FAROOQ, SAJIDPERES, DANIELLA L.CAIXETA, DOUGLAS C.LIMA, CASSIOSILVA, ROBINSON S. daZEZELL, DENISE M.2024-02-082024-02-08FAROOQ, SAJID; PERES, DANIELLA L.; CAIXETA, DOUGLAS C.; LIMA, CASSIO; SILVA, ROBINSON S. da; ZEZELL, DENISE M. Monitoring changes in urine from diabetic rats using ATR-FTIR and Machine Learning. In: INTERNATIONAL CONFERENCE ON OPTICAL MEMS AND NANOPHOTONICS; SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE, July 31 - August 3, 2023, Campinas, SP. <b>Proceedings...</b> Piscataway, NJ, USA: IEEE, 2023. DOI: <a href="https://dx.doi.org/10.1109/OMN/SBFOTONIOPC58971.2023.10230957">10.1109/OMN/SBFOTONIOPC58971.2023.10230957</a>. Disponível em: http://repositorio.ipen.br/handle/123456789/34579.http://repositorio.ipen.br/handle/123456789/34579Here, we aim to better characterize diabetes mellitus (DM) by analyzing 149 urine spectral samples, comprising of diabetes versus healthy control groups employing ATR-FTIR spectroscopy, combined with a 3D discriminant analysis machine learning approach. Our results depict that the model is highly precise with accuracy close to 100%.openAccessdiabetes mellitusmonitoringratsmachine learningfourier transformationinfrared spectrafourier transform spectrometersTexto completo de evento10.1109/OMN/SBFOTONIOPC58971.2023.102309570000-0001-7404-9606https://orcid.org/0000-0001-7404-9606