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

TL;DR

This paper introduces a portable, cost-effective bio-aerosol sensor that uses holography and deep learning to classify particles like pollen without labels, achieving over 92% accuracy in real-time detection.

Contribution

The work presents a novel mobile holographic bio-aerosol detection device combining virtual impactor technology with deep learning for label-free classification.

Findings

Achieved 92.91% classification accuracy for pollen types.

Developed a lightweight (~700 g) portable device for long-term bio-aerosol monitoring.

Demonstrated effective label-free sensing without particle immobilization.

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.