Desenvolvimento de método colorimétrico utilizando imagens digitais capturadas por smartphones para a quantificação de ureia no suor

Abstract

Digital image colorimetry (DIC) is an analytical tool based in the correlation, linear or non-linear, between the chromatic composition of images obtained through image capturing devices, such as smartphones and scanners, and the concentration of the desired analytes. For that, a color space, mathematic models that use matrices and equations to describe colors, like the CMYK (Cyan, Magenta, Yellow, Key) and the RGB (Red, Green, Blue), the latter being the most applied due to its simplicity and wide range of use, must be chosen. As this is still an emerging research field, lots of studies are conducted to evaluate the effectiveness of the method, with an emphasis on the ones directed to the healthcare sector. The quantification of biomarkers using DIC have great potential, as it can contribute to the diagnose, monitoring and treatment of severe diseases, like glucose for diabetes, and creatinine and urea for the Chronic Kidney Disease (CKD), object of study of this paper. The CKD is a disease caused by an injury on the kidney’s tissue, and characterized by the decrease of the glomerular filtration (GF). In this paper the use of digital images using smartphones as a tool to quantify urea in sweat, aiming to offer a low cost, simple, fast and efficient, analysis methodology to diagnose and monitor the CKD. Interfering conditions, such as luminosity, calibration curves and reproducibility were optimized, and analysis parameters were successfully established. Using a calibration gradient, developed with the measures of a device with higher resolution, the method was applicable to devices with inferior configurations. The applicability of the method to real diagnose scenarios had its qualitative and semiquantitative character validated through the analysis of synthetic sweat solutions with different concentrations of urea, using the slope method.