We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Supplementary material to "Merging holography, fluorescence, and machine learning for in situ, continuous characterization and classification of airborne microplastics"
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.
Sign in to start a discussion.
More Papers Like This
Merging holography, fluorescence, and machine learning for in situ, continuous characterization and classification of airborne microplastics
This study combined holography, fluorescence microscopy, and machine learning for continuous in situ detection and classification of airborne microplastics without the need for sample collection and laboratory analysis. The system enabled real-time characterization of particle size, shape, and type in ambient air.
Merging holography, fluorescence, and machine learning for in situ continuous characterization and classification of airborne microplastics
Researchers developed an instrument combining holography, fluorescence, and machine learning for continuous, real-time characterization of airborne microplastics. The system can identify and classify microplastic particles in situ without requiring laboratory sample collection and analysis. The study represents an advance in monitoring technology that could improve understanding of atmospheric microplastic transport and human exposure.
Digital holographic approaches to the detection and characterization of microplastics in water environments
This review examines advances in using digital holography as a high-throughput tool for detecting and characterizing microplastics in water. Researchers discuss both the hardware and software developments, including the growing role of artificial intelligence for classification tasks. The study highlights the emergence of field-portable holographic flow cytometers as a promising technology for real-time water monitoring of microplastic contamination.
Material analysis with polarization holography and machine learning
Researchers developed a polarization holographic imaging system combined with machine learning to identify different materials, demonstrating the approach on microplastic identification. This novel optical method could become a fast, non-destructive tool for classifying microplastics in environmental samples.
Digital holographic microplastics detection and characterization in heterogeneous samples via deep learning
Researchers used digital holographic microscopy combined with deep learning to detect and characterize microplastic particles in heterogeneous samples containing algae, microorganisms, and other natural particles. This automated approach could improve the speed and accuracy of environmental microplastic monitoring.