Last updated on 17-1-2025 by Nicolas Bruffaerts
Scientific poster, presentation or proceeding
Anglais
SCIENSANO
Mots-clés
Résumé:
Airborne bioparticles, notably fungal spores, pose health risks, necessitating precise monitoring. While manual methods exist, interest is shifting towards automated systems employing machine learning. However, challenges persist due to diverse particle properties and limited training data. This study, part of SYLVA and COST Action ADOPT, addresses these gaps by establishing best practices for cultivating reference material and creating tailored datasets. Seventeen fungal species were tested on Plair RapidE+ and SwisensPoleno Jupiter. Proof-of-principle models using holography and fluoresce…