The metabolomic approach to the assessment of cultivar specificity of Brassica napus L. seeds

Recent biomolecular studies tend to involve combinations of different methods and approaches that allow analyzing organisms on the genomic and proteomic levels, as well as on the level of metabolomics. However, in order to justify the use of the metabolomics techniques in plant breeding, it is important to perform comprehensive analysis of a broad range of species and varieties. In this study, we evaluated the contents of low-molecular-weight substances in seeds of different rapeseed cultivars by the gas chromatography–mass spectrometry (GC-MS) technique. For every metabolomic profile, we estimated 168 target substances, and 52 of them were unambiguously identified. These compounds included amino acids, organic and fatty acids, tocopherols, and phytosterols. In order to keep the data assay within the context of multivariate statistics, we used principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and partial least square regression (PLS-R). Subsequent analysis revealed a significant difference between the metabolomic profiles of the investigated rapeseed cultivars, with the primary role of the amino acids and organic acids. Noticeably, the PLS-DA model showed 65% of the explained variance and, according to the Venetian blinds cross- validation test, 91.67 % of the accuracy. Thus, we demonstrate the effectiveness of the metabolomics approach to the varietal identification of seeds. This strategy can be further improved with a continuously updated database of the metabolomic profiles of different species and cultivars. Application of the PLS-DA method will allow comparison of the metabolites of unknown samples with the existing profiles and, subsequently, identification of new seed samples