Articles | Volume 11, issue 11
https://doi.org/10.5194/amt-11-6259-2018
https://doi.org/10.5194/amt-11-6259-2018
Research article
 | 
20 Nov 2018
Research article |  | 20 Nov 2018

Improved real-time bio-aerosol classification using artificial neural networks

Maciej Leśkiewicz, Miron Kaliszewski, Maksymilian Włodarski, Jarosław Młyńczak, Zygmunt Mierczyk, and Krzysztof Kopczyński

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Subject: Aerosols | Technique: Laboratory Measurement | Topic: Data Processing and Information Retrieval
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Cited articles

Agranovski, V., Ristovski, Z., Hargreaves, M., Blackall, P. J., and Morawska, L.: Performance evaluation of the UVAPS: Influence of physiological age of airborne bacteria and bacterial stress, J. Aerosol Sci., 34, 1711–1727, https://doi.org/10.1016/S0021-8502(03)00191-5, 2003. 
Antowiak, M. and Chałasínska-Macukow, K.: Fingerprint identification by using artificial neural network with optical wavelet preprocessing, Opto-Electron. Rew., 11, 327–337, 2003. 
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Bhangar, S., Huffman, J. A., and Nazaroff, W. W.: Size-resolved fluorescent biological aerosol particle concentrations and occupant emissions in a university classroom, Indoor Air, 24, 604–617, https://doi.org/10.1111/ina.12111, 2014. 
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Short summary
In this study we demonstrate the application of artificial neural networks to the real-time analysis of single-particle fluorescence fingerprints acquired using BARDet (a BioAeRosol Detector). 48 different aerosols including pollens, bacteria, fungi, spores and nonbiological substances were characterized. An entirely new approach to data analysis using a decision tree comprising 22 independent neural networks was discussed. A very high accuracy of aerosol classification in real time resulted.