Machine-Learning Microfluidic Minute-Scale Microorganism Metrics Monitoring(M6)

On-site monitoring of microorganisms remains challenging because of low concentrations, strong background interference, and dynamic aerosol diffusion, particularly for aerosol-transmitted pathogens. Here, we report a rapid detection platform that integrates a Puri-focusing microfluidic chip, electrochemical impedance spectroscopy (EIS), and machine learning for the analysis of airborne microorganisms. Guided by fluid-dynamic design and laminar-flow focusing, the chip achieved a 95.8% separation efficiency for 5 µm target particles. African swine fever virus (ASFV) was used as a model pathogen. Impedance features, including modulus, real and imaginary components, and phase angle, were extracted from aerosol samples and analyzed using multiple machine learning classifiers. Five-fold cross-validation identified Random Forest (RF) as the optimal model, achieving 95.2% classification accuracy. The platform reached a system-level detection limit of 188 TCID50/mL for air-sampled aerosols and showed high concordance with enzyme-linked immunosorbent assay (ELISA) results. Each detection cycle required less than 1 minute. This integrated strategy offers a feasible route for rapid on-site monitoring of aerosol-transmitted microorganisms in public health, agriculture, livestock farming, and production safety.