Kachouei, Matin AtaeiChick, ShannonAli, Md. Azahar2026-01-222026-01-222025979-8-3315-1272-91944-9399https://hdl.handle.net/10919/140933Early detection of metabolic diseases, including lactic acidosis, is crucial for effective livestock health management. This study presents the development of a nanosensor platform using graphene nanosheets and lactate oxidase (LOx) enzyme to detect lactate and hydrogen peroxide (H<inf>2</inf>O<inf>2</inf>) concentrations within a minute. Machine learning (ML) techniques, including polynomial regression and random forest (RF) regression, were used to optimize sensor calibration. Polynomial regression (degrees 3 and 4) achieved perfect accuracy (r²=1.00), while RF regression demonstrated strong predictive performance (r²=0.857). These results underscore the lactate sensor's potential for precise, reliable detection in complex biological fluids, providing an advantage over traditional methods in dairy cattle health monitoring.Pages 292-2954 page(s)application/pdfenIn CopyrightConference proceedinghttps://doi.org/10.1109/NANO63165.2025.11113714Ali, Azahar [0000-0001-5752-8808]1944-9380