Label-free bio-aerosol detection and classification using a virtual impactor and deep learning

Yi Luo; Yijie Zhang; Tairan Liu; Alan Yu; Yichen Wu; Aydogan Ozcan

doi:10.1117/12.2650786

6 March 2023 Label-free bio-aerosol detection and classification using a virtual impactor and deep learning

Yi Luo, Yijie Zhang, Tairan Liu, Alan Yu, Yichen Wu, Aydogan Ozcan

Proceedings Volume PC12369, Optics and Biophotonics in Low-Resource Settings IX; PC123690E (2023) https://doi.org/10.1117/12.2650786
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

We present a computational mobile imaging device that captures holograms of aerosols through a virtual impactor, a flow-based device designed to detect aerosols. A differential detection scheme localizes all the flowing particles in air, and their auto-focused holograms are used to classify them using a trained neural network without any labels/stains. To test this cost-effective mobile device, we aerosolized different types of pollen (Bermuda, Elm, Oak, Pine, Sycamore, and Wheat) and achieved a blind testing classification accuracy of 92.91%. This cost-effective mobile system can be used as a long-term air quality monitor to automatically count/sense particulate matter and various allergens.

Conference Presentation

Citation Download Citation

Yi Luo, Yijie Zhang, Tairan Liu, Alan Yu, Yichen Wu, and Aydogan Ozcan "Label-free bio-aerosol detection and classification using a virtual impactor and deep learning", Proc. SPIE PC12369, Optics and Biophotonics in Low-Resource Settings IX, PC123690E (6 March 2023); https://doi.org/10.1117/12.2650786

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