Evaluation of machine learning algorithms for classification of primary biological aerosol using a new UV-LIF spectrometer

Ruske, Simon; Topping, David O.; Foot, Virginia E.; Kaye, Paul H.; Stanley, Warren R.; Crawford, Ian; Morse, Andrew P.; Gallagher, Martin W.

doi:https://doi.org/10.5194/amt-10-695-2017

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.

https://doi.org/10.5194/amt-10-695-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 10, issue 2

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, David O. Topping, Virginia E. Foot, Paul H. Kaye, Warren R. Stanley, Ian Crawford, Andrew P. Morse, and Martin W. Gallagher

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Final revised paper (published on 03 Mar 2017)
Preprint (discussion started on 13 Jul 2016)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'Manuscript Review - Reviewer 1', Darrel Baumgardner, 01 Sep 2016
- AC1: 'Response to Referee #1', S. Ruske, 25 Oct 2016
RC2: 'Review of Ruske et al.', Anonymous Referee #2, 07 Sep 2016
- AC2: 'Response to referee #2', S. Ruske, 25 Oct 2016

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.