Ambient mass spectrometry for rapid authentication of milk from Alpine or lowland forage

Abstract

Metabolomics approaches, such as direct analysis in real time-high resolution mass spectrometry (DART-HRMS), allow characterising many polar and non-polar compounds useful as authentication biomarkers of dairy chains. By using both a partial least squares discriminant analysis (PLS-DA) and a linear discriminant analysis (LDA), this study aimed to assess the capability of DART-HRMS, coupled with a low-level data fusion, discriminate among milk samples from lowland (silages vs. hay) and Alpine (grazing; APS) systems and identify the most informative biomarkers associated with the main dietary forage. As confirmed also by the LDA performed against the test set, DART-HRMS analysis provided an accurate discrimination of Alpine samples; meanwhile, there was a limited capacity to correctly recognise silage- vs. hay-milks. Supervised multivariate statistics followed by metabolomics hierarchical cluster analysis allowed extrapolating the most significant metabolites. Lowland milk was characterised by a pool of energetic compounds, ketoacid derivates, amines and organic acids. Seven informative DART-HRMS molecular features, mainly monoacylglycerols, could strongly explain the metabolomic variation of Alpine grazing milk and contributed to its classification. The misclassification between the two lowland groups confirmed that the intensive dairy systems would be characterised by a small variation in milk composition.

Rapid, novel screening of toxicants in poison baits, and autopsy specimens by ambient mass spectrometry

“…In this study, the matrix effect did not affect our profiling analysis, as was demonstrated by our CV% values. Note that CV% analysis is routinely used to assess data reproducibility in analytical sciences, and it has also been used in ambient mass spectrometry studies for the same purpose ( Dill et al, 2011 ; Abbassi-Ghadi et al, 2015 ; Bilkey et al, 2016 ; Riuzzi et al, 2021 ; Tata et al, 2022b ). In the present study, we have shown once more that QuEChERS-DART-HRMS is characterized by a CV% lower than 20%, which is in line with United States Food and Drug Administration guidelines for chromatographic analytical techniques ( Guidance, 2013 ).…”

Early prediction of paratuberculosis health status in cattle by integrating NMR analysis and blood indices through the LASSO method

“…Afterwards, the two datasets were concatenated by low-level data fusion. Low-level data fusion is a simple concatenation of data in a unique table (24,25). The merged dataset was submitted to the LASSO method, a multinomial regression with L1 penalty, with the aims of i) selecting and shrinking of variables, and ii) predicting the health status of animals, i.e.…”

Authentication of Edible Insects’ Powders by the Combination of DART-HRMS Signatures: The First Application of Ambient Mass Spectrometry to Screening of Novel Food

“…The signals were normalized by sum, whereas each feature was normalized by Pareto scaling. For an initial exploration, the data were concatenated by low-level data fusion [ 21 , 22 , 23 ] (Massaro, 2021, New strategies for the differentiation of fresh and frozen/thawed fish: A rapid and accurate non-targeted method by ambient mass spectrometry and data fusion (part A); Tata, 2022, Ambient mass spectrometry for rapid authentication of milk from Alpine or lowland forage ; Tata, 2022, Detection of soft-refined oils in extra virgin olive oil using data fusion approaches for LC-MS, GC-IMS, and FGC-Enose techniques: The winning synergy of GC-IMS and FGC-Enose ) and submitted to principal component analysis (PCA). Afterwards, the data were split into training (25 samples) and test (8 samples) sets; the training set was used to build the classification model, and the test set was withheld for further validation of the model.…”