Non-invasive Characterization of Glycosuria and Identification of Biomarkers in Diabetic Urine Using Fluorescence Spectroscopy and Machine Learning Algorithm.

The current study presents a steadfast, simple, and efficient approach for the non-invasive determination of glycosuria of diabetes mellitus using fluorescence spectroscopy. A Xenon arc lamp emitting light in the range of 200-950 nm was used as an excitation source for recording the fluorescent spectra from the urine samples. A consistent fluorescence emission peak of glucose at 450 nm was found in all samples for an excitation wavelength of 370 nm. For confirmation and comparison, the fluorescence spectra of non-diabetic (healthy controls) were also acquired in the same spectral range. It was found that fluorescence emission intensity at 450 nm increases with increasing glucose concentration in urine. In addition, optimized synchronous fluorescence emission at 357 nm was used for simultaneously determining a potential diabetes biomarker, Tryptophan (Trp) in urine. It was also found that the level of tryptophan decreases with the increase in urinary glucose concentration. The quantitative estimation of urinary glucose can be demonstrated based on the intensity of emission light carried by fluorescence light. Moreover, the dissimilarities were further emphasized using the hierarchical cluster analysis (HCA) algorithm. HCA gives an obvious separation in terms of dendrogram between the two data sets based on characteristic peaks acquired from their fluorescence emission signatures. These results recommend that urinary glucose and tryptophan fluorescence emission can be used as potential biomarkers for the non-invasive analysis of diabetes.