In allergology and environmental monitoring, there is a pressing need for fast and simple approaches for the analysis of pollen grains. In this work, we propose the use of microfluidic impedance cytometry, a label-free electrical characterization technique, in combination with machine learning-based image processing. Preliminary results show that this electrooptical cytometry provides high-content information useful for characterization of grain conditions and taxa classification.
D?orazio, M., Reale, R., De Ninno, A., Brighetti, M.a., Mencattini, A., Businaro, L., et al. (2020). Boosting automated palynology via microfluidics and machine learning. In Seventh National Congress of Bioengineering: proceedings (pp.244-247). Patreon.
Boosting automated palynology via microfluidics and machine learning
D?Orazio, M.;Reale, R.;De Ninno, A.;Mencattini, A.;Martinelli, E.;Bisegna, P.;Travaglini, A.;Caselli, F.
2020-01-01
Abstract
In allergology and environmental monitoring, there is a pressing need for fast and simple approaches for the analysis of pollen grains. In this work, we propose the use of microfluidic impedance cytometry, a label-free electrical characterization technique, in combination with machine learning-based image processing. Preliminary results show that this electrooptical cytometry provides high-content information useful for characterization of grain conditions and taxa classification.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
