Abstract
A simple and fast method for the detection and quantification of milk adulteration was developed using portable and hand-held infrared (IR) spectrometers. Milk samples were purchased from local supermarkets (Columbus, OH, USA) and spiked with tap water, whey, hydrogen peroxide, synthetic urine, urea, and synthetic milk in different concentrations. Spectral data were collected using mid-infrared (MIR) and near-infrared (NIR) spectrometers. Soft independent modeling of class analogy (SIMCA) classification models exhibited tight and well-separated clusters allowing the discrimination of control from adulterated milk samples. Partial least-squares regression (PLSR) was used to estimate adulteration levels, and results showed high coefficients of determination (R(2)) and low standard errors of prediction (SEP). Classification and quantification models indicated that the tested MIR systems were superior to NIR systems in monitoring milk adulteration. This method can be potentially used as an alternative to traditional methods due to their simplicity, sensitivity, low energy cost, and portability.
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