Supplementary material to "Merging holography, fluorescence, and machine learning for in situ, continuous characterization and classification of airborne microplastics"

Nicholas D. Beres; Julia Burkart; Elias Graf; Yanick Zeder; Lea Ann Dailey; Bernadett Weinzierl

doi:10.5194/egusphere-2023-2853-supplement

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Supplementary material to "Merging holography, fluorescence, and machine learning for in situ, continuous characterization and classification of airborne microplastics"

2023

Nicholas D. Beres, Julia Burkart, Elias Graf, Yanick Zeder, Lea Ann Dailey, Bernadett Weinzierl

Summary

Researchers described the technical image analysis parameters used in a system that combines holography, fluorescence, and machine learning to identify and classify airborne microplastics in real time, providing the methodological detail needed to interpret particle shape and size measurements from the instrument.

S1 Morphology measures from holographic image analysisIn Sect.3.1 of the main manuscript, we define several measured parameters of particles derived from the holographic imaging system of the SwisensPoleno.Each holographic particle image is binarized (converted from pixel values ranging from 0 to 1 to a value of either 0 or 1 based on thresholding) and measurements are calculated using the scikit-image image processing software (van der Walt et al., 2014).The measurements discussed in the main text are defined here.

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