# A framework for real-time image detection of bioaerosols

**Authors:** Ryne A. Juidici, Yan Ye, Francisco J. Romay, Miles Owen, Qisheng Ou, David Y. H. Pui

PMC · DOI: 10.1038/s41598-025-32744-x · 2025-12-29

## TL;DR

This paper introduces a framework for detecting bioaerosols in real-time using image sensors based on light scattering and fluorescence.

## Contribution

A novel framework is proposed that uses image sensors to differentiate bioaerosols from abiotic particles via fluorescence and scattering.

## Key findings

- Exposure time, particle speed, and signal-to-noise ratio are critical for detection accuracy.
- A long-pass filter can isolate fluorescence emission from elastic light scattering.
- Color differentiation of signals may help distinguish biotic from abiotic particles.

## Abstract

Starting from the elastic light scattering and the induced fluorescence emission from airborne particles and extending to the counting and differentiation of the associated signals, a framework is proposed to describe the detection of bioaerosols using an image sensor. For validation, monodisperse NaCl particles were generated to mimic abiotic particles, while monodisperse 1% mass riboflavin particles were generated to mimic biotic particles. By challenging a prototype sensor with these particles, the exposure time, particle speed, and signal-to-noise ratio were shown to be critical parameters for detection. Additionally, it was displayed that the induced fluorescence emission can be isolated from the elastic light scattering by using a well-selected long-pass filter. Furthermore, correlating the color of the captured signals to an induced fluorescence contribution was shown to be a potential avenue of differentiation between biotic and abiotic particles. It is predicted that this color differentiation method can distinguish between a near-continuous range of visible induced fluorescence emission wavelengths, giving the ability to distinguish individual fluorophores from one another using simple filtering and a single detector. This framework will be used to further optimize the image-based bioaerosol sensor evaluated here.

The online version contains supplementary material available at 10.1038/s41598-025-32744-x.

## Linked entities

- **Chemicals:** NaCl (PubChem CID 5234), riboflavin (PubChem CID 1072)

## Full-text entities

- **Diseases:** asthmatic symptoms (MESH:D013224), allergic (MESH:D004342)
- **Chemicals:** tryptophan (MESH:D014364), Riboflavin (MESH:D012256), PSL (-), flavin mononucleotide (MESH:D005486), water (MESH:D014867), NaCl (MESH:D012965), NADPH (MESH:D009249), NADH (MESH:D009243), aluminum (MESH:D000535)
- **Species:** Bacillus anthracis (anthrax bacterium, species) [taxon 1392]

## Figures

8 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12824191/full.md

---
Source: https://tomesphere.com/paper/PMC12824191