biomedical and clinical sciences
UTAS Author:	Lebanov, L (Mr Leo Lebanov)
UTAS Author:	Ghiasvand, A (Professor Alireza Ghiasvand)
UTAS Author:	Paull, B (Professor Brett Paull)
ID Code:	146502
Year Published:	2021
Web of Science® Times Cited:	10
Deposited By:	Chemistry
Deposited On:	2021-09-09
Last Modified:	2022-08-22
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