The combination with a purpose-built machine learning tool based on Linear Discriminant Analysis (LDA) allows for the real-time recognition of milk from different species and the detection of 5% of cow milk in goat milk at high accuracy. This analytical feature together with the possibility to sequence proteins of interest directly from the same MALDI sample by rapidly switching to CID (and potentially ECD/ETD) MS/MS is currently unique for MALDI MS profiling analysis 13. Liquid MALDI samples provide robust and stable ion signal yields 12 of metabolites, lipids and multiply charged proteinaceous analytes 10 in the same analysis/spectrum. In this work, we propose a simple and fast untargeted MS profiling method by utilizing liquid AP-MALDI 10, 11, reducing sample preparation and introduction times as well as the need to find ‘sweet spots’ for optimal analyte ion signals. In addition, all milk samples apart from skimmed milk were defatted by a centrifugation step of 30 min prior to MALDI sample preparation and molecular cut-off filtering with a purification step was necessary in the case of peptides. The reported platform was capable to profile proteins and peptides, although in separate analyses with fundamentally different milk sample preparations, calibration routines, and for peptides, additional instrument settings. Previously, the distinction of different mammalian species has been successfully achieved by profiling proteinaceous milk components with a MALDI-TOF MS platform 4. More importantly, compared to proteins, lipids are relatively small molecules without a species-specific sequence, which can be utilized to identify underlying species.

However, lipid profiles can often be very similar amongst related species such as ruminants. It is a rapid and inexpensive method for the detection of cow milk as an additive to coconut and soya milk 7, 8 that could lead to life-threatening consequences 9. Profiling of lipids by MALDI MS has been successfully used for the discrimination of coconut, soya and bovine milk 5. Milk adulterations have already been targeted through MALDI-TOF MS analysis, focusing on the analysis of either proteins and peptides 4 or lipids 5, 6. This represents a matter of food quality and authenticity but is also a matter of safety for consumers with cow milk allergies 3. One of the most frequent adulterations in the field of dairy products is related to the fraudulent addition of cow milk to milk of other species 2. In modern food production and retail it is important to have rapid and cost-effective analytical methods available for assuring food quality and safety. On the other hand, the commercial value of these alternative products is often higher, and therefore more susceptible to adulteration for fraudulent economic gain. For this reason, consumers look for alternatives to cow milk such as goat and camel milk. Growing concerns about physical well-being and public health have increased the demand for healthier foods with different nutraceutical properties 1. 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. First, rapid speciation of milk (cow, goat, sheep and camel) is demonstrated with 100% classification accuracy. In two examples, this method is coupled with user-friendly machine-learning software. This method has the capability to produce multiply charged proteinaceous ions as well as highly informative profiles of singly charged lipids/metabolites. Here, for milk analysis, atmospheric pressure matrix-assisted laser desorption/ionization employing homogenous liquid sample droplets was used on a Q-TOF mass analyzer. 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. Milk, as most food matrices, is a heterogeneous mixture composed of metabolites, lipids and proteins. Growing interest in food quality and traceability by regulators as well as consumers demands advances in more rapid, versatile and cost-effective analytical methods.

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