Evaluation of a hierarchical agglomerative clustering method applied to WIBS laboratory data for improved discrimination of biological particles by comparing data preparation techniques

Savage, Nicole J.; Huffman, J. Alex

Hierarchical agglomerative clustering (HAC) analysis has been successfully applied to several sets of ambient data (e.g., Crawford et al., 2015; Robinson et al., 2013) and with respect to standardized particles in the laboratory environment (Ruske et al., 2017, 2018). Here we show for the first time a systematic application of HAC to a comprehensive set of laboratory data collected for many individual particle types using the wideband integrated bioaerosol sensor (WIBS-4A) (Savage et al., 2017). The impact of the ratio of particle concentrations on HAC results was investigated, showing that clustering quality can vary dramatically as a function of ratio. Six strategies for particle preprocessing were also compared, concluding that using raw fluorescence intensity (without normalizing to particle size) and logarithmically transforming data values (scenario B) consistently produced the highest-quality results for the particle types analyzed. A total of 23 one-to-one matchups of individual particles types was investigated. Results showed a cluster misclassification of < 15 % for 12 of 17 numerical experiments using one biological and one nonbiological particle type each. Inputting fluorescence data using a baseline inline-formula+3σ threshold produced a lower degree of misclassification than when inputting either all particles (without a fluorescence threshold) or a baseline inline-formula+9σ threshold. Lastly, six numerical simulations of mixtures of four to seven components were analyzed using HAC. These results show that a range of 12 %–24 % of fungal clusters was consistently misclassified by inclusion of a mixture of nonbiological materials, whereas bacteria and diesel soot were each able to be separated with nearly 100 % efficiency. The study gives significant support to clustering analysis commonly being applied to data from commercial ultraviolet laser/light-induced fluorescence (UV-LIF) instruments used for bioaerosol research across the globe and provides practical tools that will improve clustering results within scientific studies as a part of diverse research disciplines.

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Savage, Nicole J. / Huffman, J. Alex: Evaluation of a hierarchical agglomerative clustering method applied to WIBS laboratory data for improved discrimination of biological particles by comparing data preparation techniques. 2018. Copernicus Publications.

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Rechteinhaber: Nicole J. Savage

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