Real-time detection, classification and identification of aerosol particles is crucial in various industries and public health areas. In order to circumvent the limitations of existing particle analysis methods for efficient discrimination, we demonstrate a compact digital in-line holographic microscopy platform with an inertial spectrometer for simultaneous measurement of two independent fingerprint parameters at single species level. In particular, 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 also be extracted from the interference patterns. It is demonstrated that 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. This innovative approach represents a significant advancement in particle characterization and composition identification, with potential applications in various industrial, scientific, and research domains. An iOS-based app interface is then customized for wireless controlling of the CMOS imager, image acquisition, reconstruction, and data analysis. The imaging platform proposed in this work has prominent advantages including compactness, accuracy, efficiency, high throughput, and remote sensing capability, which is especially relevant for applications where on-site/remote metrology and identification of particles is required.
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