Abstract
The complexity of modern food supply chains limits the effectiveness of targeted approaches to address food traceability issues. Untargeted metabolomics provides a comprehensive profile of small molecules present within biological samples. In this study, the potential of ultra-high performance liquid chromatography-ion mobility-high resolution mass spectrometry (UHPLC-IMS-HRMS) to discriminate bovine milk samples collected at individual level was evaluated for traceability purposes. For the first time, IMS coupled with UHPLC-HRMS was applied to milk analysis, increasing confidence in metabolite annotation. Supervised Partial Least Squares-Discriminant Analysis coupled to backward elimination variable selection allowed the selection of 52 and 153 features able to discriminate samples belonging to different dairy supply chains and trace samples at herd level, respectively. Amino acids, glycerolipids, and glycerophospholipids were the most represented classes, influencing the biological/technological properties of the final product. The perfect classification of samples belonging to external test sets demonstrated the reliability of the proposed approach.
Published Version
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