Articles | Volume 11, issue 11
https://doi.org/10.5194/amt-11-6259-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-11-6259-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Nov 2018
Research article |
| 20 Nov 2018
| 20 Nov 2018
Improved real-time bio-aerosol classification using artificial neural networks
Maciej Leśkiewicz
PCO S.A. ul. Jana Nowaka-Jeziorańskiego 28, 03-982 Warsaw, Poland
Miron Kaliszewski
CORRESPONDING AUTHOR
Institute of Optoelectronics, Military University of Technology, ul. Gen. Witolda Urbanowicza 2, 00-908 Warsaw, Poland
Maksymilian Włodarski
Institute of Optoelectronics, Military University of Technology, ul. Gen. Witolda Urbanowicza 2, 00-908 Warsaw, Poland
Jarosław Młyńczak
Institute of Optoelectronics, Military University of Technology, ul. Gen. Witolda Urbanowicza 2, 00-908 Warsaw, Poland
Zygmunt Mierczyk
Institute of Optoelectronics, Military University of Technology, ul. Gen. Witolda Urbanowicza 2, 00-908 Warsaw, Poland
Krzysztof Kopczyński
Institute of Optoelectronics, Military University of Technology, ul. Gen. Witolda Urbanowicza 2, 00-908 Warsaw, Poland
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Total article views: 2,588 (including HTML, PDF, and XML)
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Total article views: 607 (including HTML, PDF, and XML)
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Cited
16 citations as recorded by crossref.
- A Virtual Impactor-Based Portable Optical Sensor With Probabilistic Neural Network: For Particle Property Identification R. Wang et al. 10.1109/JSEN.2023.3277847
- Fluorescence Methods for the Detection of Bioaerosols in Their Civil and Military Applications M. Kwaśny et al. 10.3390/s23063339
- Recent Advances in Monitoring, Sampling, and Sensing Techniques for Bioaerosols in the Atmosphere E. Kabir et al. 10.1021/acssensors.9b02585
- Airborne transmission of biological agents within the indoor built environment: a multidisciplinary review C. Argyropoulos et al. 10.1007/s11869-022-01286-w
- Enhanced Detection of Primary Biological Aerosol Particles Using Machine Learning and Single-Particle Measurement A. Rahman et al. 10.1021/acsestengg.4c00262
- Multispectral LIF-Based Standoff Detection System for the Classification of CBE Hazards by Spectral and Temporal Features L. Fellner et al. 10.3390/s20092524
- 悬浮粒子的光学散射相关测量与分析方法 曾. Zeng Nan & 杨. Yang Likun 10.3788/AOS231206
- Measurements of the Optical Scattering Properties of Single Suspended Particles and Implications for Atmospheric Studies: A Review W. Yao et al. 10.1007/s40726-024-00323-9
- Study on polarization scattering applied in aerosol recognition in the air D. Li et al. 10.1364/OE.27.00A581
- Real time and online aerosol identification based on deep learning of multi-angle synchronous polarization scattering indexes Q. Xu et al. 10.1364/OE.426501
- Identification and Removal of Pollen Spectral Interference in the Classification of Hazardous Substances Based on Excitation Emission Matrix Fluorescence Spectroscopy P. Zhang et al. 10.3390/molecules29133132
- On-Site Bioaerosol Sampling and Airborne Microorganism Detection Technologies A. Rastmanesh et al. 10.3390/bios14030122
- Understanding hourly patterns of Olea pollen concentrations as tool for the environmental impact assessment S. Fernández-Rodríguez et al. 10.1016/j.scitotenv.2020.139363
- Investigation of artificial neural network performance in the aerosol properties retrieval N. Srivastava et al. 10.2166/wcc.2021.336
- 一种基于特征提取的生物气溶胶遥测识别算法研究 杨. Yang Rong et al. 10.3788/CJL230847
- Label-Free Bioaerosol Sensing Using Mobile Microscopy and Deep Learning Y. Wu et al. 10.1021/acsphotonics.8b01109
15 citations as recorded by crossref.
- A Virtual Impactor-Based Portable Optical Sensor With Probabilistic Neural Network: For Particle Property Identification R. Wang et al. 10.1109/JSEN.2023.3277847
- Fluorescence Methods for the Detection of Bioaerosols in Their Civil and Military Applications M. Kwaśny et al. 10.3390/s23063339
- Recent Advances in Monitoring, Sampling, and Sensing Techniques for Bioaerosols in the Atmosphere E. Kabir et al. 10.1021/acssensors.9b02585
- Airborne transmission of biological agents within the indoor built environment: a multidisciplinary review C. Argyropoulos et al. 10.1007/s11869-022-01286-w
- Enhanced Detection of Primary Biological Aerosol Particles Using Machine Learning and Single-Particle Measurement A. Rahman et al. 10.1021/acsestengg.4c00262
- Multispectral LIF-Based Standoff Detection System for the Classification of CBE Hazards by Spectral and Temporal Features L. Fellner et al. 10.3390/s20092524
- 悬浮粒子的光学散射相关测量与分析方法 曾. Zeng Nan & 杨. Yang Likun 10.3788/AOS231206
- Measurements of the Optical Scattering Properties of Single Suspended Particles and Implications for Atmospheric Studies: A Review W. Yao et al. 10.1007/s40726-024-00323-9
- Study on polarization scattering applied in aerosol recognition in the air D. Li et al. 10.1364/OE.27.00A581
- Real time and online aerosol identification based on deep learning of multi-angle synchronous polarization scattering indexes Q. Xu et al. 10.1364/OE.426501
- Identification and Removal of Pollen Spectral Interference in the Classification of Hazardous Substances Based on Excitation Emission Matrix Fluorescence Spectroscopy P. Zhang et al. 10.3390/molecules29133132
- On-Site Bioaerosol Sampling and Airborne Microorganism Detection Technologies A. Rastmanesh et al. 10.3390/bios14030122
- Understanding hourly patterns of Olea pollen concentrations as tool for the environmental impact assessment S. Fernández-Rodríguez et al. 10.1016/j.scitotenv.2020.139363
- Investigation of artificial neural network performance in the aerosol properties retrieval N. Srivastava et al. 10.2166/wcc.2021.336
- 一种基于特征提取的生物气溶胶遥测识别算法研究 杨. Yang Rong et al. 10.3788/CJL230847
1 citations as recorded by crossref.
Latest update: 06 Aug 2025
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.
In this study we demonstrate the application of artificial neural networks to the real-time...
