In this work, the polar metabolite pool of commercial caprine milk was studied by gas chromatography-mass spectrometry and multivariate statistical data analysis. Experimental data were compared with those of cow milk and the discriminant analysis correctly classified milk. By the same means, differences due to heat treatments (UHT or pasteurization) on milk samples were also investigated. Results of the 2 discriminant analyses were combined, with the aim of finding the discriminant metabolites unique for each class and shared by 2 classes. Valine and glycine were peculiar to goat milk, talose and malic acid to cow milk, and hydroxyglutaric acid to pasteurized samples. Glucose and fructose were shared by cow milk and UHT-treated samples, whereas ribose was shared by pasteurized and goat milk. Other discriminant variables were not attributed to specific metabolites. Furthermore, with the aim to reduce food fraud, the issue of adulteration of caprine milk by addition of cheaper bovine milk has been also addressed. To this goal, mixtures of goat and cow milk were prepared by adding the latter in a range from 0 to 100% (vol/vol) and studied by multivariate regression analysis. The error in the level of cow milk detectable was approximately 5%. These overall results demonstrated that, through the combined approach of gas chromatography-mass spectrometry and multivariate statistical data analysis, we were able to discriminate between milk typologies on the basis of their polar metabolite profiles and to propose a new analytical method to easily discover food fraud and to protect goat milk uniqueness. The use of appropriate visualization tools improved the interpretation of multivariate model results.