Milk and dairy products are important sources of nutrients, but avoidance is common due to allergies, intolerances, lifestyle choices, or ethical considerations. Alternatives like goat, sheep, and camel milk, and plant-based milks (oat, soy) are gaining popularity. However, the increasing demand and potential supply chain issues increase the risk of milk adulteration—the fraudulent addition of cheaper milks or other substances to premium products. Milk adulteration poses serious health risks due to potential allergens or harmful additives and is difficult to detect with current methods. Existing techniques, like isoelectric focusing (IEF), are time-consuming and have limitations, while DNA-based methods require complex sample preparation. High-resolution mass spectrometry (HRMS) offers a potential solution, but often involves complex sample preparation. Ambient mass spectrometry (AMS) techniques offer a faster, simpler alternative. DESI-MS, a soft ionization technique requiring minimal sample preparation, is a promising candidate for rapid milk authentication and adulteration detection. This study aimed to develop a reliable, sensitive, and rapid DESI-MS-based method for identifying milk from different sources and detecting adulteration, focusing on the detection of cow milk in other milk types.

Several methods have been explored for milk species differentiation. DNA-based techniques, particularly PCR, are favored but are time-consuming and require complex sample preparation. High-resolution mass spectrometry (HRMS) shows promise for speciation, but often involves sample pretreatment that can negatively impact analysis. Ambient mass spectrometry (AMS) techniques, such as Direct Analysis in Real Time (DART), offer advantages in speed and simplicity, but may still require pretreatment steps and expensive consumables. DESI-MS, a soft ionization technique, presents several key advantages for milk analysis. It doesn't require organic solvents, it is minimally destructive, and it allows for direct analysis of diluted samples, significantly reducing analysis time.

The study optimized a DESI-MS method for milk analysis. Three sample preparation methods were compared: direct analysis, methanol dilution, and water dilution. Water dilution (1:4 milk:water) was chosen as it provided the best spectral intensity without removing potentially important biomarkers. An optimal acquisition time of 15 seconds per sample was determined. Samples of cow, goat, camel, oat, and soy milk were collected from various sources to account for within-group variations. Unsupervised principal component analysis (PCA) and supervised linear discriminant analysis (LDA) were used for data analysis. To assess the ability to detect cow milk adulteration, samples with varying concentrations of cow milk mixed with other milk types were created. Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) was used to identify and quantify biomarkers for each milk type. The LipidMaps database was utilized to identify lipid biomarkers. MS/MS data was used to confirm the chemical structures of the identified biomarkers. LDA models were built to assess the sensitivity of the method in detecting adulteration at different levels.

PCA clearly separated the five milk types, although cow and goat milk showed some overlap. LDA improved separation between all milk types. The analysis of cow milk and goat milk revealed spectra dominated by fatty acids, glycerophospholipids (GP), and sphingolipids (SP), with distinct differences between the two. Camel, oat, and soy milk showed distinct differences from cow milk in terms of glycerolipid (GL) and other lipid groups. OPLS-DA further sharpened the separation between cow milk and other milk types, even with goat milk, which was previously less distinct in PCA. The OPLS-DA model showed high predictive power (R2Y = 0.965, Q2Y = 0.964). The study identified 28 robust candidate markers that differentiated between different milk species, with glycerophospholipids (GP) and sphingolipids (SP) more abundant in cow milk and glycerolipids in the other milk types. The LDA model showed excellent accuracy in detecting cow milk adulteration in various milk types. The detection limit for cow milk adulteration ranged from 0.1% (soy milk) to 5% (goat milk).

This study successfully demonstrated, for the first time, the use of DESI-MS for rapid lipidomic profiling of milk samples to identify species and detect adulteration. The method offers several advantages over existing techniques, including speed, simplicity, and reduced need for sample preparation. The high accuracy and sensitivity of the method make it suitable for routine quality control and fraud detection. The identified lipid biomarkers provide specific markers for different milk types. The successful detection of cow milk adulteration at low concentrations demonstrates the method's potential for protecting consumers from potentially harmful or fraudulent products. The results emphasize the potential of DESI-MS as a valuable tool for ensuring food safety and authenticity.

The developed DESI-MS method provides a rapid, accurate, and environmentally friendly approach for identifying milk from different animal and plant sources and detecting adulteration. The identification of specific lipid biomarkers allows for accurate classification and quantification of cow milk adulteration in various milk types. The method's high sensitivity, coupled with its simplicity, makes it a valuable tool for food safety and quality control. Future research could focus on validating the method with a larger dataset and exploring its applicability to other dairy products.

The study's sample size, while substantial, could be further expanded to enhance the generalizability of the findings. The study primarily focused on specific milk types and adulteration scenarios, and future work could extend the analysis to other milk types and adulterants. Further investigation into the influence of processing methods on the lipid profiles would also be beneficial.