Abstract

In the present work, an analytical approach for the voltammetric detection and prediction of adulteration of fresh cow milk with reconstituted skim milk powder is developed. After precipitation of milk proteins upon addition of ethanol and centrifugation, the supernatant liquid of the samples was analyzed by cyclic voltammetry on a novel graphite/SiO2 hybrid working electrode (GSiHE) using LiClO4 as electrolyte. Under these conditions, fresh milk samples gave broadened peaks/plateaus in both forward and backward potential scanning, attributed mainly to oxidases. Such peaks were not evident in the case of reconstituted skim milk powder samples due to inactivation of enzymes and breakdown of certain antioxidants caused by heat and pressure-treatments. The differences between fresh and reconstituted skim milk powder samples in their voltammetric profile were exploited for the detection of fresh milk adulteration by submitting voltammetric data to chemometrics. As datapoints, the differences between forward and backward current values, recorded at the same potentials, were determined and submitted to multivariate analysis. Principal Component Analysis (PCA) provided a clear differentiation between fresh milk and reconstituted skim milk powder samples. Soft independent modeling of class analogy (SIMCA) was employed to model the class of fresh milks, using samples from 12 commercially available fresh milk brands. Prediction of fresh milk adulteration with reconstituted skim milk powders was achieved by means of Partial Least Squares (PLS) regression analysis. Detection limit of the technique was found to be below 6% (v/v) and the linearity of model in terms of observed/predicted values was confirmed up to 100% (v/v). Validation and applicability of both SIMCA and PLS models were confirmed using a suitable test set, consisting of commercial fresh milk and skim milk powder samples as well as synthetic adulterated fresh milk samples.

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