The detection of goat milk adulteration with cow milk using a combination of voltammetric fingerprints and chemometrics analysis

Abstract

In this study, a novel analytical approach was developed for detecting and predicting adulteration of goat milk with cow milk using a combination of voltammetric fingerprints and chemometrics analysis. The fresh milk samples were obtained from local farmers and analyzed using cyclic voltammetry technique using a glassy carbon electrode as the working electrode and KClO4 as the supporting electrolyte. The voltammetric fingerprint was obtained from both milk samples and showed an anodic peak between a potential range of 0.40–0.75 V versus Ag/AgCl. This anodic peak is mainly attributed to several electroactive species contained in both milk samples. The current intensities at the potential range of 0 to + 1 V versus Ag/AgCl were further selected due to the majority of electroactive components in the milk samples having their oxidation potential in this potential range. The current intensities were further pre-treated using maximum normalization and submitted to the chemometric tools for multivariate analysis. Orthogonal partial least square-discriminant analysis provided clear discrimination between goat and cow milk. Meanwhile, the prediction of goat milk adulteration with cow milk was achieved using partial least squares regression analysis. This multivariate analysis enabled a satisfactory discrimination and successful model to predict the percentage of cow milk as adulterants in goat milk samples. The demonstrated results revealed that a combination of voltammetric fingerprints and chemometrics tools might offer a low-cost, simple, and rapid analysis which might be possible as a promising method to be developed further for the detection of adulterants.