Articles | Volume 17, issue 17
https://doi.org/10.5194/amt-17-5051-2024
https://doi.org/10.5194/amt-17-5051-2024
Research article
 | 
03 Sep 2024
Research article |  | 03 Sep 2024

Classification accuracy and compatibility across devices of a new Rapid-E+ flow cytometer

Branko Sikoparija, Predrag Matavulj, Isidora Simovic, Predrag Radisic, Sanja Brdar, Vladan Minic, Danijela Tesendic, Evgeny Kadantsev, Julia Palamarchuk, and Mikhail Sofiev

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Cited articles

Brdar, S., Panić, M., Matavulj, P., Stanković, M., Bartolić, D., and Šikoparija, B.: Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy, Sci. Rep.-UK, 13, 3205, https://doi.org/10.1038/s41598-023-30064-6, 2023. 
Bruffaerts, N., Graf, E., Matavulj, P., Tiwari, A., Pyrri, I., Zeder, Y., Erb, S., Plaza, M., Dietler, S., Bendinelli, T., D'hooge, E., and Sikoparija, B.: Advancing automated identification of airborne fungal spores: guidelines for cultivation and reference dataset creation, Aerobiologia, in review, 2024. 
CEN: EN 16868: Ambient air - Sampling and analysis of airborne pollen grains and fungal spores for networks related to allergy - Volumetric Hirst method, CEN​​​​​​​, 2019. 
Crouzy, B., Stella, M., Konzelmann, T., Calpini, B., and Clot, B.: All-optical automatic pollen identification: Towards an operational system, Atmos. Environ., 140, 202–212, https://doi.org/10.1016/j.atmosenv.2016.05.062, 2016. 
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Short summary
We assess the suitability of a Rapid-E+ particle counter for use in pollen monitoring networks. The criterion was the ability of different devices to provide the same signal for the same pollen type, which would allow for unified reference libraries and recognition algorithms for Rapid-E+. We tested three devices and found notable differences between their fluorescence measurements. Each one showed potential for pollen identification, but the large variability between them needs to be addressed.
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