“Electro-Optical Classification of Pollen Grains via Microfluidics and Machine Learning”
D’Orazio M., Reale R., De Ninno A., Brighetti M.A., Mencattini A., Businaro L., Martinelli E., Bisegna P., Travaglini A., Caselli F. (2022), IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, vol. 69, p. 921-931, ISSN: 0018-9294, doi: 10.1109/TBME.2021.3109384
This interdisciplinary work involves sensor science, microfluidics, machine learning, and palynology. Palynology – i.e., the study of pollen and fungal spores – finds applications in high-impact fields like air quality control, allergology, and agriculture. Traditionally, the study of pollen takes place through microscopic analysis performed by specialized operators, after staining of the sample. The procedure requires long times and is prone to human errors. Therefore, there is an unmet need for accurate, label-free, and automated systems for the analysis of pollen, ideally within a field-portable and cost-effective platform. In this framework, we propose an original multimodal approach.