Minimum Minutes Machine-Learning Microfluidic Microbe Monitoring Method (M7)

Abstract

Frequent outbreaks of viral diseases have brought substantial negative impacts on society and the economy, and they are very difficult to detect, as the concentration of viral aerosols in the air is low and the composition is complex. The traditional detection method is manually collection and re-detection, being cumbersome and time-consuming. Here we propose a virus aerosol detection method based on microfluidic inertial separation and spectroscopic analysis technology to rapidly and accurately detect aerosol particles in the air. The microfluidic chip is designed based on the principles of inertial separation and laminar flow characteristics, resulting in an average separation efficiency of 95.99% for 2 μm particles. We build a microfluidic chip composite spectrometer detection platform to capture the spectral information on aerosol particles dynamically. By employing machine-learning techniques, we can accurately classify different types of aerosol particles. The entire experiment took less than 30 min as compared with hours by PCR detection. Furthermore, our model achieves an accuracy of 97.87% in identifying virus aerosols, which is comparable to the results obtained from PCR detection.