Growing interest in food quality and traceability by regulators as well as consumers demands advances in more rapid, versatile and cost-effective analytical methods. Milk, as most food matrices, is a heterogeneous mixture composed of metabolites, lipids and proteins. One of the major challenges is to have simultaneous, quantitative detection (profiling) of this panel of biomolecules to gather valuable information for assessing food quality, traceability and safety. Here, for milk analysis, atmospheric pressure matrix-assisted laser desorption/ionization employing homogenous liquid sample droplets was used on a Q-TOF mass analyzer. This method has the capability to produce multiply charged proteinaceous ions as well as highly informative profiles of singly charged lipids/metabolites. In two examples, this method is coupled with user-friendly machine-learning software. First, rapid speciation of milk (cow, goat, sheep and camel) is demonstrated with 100% classification accuracy. Second, the detection of cow milk as adulterant in goat milk is shown at concentrations as low as 5% with 92.5% sensitivity and 94.5% specificity.
Piras, C., Hale, O.J., Reynolds, C.K. et al. Speciation and milk adulteration analysis by rapid ambient liquid MALDI mass spectrometry profiling using machine learning. Sci Rep11, 3305 (2021). https://doi.org/10.1038/s41598-021-82846-5