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

arXiv: https://arxiv.org/abs/2208.13979 · HTML: https://mlsanity.com/paper/2208.13979/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 68 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [Phase recovery and holographic image reconstruction using deep learning in neural networks](https://mlsanity.com/paper/1705.04286/) (2017)
- [Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery](https://mlsanity.com/paper/1803.08138/) (2018)
- [Label-free detection of Giardia lamblia cysts using a deep learning-enabled portable imaging flow cytometer](https://mlsanity.com/paper/2007.10795/) (2020)

## Abstract (arXiv)

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.