Virtual impactor-based label-free bio-aerosol detection using holography and deep learning

• URL: http://arxiv.org/abs/2208.13979v1
• Date: Tue, 30 Aug 2022 04:18:18 GMT
• Title: Virtual impactor-based label-free bio-aerosol detection using holography and deep learning
• Authors: Yi Luo, Yijie Zhang, Tairan Liu, Alan Yu, Yichen Wu, Aydogan Ozcan
• Abstract summary: Exposure to bio-aerosols such as mold spores and pollen can lead to adverse health effects. We present a label-free bio-aerosol sensor that takes holographic images of flowing particulate matter concentrated by a virtual impactor.
• Score: 12.638229037752586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Exposure to bio-aerosols such as mold spores and pollen can lead to adverse health effects. There is a need for a portable and cost-effective device for long-term monitoring and quantification of various bio-aerosols. To address this need, we present a mobile and cost-effective label-free bio-aerosol sensor that takes holographic images of flowing particulate matter concentrated by a virtual impactor, which selectively slows down and guides particles larger than ~6 microns to fly through an imaging window. The flowing particles are illuminated by a pulsed laser diode, casting their inline holograms on a CMOS image sensor in a lens-free mobile imaging device. The illumination contains three short pulses with a negligible shift of the flowing particle within one pulse, and triplicate holograms of the same particle are recorded at a single frame before it exits the imaging field-of-view, revealing different perspectives of each particle. The particles within the virtual impactor are localized through a differential detection scheme, and a deep neural network classifies the aerosol type in a label-free manner, based on the acquired holographic images. We demonstrated the success of this mobile bio-aerosol detector with a virtual impactor using different types of pollen (i.e., bermuda, elm, oak, pine, sycamore, and wheat) and achieved a blind classification accuracy of 92.91%. This mobile and cost-effective device weighs ~700 g and can be used for label-free sensing and quantification of various bio-aerosols over extended periods since it is based on a cartridge-free virtual impactor that does not capture or immobilize particulate matter.

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