Abstract: Real-time detection, classification, and identification of aerosol particles are crucial in various industries and public health areas. To overcome the limitations of existing particle analysis methods, we investigated three categories of industrial-oriented techniques for both statistical monitoring and fingerprint detection. The first technique is based on optical scattering, which correlates particle information with scattered intensity. By employing polarization characterization and multi-angle measurement, we have sufficiently classified different particle types at the single-species level. Additionally, to achieve high-throughput particle characterization, we developed imaging platforms for particle detection. Utilizing polarization imaging and deep learning algorithms, we achieved a classification accuracy of ~95%. Finally, we demonstrated a compact digital in-line holographic microscopy platform with an inertial spectrometer for simultaneous measurement of two independent fingerprint parameters at the single-species level. Specifically, by interrogating the particle location and size captured with the platform, particle mass density can be estimated. Furthermore, by employing Monte Carlo fitting to the Lorenz-Mie theory, the refractive index of each particle can be extracted from the interference patterns. The combination of mass density and optical density characterization unambiguously enhances the discriminatory power of the system, especially when dealing with particles that exhibit similar mass densities but distinctive refractive indices or vice versa. [] Co-sponsored by: Co-sponsored by National Research Council, Canada. Optonique. ETS Optica Student Chapter. Speaker(s): Jingwen Li, Agenda: – – Introduction from the host (2 to 5 minutes) – Presentation (40 to 45 minutes) – Questions from the audience (5 to 10 minutes) – Lunch and networking Room: Room 1302, Bldg: Building A, 1100 Notre-Dame St W, Montréal, Quebec, Canada, H3C 1K3