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Spheres, tubes, and planar-shaped nanomaterials as Fe3O4 nanoparticles (MNPs), multiwalled carbon nanotubes (MWCNT), and graphene oxide (GO) were used for the first time to treat microfluidic paper-based analytical devices (μPADs) and create a biocompatible layer with high catalytic surface. Once glucose measurements are critical for diabetes or glycosuria detection and monitoring, the analytical performance of the proposed devices was studied by using bienzymatic colorimetric detection of this carbohydrate. The limit of detection values achieved for glucose with μPADs treated with MNPs, MWCNT, and GO were 43, 62, and 18 μM, respectively. The paper surface modification solves problems associated with the lack of homogeneity on color measurements that compromise the sensitivity and detectability levels in clinical diagnosis.

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Nitrogen compounds like urea and melamine are known to be commonly used for milk adulteration resulting in undesired intoxication; a well-known example is the Chinese episode occurred in 2008. The development of a rapid, reliable and economic test is of relevance in order to improve adulterated milk identification. Cyclic voltammetry studies using an Au working electrode were performed on adulterated and non-adulterated milk samples from different independent manufacturers. Voltammetric data and their first derivative were subjected to functional principal component analysis (f-PCA) and correctly classified by the KNN classifier. The adulterated and non-adulterated milk samples showed significant differences. Best results of prediction were obtained with first derivative data. Detection limits in milk samples adulterated with 1% of its total nitrogen derived from melamine or urea were as low as 85.0 mg·L−1 and 121.4 mg·L−1, respectively. We present this method as a fast and robust screening method for milk adulteration analysis and prevention of food intoxication.

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