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In this paper, we describe a validated paper-based microfluidic analytical device for the simultaneous quantification of two important biomarkers of renal function in urine. This paper platform provides an inexpensive, simple, and easy to use colorimetric method for the quantification of creatinine (CRN) and uric acid (UA) in urine samples. The microfluidic paper-based analytical device (μPAD) consists of a main channel with three identical arms, each containing a circular testing zone and a circular uptake zone. Creatinine detection is based on the Jaffé reaction, in which CRN reacts with picrate to form an orange-red product. Uric acid quantification is based on the reduction of Fe to Fe by UA, which is detected in a colorimetric reaction using 1,10-phenanthroline. Under optimum conditions, obtained through chemometrics, the concentrations of the analytes showed good linear correlations with the effective intensities, and the method presented satisfactory repeatability. The limits of detection and the linear ranges, respectively, were 15.7 mg L and 50-600 mg L for CRN and 16.5 mg L and 50-500 mg L for UA. There were no statistically significant differences between the results obtained using the μPAD and a chromatographic comparative method (Student's t-test at 95% confidence level).

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Portable spectrometers are promising tools that can be an alternative way, for various purposes, of analyzing food quality, such as monitoring in a few seconds the internal quality during fruit ripening in the field. A portable/handheld (palm-sized) near-infrared (NIR) spectrometer (Neospectra, Si-ware) with spectral range of 1295–2611 nm, equipped with a micro-electro-mechanical system (MEMs), was used to develop prediction models to evaluate tomato quality attributes non-destructively. Soluble solid content (SSC), fructose, glucose, titratable acidity (TA), ascorbic, and citric acid contents of different types of fresh tomatoes were analyzed with standard methods, and those values were correlated to spectral data by partial least squares regression (PLSR). Fresh tomato samples were obtained in 2018 and 2019 crops in commercial production, and four fruit types were evaluated: Roma, round, grape, and cherry tomatoes. The large variation in tomato types and having the fruits from distinct years resulted in a wide range in quality parameters enabling robust PLSR models. Results showed accurate prediction and good correlation (Rpred) for SSC = 0.87, glucose = 0.83, fructose = 0.87, ascorbic acid = 0.81, and citric acid = 0.86. Our results support the assertion that a handheld NIR spectrometer has a high potential to simultaneously determine several quality attributes of different types of tomatoes in a practical and fast way.