Comparative metabolite fingerprinting of chia, flax and sesame seeds using LC-MS untargeted metabolomics

Chia, flax, and sesame seeds are well known for their nutritional quality and are commonly included in bakery products. So far, the development of methods to verify their presence and authenticity in foods is a requisite and a raised need. In this work we applied untargeted metabolomics to propose authenticity markers. Seeds were analyzed by HPLC-MS/MS and 9938 features in negative mode and 9044 in positive mode were obtained by Mzmine. After isotopes grouping, alignment, gap-filling, filtering adducts, and normalization, PCA was applied to explore the dataset and recognize pre-existent classification patterns. OPLS-DA analysis and S-Plots were used as supervised methods. Twenty-five molecules (12 in negative mode and 13 in positive mode) were selected as discriminant for the three seeds, polyphenols and lignans were identified among them. To the best of our knowledge, this is the first approach using non-target HPLC-MS/MS for the authentication of chia, flax and sesame seeds.

Automated summary

In this study, untargeted metabolomics combined with HPLC-MS/MS was used to propose authenticity markers for chia, flax, and sesame seeds, identifying polyphenols and lignans as discriminant compounds. This represents the first approach utilizing non-targeted HPLC-MS/MS for the authentication of these seeds.