Electro-Optical Classification of Pollen Grains via Microfluidics and Machine Learning.

In aerobiological monitoring and agriculture there is a pressing need for accurate, label-free and automated analysis of pollen grains, in order to reduce the cost, workload and possible errors associated to traditional approaches. We propose a new multimodal approach that combines electrical sensing and optical imaging to classify pollen grains flowing in a microfluidic chip at a throughput of 150 grains per second. Electrical signals and synchronized optical images are processed by two independent machine learning-based classifiers, whose predictions are then combined to provide the final classification outcome. The applicability of the method is demonstrated in a proof-of-concept classification experiment involving eight pollen classes from different taxa. The average balanced accuracy is 78.7% for the electrical classifier, 76.7% for the optical classifier and 84.2% for the multimodal classifier. The accuracy is 82.8% for the electrical classifier, 84.1% for the optical classifier and 88.3% for the multimodal classifier. The multimodal approach provides better classification results with respect to the analysis based on electrical or optical features alone. The proposed methodology paves the way for automated multimodal palynology. Moreover, it can be extended to other fields, such as diagnostics and cell therapy, where it could be used for label-free identification of cell populations in heterogeneous samples.