Discriminating cereal and pseudocereal speciesusing binary system of GC/MS data – Pattern recognition approach

Kristian Pastor, Lato Pezo, Djura Vujić, Djordje Jovanović, Marijana Ačanski

Abstract


Various cultivars of different cereal and pseudocereal species (wheat – 9, barley– 8, rye– 1, oat– 3, triticale– 2, spelt– 3, corn – 12, amaranth– 3, buckwheat – 9cultivar samples) were milled into flour, extracted using n-hexane, derivatized with trimethylsulfonium hydroxide solution, and subjected to GC-MS analysis. Fatty acid methyl esters and non-saponifiable compounds (phytosterols, alpha-tocopherol and squalene) were identifiedby comparing mass spectra with Wiley MS library. Binary system was applied in further data processing: the presence or the absence of a particular lipid component was coded with either (1) or (0), in each sample. Major lipid components, that were present in all analyzed flour samples, were removed from further data analysis, leaving only those that represent a good pattern to differentiate flour samples according to corresponding cereal/pseudocereal species. Pattern recognition tools (cluster analysis and principal component analysis) were applied to visualize groupings and separations among samples. The presented approach enables differentiation of flour samples made from various cereal/pseudocereal species according to their botanical origin and gluten content in a rapid way, successfully avoiding exact quantitative determinations.


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DOI: http://dx.doi.org/10.2298/JSC170926014P

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