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Sample-to-answer on-farm raw milk quality evaluation via a portable chip-based high-accuracy somatic cell counting
Xiaoyun Sun1, Xingyi Bao1, Junting Zhang1
1State Key Laboratory for Quality and Safety of Agro-Products, Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Abstract:
Milk somatic cell count (SCC) is a key indicator linking bovine udder health assessment and raw milk quality control, serving not only as a quantitative basis for mastitis risk screening but also as an important parameter in raw milk procurement and quality grading. Currently, SCC testing in dairy enterprises and testing centers is commonly performed using flow cytometry. However, owing to the high instrument cost, maintenance requirements, and operational complexity, dairy farms often lack a rapid and reliable prescreening tool that can provide results comparable to centralized testing before raw milk delivery. To meet the urgent need for rapid and reliable on-farm SCC prescreening, this study developed a low-cost sample-to-answer portable chip-based SCC detection system. The system employs a disposable reagent-preloaded chip as the raw milk sample-processing unit for on-chip cell staining and integrates automated fluorescence imaging, multi-field image acquisition, and automatic SCC output. Through a standardized detection workflow and an area-assisted counting strategy, the system enabled high-accuracy somatic cell counting across an SCC range of 19,000-4,000,000 cells mL-1 in real-sample validation, with results highly consistent with those obtained using the Bentley FTS/FCM flow cytometer and an overall repeated-measurement CV of 2.86%. Furthermore, the complete detection could be finished within 5 min without requiring additional reagents or external devices, and the estimated per-test consumable cost was below US$0.50. By enabling SCC testing before raw milk delivery, this system advances milk quality control from centralized post-delivery analysis toward on-farm prescreening, supporting timely mastitis risk identification and proactive raw milk quality management.

