Monitoring changes in urine from diabetic rats using ATR-FTIR and Machine Learning

Author(s)

Farooq, Sajid ; Peres, Daniella L. Prime Umara ; Caixeta, Douglas Carvalho ; Lima, Cassio ; da Silva, Robinson Sabino ; Zezell, Denise Maria ; IEEE

• Document type: Journal article
• Source: 2023 INTERNATIONAL CONFERENCE ON OPTICAL MEMS AND NANOPHOTONICS, OMN AND SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE, SBFOTON IOPC; v. N/A, p. 2-pg., 2023-01-01.

Abstract

Here, 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%. (AU)

• FAPESP's process: 21/00633-0 - Hyperspectral signal processing and analysis applied to histopathological diagnosis
• Grantee: Luciano Bachmann
• Support Opportunities: Regular Research Grants
• FAPESP's process: 17/50332-0 - Scientific, technological and infrastructure qualification in radiopharmaceuticals, radiation and entrepreneurship for health purposes (PDIp)
• Grantee: Marcelo Linardi
• Support Opportunities: Research Grants - State Research Institutes Modernization Program