AI-Powered Nanosensing of Lactate in Dairy Cows

Abstract

Early 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 (H2O2) 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.