MALDI-MS and multivariate analysis for the detection and quantification of different milk species

The extensive consumption of milk and dairy products makes these foodstuffs targets for potential adulteration with financial gains for unscrupulous producers. Such practices must be detected as these can impact negatively on product quality, labelling and even health. Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-ToF-MS) is a potentially useful technique, with proven abilities in protein identification and more recently through the use of internal standards for quantification purposes of specific proteins or peptides. In the current work, we therefore aim to explore the accuracy and attributes of MALDI-ToF-MS with chemometrics for the detection and quantification of milk adulteration. Three binary mixtures containing cows' and goats', cows' and sheep's, and goats' and sheep's milk and a fourth tertiary mixture containing all types of milk were prepared and analysed directly using MALDI-ToF-MS. In these mixtures, the milk concentrations of each milk varied from 0% to 100% in 5% steps. Multivariate statistical methods including partial least squares (PLS) regression and non-linear Kernel PLS regression were employed for multivariate calibration and final interpretation of the results. The results for PLS and KPLS were encouraging with between 2% and 13% root mean squared error of prediction on independent data; KPLS slightly outperformed PLS. We believe that these results show that MALDI-ToF-MS has excellent potential for future use in the dairy industry as a rapid method of detection and enumeration in milk adulteration.