Machine Learning-Assisted Nanosensing of Ketone Bodies in Dairy Cows

Monitoring ketone bodies, especially beta-hydroxybutyrate (βHB), is crucial for enhancing the health, productivity, and welfare of dairy cows. Elevated ketone levels can signal metabolic issues, such as subclinical or clinical ketosis, potentially leading to reduced milk yield, lower feed efficiency, and significant economic losses. In this study, we developed a graphene-based nanosensor assisted by machine learning to detect βHB concentrations in dairy cows. The sensing data from this innovative device were utilized to generate various machine-learning regression models, improving accuracy in identifying unknown βHB levels. This nanosensor, in conjunction with machine learning, offers a novel approach to βHB detection, resulting in improved accuracy for diagnosing diseases in dairy cattle compared to traditional methods.