Characterization of Fatty Acids in Cereals and Oilseeds from the Republic of Serbia by Gas Chromatography - Mass Spectrometry (GC/MS) with Chemometrics

This study investigates the potential of fatty acid profiles of various industrial crops to discriminate them based on their botanical origin. All of the analyzed plants were grown on experimental fields at Rimski Šančevi, Republic of Serbia. Cereal samples (corn, wheat, barley, and oat) were milled...

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Bibliographic Details
Published in:Analytical letters 2020-05, Vol.53 (8), p.1177-1189
Main Authors: Pastor, Kristian, Ilić, Marko, Vujić, Djura, Jovanović, Djordje, Ačanski, Marijana
Format: Article
Language:English
Subjects:
Barley
Cereals
Chemometrics
Chromatography
Classification
Cluster analysis
Crops
Data analysis
Data processing
Discriminant analysis
Esters
fatty acid profile
Fatty acids
Flax
Gas chromatography
gas chromatography-mass spectrometry (GC-MS)
industrial crops
Lipids
Mass spectra
Mass spectrometry
Molecular ions
Multivariate analysis
Oilseed crops
Oilseeds
Principal components analysis
Scientific imaging
Spectroscopy
Wheat
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Summary:This study investigates the potential of fatty acid profiles of various industrial crops to discriminate them based on their botanical origin. All of the analyzed plants were grown on experimental fields at Rimski Šančevi, Republic of Serbia. Cereal samples (corn, wheat, barley, and oat) were milled into flour and lipid fractions were extracted using n-hexane. Lipid fractions of oilseed samples (rape, flax, safflower, and pumpkin) were obtained by mechanical pressing. The fatty acids present in lipid fractions, were derivatized into corresponding volatile methyl esters (FAMEs) and determined on a gas chromatography/mass spectrometry instrument (GC-MS). Peaks of eluting compounds were simultaneously identified using mass spectra libraries, giving a match quality of over 90%. The peaks of molecular ions of methyl esters of 9 dominant fatty acids were extracted, and their abundances used to create numerical matrices for further data processing. Chemometric multivariate data analysis tools-principal component analysis (PCA), cluster analysis (CA), principal coordinate analysis (PCoA) and linear discriminant analysis (LDA), were used to extract significant variables, visualize discriminations, and classify between analyzed plant samples. The applied tools demonstrated separations of analyzed cereals in three groups: corn, oat and the samples of small grains-wheat and barley. In the case of oilseeds a clear separation between all four plant species was obtained. This work represents a new concept of the application of fatty acid analysis in developing authentication methods for industrial crops and their staple food products.
ISSN:0003-2719
1532-236X
DOI:10.1080/00032719.2019.1700270