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Papers
20 resultsShowing papers similar to Pulse Feature-Enhanced Classification of Microalgae and Cyanobacteria Using Polarized Light Scattering and Fluorescence Signals
ClearMachine Learning Powered Microalgae Classification by Use of Polarized Light Scattering Data
Researchers developed a machine learning framework using polarized light scattering data to classify 35 categories of marine microalgae, finding that non-linear support vector machines achieved identification accuracy above 80% for more than 10 algal categories.
Probing Individual Particles in Aquatic Suspensions by Simultaneously Measuring Polarized Light Scattering and Fluorescence
Researchers developed a portable optical sensor that simultaneously measures polarized light scattering and fluorescence from individual particles in water, enabling classification of microplastics versus microalgae in situ. This dual-measurement approach improves particle identification accuracy compared to single-measurement methods.
Optimized Classification of Suspended Particles in Seawater by Dense Sampling of Polarized Light Pulses
Researchers developed an optical method using polarized light pulses to classify suspended particles in seawater, aiming to distinguish microplastics from natural particles like algae in situ. A reliable in-water optical sensor for microplastics would greatly improve environmental monitoring capability.
Simultaneously Acquiring Optical and Acoustic Properties of Individual Microalgae Cells Suspended in Water
Researchers developed a multimodality technique to simultaneously measure polarized light scattering, fluorescence, and laser-induced acoustic wave signals from individual microalgae cells in water, demonstrating that the combined approach could discriminate between Spirulina species and different physiological states of Microcystis and enable single-cell assessment of photosynthetic energy absorption.
Differentiating Microplastics from Natural Particles in Aqueous Suspensions Using Flow Cytometry with Machine Learning
Researchers developed a stain-free flow cytometry method combined with machine learning to rapidly distinguish microplastics from natural particles like algae and sediment in water samples. The approach achieved identification accuracies over 93% and was validated in freshwater environmental samples, offering a time-efficient screening tool for microplastic monitoring.
Classification of suspended particles in seawater using an in situ polarized light scattering prototype
This study developed and field-tested an underwater sensor that uses polarized light scattering to distinguish between microplastics, sediment particles, and phytoplankton in seawater in real time. Lab tests showed classification accuracy above 85%, and the device was successfully deployed in a Chinese coastal bay across two seasons. Such in-situ monitoring tools could greatly improve our ability to track microplastic concentrations in the ocean without the labor-intensive sample collection and lab analysis currently required.
Simultaneous Measurement of Polarization and Excitation-Emission Spectrum of Suspended Particles in Water
Researchers developed an optical method capable of simultaneously measuring multi-wavelength polarized light scattering (Stokes parameters) and fluorescence excitation-emission matrix spectra of suspended single particles in water within 125 ns. Testing on microalgae, microplastics, and sediments demonstrated that the multi-wavelength approach outperforms single-wavelength classification for identifying harmful microalgae and other aquatic particulates.
Discrimination of Microplastics and Phytoplankton Using Impedance Cytometry
Researchers demonstrated that impedance cytometry can discriminate between microplastics and phytoplankton in ocean water samples. The study suggests this technique could enable high-throughput, deployable monitoring of both plankton communities and microplastic pollution levels, addressing a key gap in current marine monitoring capabilities.
Imaging and spectroscopic analysis of pathogens in water, and their classification with machine learning algorithms
Researchers developed an integrated approach for automated classification of cyanobacterial pathogens in water using dark-field illumination imaging combined with Raman spectroscopy, with machine learning algorithms applied for rapid species identification. The system aims to reduce pathogen detection times in water quality monitoring compared to conventional culture-based methods.
Intelligent polarization-sensitive holographic flow-cytometer: Towards specificity in classifying natural and microplastic fibers
An intelligent polarization-sensitive holographic flow cytometer was developed to classify natural and synthetic microplastic fibers at the micron scale, addressing the need for automated identification of the dominant form of microplastic pollution -- fibers -- in aquatic ecosystems.
Differentiation of suspended particles by polarized light scattering at 120°
A polarized light scattering method was developed to rapidly distinguish different types of suspended particles in seawater, including microplastics, microalgae, and sediment. This optical approach could enable faster, real-time monitoring of microplastic concentrations in ocean water.
Classification of Microplastic Particles in Water using Polarized Light Scattering and Machine Learning Methods
Researchers developed a reflection-based, in-situ classification method for microplastic particles in water using polarized light scattering combined with machine learning, successfully identifying colorless particles in the 50-300 micrometer range. The approach circumvents transmission-based interference problems and offers a pathway toward continuous, large-scale microplastic monitoring in aquatic environments.
Imaging-based lensless polarisation-resolving fluid stream analyser for automated, label-free and cost-effective microplastic classification
Researchers developed an imaging-based, lensless, polarisation-resolving fluid stream analyser for automated, label-free, and cost-effective microplastic classification in liquid samples, addressing the lack of in-situ monitoring solutions for ocean environments. The device operates at high flow rates using a custom illumination circuit to reduce motion blur, providing quantitative classification of microplastics without the labour intensity and cost of traditional sampling methods.
Imaging‐Based Lensless Polarization‐Sensitive Fluid Stream Analyzer for Automated, Label‐Free, and Cost‐Effective Microplastic Classification
Researchers developed an imaging-based lensless polarization-sensitive fluid stream analyzer that combines digital in-line holography with polarization sensitivity for automated, label-free, and cost-effective in situ detection and classification of microplastics in fluid streams, offering a practical tool for continuous aquatic monitoring without the labor costs of traditional sampling.
Digital Image Identification of Plankton Using Regionprops and Bagging Decision Tree Algorithm
Researchers developed a digital image classification system using machine learning to identify and count plankton from microscopy images. The method reduced the time and subjectivity of manual identification while maintaining accuracy. Automated plankton identification could also be adapted to distinguish microplastics from biological particles in environmental water samples.
Identification of microplastics in wastewater samples by means of polarized light optical microscopy
Scientists tested polarized light optical microscopy as a rapid method for identifying microplastics in wastewater samples, finding it could distinguish synthetic polymer particles from natural debris based on their optical properties without requiring expensive spectroscopy equipment.
Statistical Mueller matrix driven discrimination of suspended particles
Researchers developed a statistical method using polarized light scattering to distinguish between different types of suspended particles. This technique has potential applications for identifying and characterizing microplastic particles in water samples.
Plankton classification with high-throughput submersible holographic microscopy and transfer learning
Researchers used underwater holographic microscopes and transfer learning — an AI technique that applies knowledge from one task to another — to automatically classify diverse plankton species from images, including rare forms. The system shows promise for large-scale, automated ocean monitoring without needing constant human analysis.
Photonic Microfluidic Technologies for Phytoplankton Research
This review covers photonic microfluidic technologies for studying phytoplankton — microscopic algae that produce half of Earth's oxygen — highlighting how miniaturized optical tools enable single-cell analysis of these ecologically critical organisms.
High-throughput microplastic assessment using polarization holographic imaging
Researchers built a portable, low-cost system that uses holographic imaging and polarized light combined with deep learning to automatically detect, count, and classify microplastics in water in real time — without lengthy sample preparation. This tool significantly speeds up microplastic monitoring and could be widely deployed for environmental surveillance.