Articles | Volume 10, issue 2
https://doi.org/10.5194/amt-10-695-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-10-695-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
03 Mar 2017
Research article |
| 03 Mar 2017
Evaluation of machine learning algorithms for classification of primary biological aerosol using a new UV-LIF spectrometer
Simon Ruske
CORRESPONDING AUTHOR
Centre for Atmospheric Science, SEAES, University of Manchester,
Manchester, UK
David O. Topping
Centre for Atmospheric Science, SEAES, University of Manchester,
Manchester, UK
NCAS, National Centre for Atmospheric Science,
University of Manchester, Manchester, UK
Virginia E. Foot
Defence, Science and Technology Lab., Porton Down, Salisbury, Wiltshire, SP4 0JQ, UK
Paul H. Kaye
Particle Instruments Research Group, University of Hertfordshire, Hatfield, AL 10 9AB, UK
Warren R. Stanley
Particle Instruments Research Group, University of Hertfordshire, Hatfield, AL 10 9AB, UK
Ian Crawford
Centre for Atmospheric Science, SEAES, University of Manchester,
Manchester, UK
Andrew P. Morse
Department of Geography
and Planning, University of Liverpool, Liverpool, UK
Martin W. Gallagher
Centre for Atmospheric Science, SEAES, University of Manchester,
Manchester, UK
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- Fluorescent bioaerosol particle, molecular tracer, and fungal spore concentrations during dry and rainy periods in a semi-arid forest M. Gosselin et al. 10.5194/acp-16-15165-2016
51 citations as recorded by crossref.
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- 基于1D-CNN的生物气溶胶衰减全反射傅里叶变换红外光谱识别 汪. Wang Yang et al. 10.3788/AOS231963
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- Fluorescence Methods for the Detection of Bioaerosols in Their Civil and Military Applications M. Kwaśny et al. 10.3390/s23063339
- Size-Resolved Single-Particle Fluorescence Spectrometer for Real-Time Analysis of Bioaerosols: Laboratory Evaluation and Atmospheric Measurements M. Zhang et al. 10.1021/acs.est.9b01862
- Enhanced Detection of Primary Biological Aerosol Particles Using Machine Learning and Single-Particle Measurement A. Rahman et al. 10.1021/acsestengg.4c00262
- Systematic characterization and fluorescence threshold strategies for the wideband integrated bioaerosol sensor (WIBS) using size-resolved biological and interfering particles N. Savage et al. 10.5194/amt-10-4279-2017
- Virtual Impactor-Based Label-Free Pollen Detection using Holography and Deep Learning Y. Luo et al. 10.1021/acssensors.2c01890
- Quantifying bioaerosol concentrations in dust clouds through online UV-LIF and mass spectrometry measurements at the Cape Verde Atmospheric Observatory D. Morrison et al. 10.5194/acp-20-14473-2020
- Recent Advances in Monitoring, Sampling, and Sensing Techniques for Bioaerosols in the Atmosphere E. Kabir et al. 10.1021/acssensors.9b02585
- Towards a UK Airborne Bioaerosol Climatology: Real-Time Monitoring Strategies for High Time Resolution Bioaerosol Classification and Quantification I. Crawford et al. 10.3390/atmos14081214
- Real-time detection of airborne fluorescent bioparticles in Antarctica I. Crawford et al. 10.5194/acp-17-14291-2017
- Evaluation of a hierarchical agglomerative clustering method applied to WIBS laboratory data for improved discrimination of biological particles by comparing data preparation techniques N. Savage & J. Huffman 10.5194/amt-11-4929-2018
- Recent progress in online detection methods of bioaerosols T. An et al. 10.1016/j.fmre.2023.05.012
- Development and characterization of an inexpensive single-particle fluorescence spectrometer for bioaerosol monitoring B. Swanson & J. Huffman 10.1364/OE.26.003646
- Label-Free Bioaerosol Sensing Using Mobile Microscopy and Deep Learning Y. Wu et al. 10.1021/acsphotonics.8b01109
- RealForAll: real-time system for automatic detection of airborne pollen D. Tešendić et al. 10.1080/17517575.2020.1793391
- Bioaerosol Sensor for In Situ Measurement: Real-Time Measurement of Bioaerosol Particles in a Real Environment and Demonstration of the Effectiveness of Air Purifiers to Reduce Bioaerosol Particle Concentrations at Hot Spots U. Yanagi et al. 10.3390/atmos14111656
- Comprehensive insights into advances in ambient bioaerosols sampling, analysis and factors influencing bioaerosols composition B. Sajjad et al. 10.1016/j.envpol.2023.122473
- STRAPS v1.0: evaluating a methodology for predicting electron impact ionisation mass spectra for the aerosol mass spectrometer D. Topping et al. 10.5194/gmd-10-2365-2017
- Contribution of fluorescent primary biological aerosol particles to low-level Arctic cloud residuals G. Pereira Freitas et al. 10.5194/acp-24-5479-2024
- Bioaerosol field measurements: Challenges and perspectives in outdoor studies T. Šantl-Temkiv et al. 10.1080/02786826.2019.1676395
- Identification of new particle formation events with deep learning J. Joutsensaari et al. 10.5194/acp-18-9597-2018
- Performance of feature extraction method for classification and identification of proteins based on three-dimensional fluorescence spectrometry J. Xu et al. 10.1016/j.saa.2022.121841
- Classification of iron oxide aerosols by a single particle soot photometer using supervised machine learning K. Lamb 10.5194/amt-12-3885-2019
- Machine learning for improved data analysis of biological aerosol using the WIBS S. Ruske et al. 10.5194/amt-11-6203-2018
- Pollen clustering strategies using a newly developed single-particle fluorescence spectrometer B. Swanson & J. Huffman 10.1080/02786826.2019.1711357
- Laboratory validation of rapid discrimination of single microbial cells via SPAMS with machine learning C. Liu et al. 10.1016/j.ijms.2020.116340
- Potential of laser‐induced fluorescence‐light detection and ranging for future stand‐off virus surveillance O. Owoicho et al. 10.1111/1751-7915.13698
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- Review: The Use of Real-Time Fluorescence Instrumentation to Monitor Ambient Primary Biological Aerosol Particles (PBAP) M. Fennelly et al. 10.3390/atmos9010001
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- Improved real-time bio-aerosol classification using artificial neural networks M. Leśkiewicz et al. 10.5194/amt-11-6259-2018
- Evaluation of regional industrial cluster innovation capability based on particle swarm clustering algorithm and multi-objective optimization Y. Yan et al. 10.1007/s40747-021-00521-8
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Latest update: 23 Nov 2024
Short summary
Particles such as bacteria, pollen and fungal spores have important implications within the environment and public health sectors. Here we evaluate the performance of various different methods for distinguishing between these different types of particles using a new instrument. We demonstrate that there may be better alternatives to the currently used methods which can be further investigated in future research.
Particles such as bacteria, pollen and fungal spores have important implications within the...
