DSC coupled with PCA as a tool for butter authenticity assessment

Abstract

The authenticity assessment of butter is still a big challenge for analytics. Due to its low cost, palm oil (PO) is the edible fat most frequently used as an adulterant in the production of butter. The aim of this study was to test the feasibility of the DSC technique coupled with principal component analysis (PCA) for the distinction of pure butterfat from adulterated. A differential scanning calorimeter DSC 7 from Perkin-Elmer was used for the examination of crystallization and melting properties of pure butterfat and its mixtures with PO (2, 5, 10, 15, 20, 25, 30, 35 % w/w). The addition of PO affects the thermodynamic parameters of the crystallization and melting of butter, which modifies the position and area of phase transition peaks. A large dataset concerning DSC cooling and heating parameters such as temperatures, enthalpies and peak heights was obtained. Processing of data was supported by the PCA method, which proved its applicability for differentiation between genuine and adulterated fat. Statistical analysis revealed that the best separation of samples with different palm oil concentration was achieved using DSC melting parameters of enthalpies and peak heights of low and medium melting fractions. PCA of crystallization parameters did not give such distinct separation. The results of this study confirmed that the DSC technique coupled with PCA can be successfully applied to detect with high sensitivity (from 2 %) the adulterant in butter.