Articles | Volume 18, issue 23
https://doi.org/10.5194/amt-18-7297-2025
https://doi.org/10.5194/amt-18-7297-2025
Research article
 | 
02 Dec 2025
Research article |  | 02 Dec 2025

Laser-Induced Fluorescence coupled with Machine Learning as an effective approach for real-time identification of bacteria in bioaerosols

Alejandro Fontal, Sílvia Borràs, Lídia Cañas, Sofya Pozdniakova, and Xavier Rodó

Related authors

Estimation of seasonal methane fluxes over a Mediterranean rice paddy area using the Radon Tracer Method (RTM)
Roger Curcoll, Alba Àgueda, Josep-Anton Morguí, Lídia Cañas, Sílvia Borràs, Arturo Vargas, and Claudia Grossi
Atmos. Chem. Phys., 25, 6299–6323, https://doi.org/10.5194/acp-25-6299-2025,https://doi.org/10.5194/acp-25-6299-2025, 2025
Short summary
Assessment of Future Precipitation Changes in Mediterranean Climate Regions from CMIP6 ensemble
Patricia Tarín-Carrasco, Desislava Petrova, Laura Chica-Castells, Jelena Lukovic, Xavier Rodó, and Ivana Cvijanovic
EGUsphere, https://doi.org/10.5194/egusphere-2023-3057,https://doi.org/10.5194/egusphere-2023-3057, 2024
Preprint archived
Short summary

Cited articles

Amato, P., Mathonat, F., Nuñez Lopez, L., Péguilhan, R., Bourhane, Z., Rossi, F., Vyskocil, J., Joly, M., and Ervens, B.: The aeromicrobiome: the selective and dynamic outer-layer of the Earth's microbiome, Front. Microbiol., 14, https://doi.org/10.3389/fmicb.2023.1186847, 2023. 
Banerjee, S. and van der Heijden, M. G. A.: Soil microbiomes and one health, Nat. Rev. Microbiol., 21, 6–20, https://doi.org/10.1038/s41579-022-00779-w, 2023. 
Behzad, H., Gojobori, T., and Mineta, K.: Challenges and Opportunities of Airborne Metagenomics, Genome Biol. Evol., 7, 1216–1226, https://doi.org/10.1093/gbe/evv064, 2015. 
Berezin, M. Y. and Achilefu, S.: Fluorescence Lifetime Measurements and Biological Imaging, Chem. Rev., 110, 2641–2684, https://doi.org/10.1021/cr900343z, 2010. 
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. 
Download
Short summary
Monitoring airborne microbes is crucial for health and ecosystems, but often slow and expensive. We adapted an existing instrument, Rapid-E, using Laser-Induced Fluorescence and machine learning, for rapid, field-deployable bacterial identification. Our system successfully detected bacteria and showed promise in distinguishing various species. This faster approach improves environmental monitoring and helps safeguard public health by quickly spotting potential microbial threats in the air.
Share