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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.

Emission signal normalization in LIBS for the direct analysis of metal samples aiming at the determination of 10 analytes (Al, Cr, Cu, Fe, Mn, Mo, Ni, Ti, V and Zn).

a b s t r a c tA functionalized carbon nanotubes paste electrode modified with cross-linked chitosan for the determination of trace amounts of cadmium(II) and mercury(II) by linear anodic stripping voltammetry is described. Under optimal experimental conditions, the peak current was linear in the Cd(II) concentration range from 5.9 Â 10 À8 to 1.5 Â 10 À6 mol L À1 with a detection limit of 9.8 Â 10 À9 mol L À1 and, for Hg(II) from 6.7 Â 10 À9 to 8.3 Â 10 À8 mol L À1 with a detection limit of 2.4 Â 10 À9 mol L À1 . The proposed method was successfully applied for the determination of Hg(II) in natural and industrial wastewater samples, and Cd(II) in sediments, human urine, natural, and industrial wastewater samples.

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