Discriminating cereal and pseudocereal species using a binary system of GC/MS data: A pattern recognition approach

Abstract

Various cultivars of different cereal and pseudocereal species (9 wheat, 8 barley, 1 rye, 3 oat, 2 triticale, 3 spelt, 12 corn, 3 amaranth and 9 buckwheat cultivar 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, α-tocopherol and squalene) were identified by comparing mass spectra with the Wiley MS library. A binary system was applied in further data processing: the presence or the absence of a particular lipid component in each sample was coded with either (1) or (0). 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 the 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 the samples. The presented approach enables the rapid differentiation of flour samples made from various cereal/pseudocereal species according to their botanical origin and gluten content, thereby, successfully avoiding exact quantitative determinations.

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