| Autor: |
Saatkamp CJ; Instituto Esperança de Ensino Superior (IESPES), Rua Coaracy Nunes, 3315, Santarém, Pará 68040-100, Brazil., de Almeida ML; Instituto Esperança de Ensino Superior (IESPES), Rua Coaracy Nunes, 3315, Santarém, Pará 68040-100, Brazil., Bispo JA; Faculdades Integradas do Tapajós-FIT, Rua Rosa Vermelha, No. 335, Aeroporto Velho, Santarém, Pará 68010-200, Brazil., Pinheiro AL; Universidade Camilo Castelo Branco-UNICASTELO, Biomedical Engineering Institute, Parque Tecnológico de São José dos Campos, Estrada Dr. Altino Bondesan, 500, São José dos Campos, São Paulo 12247-016, BrazildFederal University of Bahia-UFBA, Center of Biop., Fernandes AB; Universidade Camilo Castelo Branco-UNICASTELO, Biomedical Engineering Institute, Parque Tecnológico de São José dos Campos, Estrada Dr. Altino Bondesan, 500, São José dos Campos, São Paulo 12247-016, Brazil., Silveira L Jr; Universidade Camilo Castelo Branco-UNICASTELO, Biomedical Engineering Institute, Parque Tecnológico de São José dos Campos, Estrada Dr. Altino Bondesan, 500, São José dos Campos, São Paulo 12247-016, Brazil. |
| Abstrakt: |
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 25.2 mg/dL, respectively. RS may become a technique for rapid urinalysis, with concentration errors suitable for population screening aimed at the prevention of renal diseases. |
