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

ABSTRACT 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.