Authentication of forage-based milk by mid-level data fusion of ( plus /-) DART-HRMS signatures

The study aimed to authenticate milk samples derived from maize silage (MS)-, crop silage/hay (SH)and grassland hay (GH)-based diets through direct analysis in real time coupled to high resolution mass spectrometry (DART-HRMS). A mid-level data fusion partial least squareediscriminant analysis (PLS-DA) allowed the correct separation of milk samples, as confirmed by a linear discriminant analysis (LDA) validation procedure. A pool of ions (absolute loading value > 0.3) of a set of four (two extraction solvents per two ion modes) PLS-DA was selected, fused and submitted to hierarchical cluster analysis. This approach identified the 25 most informative DART-HRMS biomarkers: lactate, glutamate and hydroxycinnamic acid for MS; creatinine, methyl 2-furoate, norgramine, C18:2, C20:2 and C22:2 monoacylglycerols for SH; palmitate, O-methylated flavonoid and glyceric acid for GH. This restricted (+/-) DART-HRMS pool of low-molecular-weight biomolecules confirmed that forage botanical origin and conservation method affect the milk metabolomics profile. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

The study utilized DART-HRMS technology to authenticate milk samples derived from different feed sources, identifying key biomarkers that influence the milk metabolomics profile through PLS-DA and LDA analysis. The conclusion suggests that the botanical origin of feed and conservation method significantly impact the composition of milk.