This work employed NIR spectroscopy and PLS algorithms for the identification and quantification of goat milk adulteration by adding cow milk, besides the determination of their fat and protein contents. Since cow milk can represent a health risk to allergic consumers regardless its amount, PLS-DA was able to identify cow milk additions in goat milk as low as 1.0154 g/100g, likewise the non-adulterated goat and cow milk samples, achieving a 100% of correct classification. For quantification purposes, the Successive Projections Algorithm for interval selection in PLS (iSPA-PLS) provided the best results for the determination of both adulteration and fat contents, while PLS gave better results for the protein quantification. Despite the great similarity of both natural dairy matrices and their intrinsic variability, the prediction results provided suitable values with high correlation coefficients and low RMSEP and REP values, with RPD values higher than 3. Therefore, the proposed methodology proved to be a useful, fast and non-destructive tool for screening the quality of goat milk in terms of its adulteration with cow milk, in addition to the quantification of its fat and protein contents. •Identification and quantification of goat milk adulteration by adding cow milk.•Fat and protein content quantifications in goat and cow milk and their mixtures.•NIR spectroscopy coupled with different preprocessing techniques and PLS algorithms.•PLS-DA classified correctly non-adulterated goat and cow milk, and their mixtures.•Good quantifications for adulteration and fat by iSPA-PLS and for protein by PLS.