Ion-Gated 3D-Printed Sensor for In Situ, Teat-Level Detection of Subclinical Mastitis

Abstract Subclinical mastitis (SCM) is a widespread udder infection that lacks visible symptoms, leading to undiagnosed cases that compromise milk quality and yield. Current detection methods, such as post-milking somatic cell counts (SCC), are delayed and fail to capture early, quarter-level infections. To address this, we present TeatSense, a miniaturized, solid-state sensing platform for real-time, in situ milk pH monitoring during active milking. The system integrates a 3D-printed micropatterned µ-pinstripe electrode with a poly(3-octylthiophene) ion-to-electron transducing layer, dual hydrogen ion-selective membranes, and a biofouling-resistant ion-access membrane (IAM). This multi-layered architecture enables stable, selective pH detection in raw milk with a resolution better than 0.05 pH units, despite interference from fats, proteins, and somatic cells. TeatSense is housed within a PDMS microfluidic module and equipped with a peristaltic pump, compact electronics, and wireless data transmission. Integrated machine learning algorithms (e.g., feedforward neural networks) enable accurate health classification and artifact correction. Validated with 140 milk samples from Holstein cows, including quarter- and composite-milk, TeatSense effectively detected mastitis-related pH shifts. Unlike conventional lab-based sensors, TeatSense is customizable, field-deployable, and compatible with automated milking systems, offering a scalable, real-time solution for early mastitis detection and improved dairy herd health.