Due to their importance in the regulation of metabolites, the kidneys need continuous monitoring to check for correct functioning, mainly by urea and creatinine urinalysis. This study aimed to develop a model to estimate the concentrations of urea and creatinine in urine by means of Raman spectroscopy (RS) that could be used to diagnose kidney disease. Midstream urine samples were obtained from 54 volunteers with no kidney complaints. Samples were subjected to a standard colorimetric assay of urea and creatinine and submitted to spectroscopic analysis by means of a dispersive Raman spectrometer (830 nm, 350 mW, 30 s). The Raman spectra of urine showed peaks related mainly to urea and creatinine. Partial least squares models were developed using selected Raman bands related to urea and creatinine and the biochemical concentrations in urine measured by the colorimetric method, resulting in $r=0.90$ and 0.91 for urea and creatinine, respectively, with root mean square error of cross-validation (RMSEcv) of 312 and $25.2mg/dL$, respectively. RS may become a technique for rapid urinalysis, with concentration errors suitable for population screening aimed at the prevention of renal diseases.

Patients with diabetes mellitus and hypertension (HT) diseases are predisposed to kidney diseases. The objective of this study was to identify potential biomarkers in the urine of diabetic and hypertensive patients through Raman spectroscopy in order to predict the evolution to complications and kidney failure. Urine samples were collected from control subjects (CTR) and patients with diabetes and HT with no complications (lower risk, LR), high degree of complications (higher risk, HR), and doing blood dialysis (DI). Urine samples were stored frozen ($−20°C$) before spectral analysis. Raman spectra were obtained using a dispersive spectrometer (830-nm, 300-mW power, and 20-s accumulation). Spectra were then submitted to principal component analysis (PCA) followed by discriminant analysis. The first PCA loading vectors revealed spectral features of urea, creatinine, and glucose. It has been found that the amounts of urea and creatinine decreased as disease evoluted from CTR to LR/HR and DI (PC1, $p<0.05$), and the amount of glucose increased in the urine of LR/HR compared to CTR (PC3, $p<0.05$). The discriminating model showed better overall classification rate of 70%. These results could lead to diagnostic information of possible complications and a better disease prognosis.

Fourier-transform (FT)-Raman spectroscopy has been used for identification and evaluation of human artherosclerotic lesions, providing biochemical information on arteries. In this work, fragments of human carotid arteries postmortem were analyzed using a FT-Raman spectrometer operating at an excitation wavelength of 1064 nm, power of 200 mW, and spectral resolution of $4 cm−1.$ A total of 75 carotid fragments were spectroscopically scanned and FT-Raman results were compared with histopathology. Discriminant analysis using Mahalanobis distance was applied over principal components scores for tissue classification into three categories: nonatherosclerotic, atherosclerotic plaque without calcification and with calcification. Nonatherosclerotic artery, atherosclerotic plaque, and calcified plaque exhibit spectral signatures related to biochemicals presented in each tissue type, such as bands of collagen and elastin (proteins), cholesterol and its esters, and calcium hydroxyapatite and carbonate apatite, respectively. Spectra of nonatherosclerotic artery were then classified into two groups: normal and discrete diffuse thickening of the intima layer (first group) and moderate and intense diffuse thickening of the intima layer (second group). FT-Raman could identify and classify the tissues found in the atherosclerotic process in human carotid in vitro and had the ability to identify alterations to the diffuse thickening of the intima layer and classify it depending on the intensity of the thickening. © 2005 Society of Photo-Optical Instrumentation Engineers.