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Papers
20 resultsShowing papers similar to Imaging and spectroscopic analysis of pathogens in water, and their classification with machine learning algorithms
ClearRaman spectroscopy based detection and classification of algal blooms: A microchemical approach for environmental management
This study applied Raman spectroscopy as a microchemical tool for detecting and classifying algal blooms linked to eutrophication in aquatic ecosystems. Researchers found that the technique can identify bloom-forming organisms and associated contaminants, offering a promising approach for environmental monitoring and management of water quality issues connected to nutrient pollution.
Analysis and differentiation of toxic and non-toxic cyanobacteria using Raman spectroscopy
This paper is not about microplastics. It used Raman spectroscopy to distinguish between toxic and non-toxic strains of cyanobacteria (blue-green algae) in water. While the detection technology overlaps with methods used for microplastic identification, this study focuses entirely on algal toxin monitoring with no connection to microplastic contamination.
Raman Spectroscopy Enhanced By Machine Learning For Effective Microplastic Detection In Aquatic Systems
Researchers explored combining Raman spectroscopy with machine learning techniques to improve microplastic detection and classification in aquatic systems. The study found that deep learning models, particularly convolutional neural networks, achieved high classification accuracy and significantly reduced reliance on labor-intensive manual spectral analysis for real-time environmental monitoring.
Raman Spectroscopy and Machine Learning for Microplastics Identification and Classification in Water Environments
Researchers combined Raman spectroscopy with machine learning algorithms for automated identification and classification of microplastics in water environments, achieving high accuracy in distinguishing different polymer types based on spectral fingerprints.
Pulse Feature-Enhanced Classification of Microalgae and Cyanobacteria Using Polarized Light Scattering and Fluorescence Signals
Researchers used polarization-sensitive flow cytometry with enhanced pulse feature analysis to classify microalgae and cyanobacteria in mixed samples, training classifiers on optical signatures that distinguish cell types without staining. The method achieved high classification accuracy and offers potential for rapid, label-free phytoplankton monitoring in environmental water samples.
A Distributed Deep Learning Network Based on Data Enhancement for Few-Shot Raman Spectral Classification of Litopenaeus vannamei Pathogens
Not relevant to microplastics — this paper describes a deep learning method for classifying bacterial pathogens in shrimp aquaculture using Raman spectroscopy.
Deep learning‐enabled imaging flow cytometry for high‐speedCryptosporidium and Giardia detection
Researchers developed a deep learning-enabled imaging flow cytometry system that detects Cryptosporidium and Giardia in drinking water with >99.6% classification accuracy and sensitivity of 97.4%, processing 346 frames per second and outperforming existing methods.
Multi Analyte Concentration Analysis of Marine Samples Through Regression Based Machine Learning
Researchers used Raman spectroscopy combined with machine learning to identify concentrations of multiple chemical compounds in marine water samples. The study demonstrates that this approach offers a low-cost, portable method for monitoring ocean chemistry, which is relevant for understanding environmental health in marine ecosystems.
Toward in Situ Identification of Microplastics in Water Using Raman Spectroscopy and Machine Learning
This study developed an early-stage system combining Raman spectroscopy and machine learning to identify microplastics directly in ocean water in real time, without needing to collect and process samples in a lab. A support vector machine classifier trained on spectral libraries correctly identified all pristine microplastic samples and most environmental ones, demonstrating that field-deployable automated detection is feasible. Accurate real-time monitoring tools are urgently needed to understand where microplastics concentrate in the ocean and to track pollution trends.
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.
Monitoring Bioindication of Plankton through the Analysis of the Fourier Spectra of the Underwater Digital Holographic Sensor Data
Researchers developed a method to monitor plankton biodiversity using Fourier spectral analysis of plankton images, demonstrating that spectral features of plankton assemblages correlate with species composition and ecosystem health indicators. The approach offers a computationally efficient route to continuous bioindication in marine and freshwater monitoring programs.
Application of a convolutional neural network for automated multiclass identification of field-collected microplastics and diatom algae from optical microscopy images
Researchers developed and evaluated a convolutional neural network model using transfer learning to automatically classify field-collected microplastics and diatom algae from optical microscopy images, using a dataset of real microplastics sampled from a freshwater reservoir. The model achieved automated multi-class identification, including detection of diatom frustules that survive hydrogen peroxide processing, addressing challenges posed by the lack of standardised microplastic analysis protocols.
Deep Learning-Based Image Recognition System for Automated Microplastic Detection and Water Pollution Monitoring
This study developed a deep learning image recognition system to automate the detection and classification of microplastics from microscopy images of water samples. The system achieved high accuracy across particle types and sizes, offering a scalable and less labor-intensive alternative to manual microscopy for large-scale water pollution monitoring.
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.
Emerging IoT-Spectroscopy Methods for Targeted Water Pollutants in Household Point-of-Use (POUs) Environments
This paper is not about microplastics; it uses IoT-connected Raman spectroscopy to detect bacterial pathogens like Legionella and Salmonella in household water supplies in Malaysia.
Rapid identification of microplastic using portable Raman system and extra trees algorithm
Researchers developed a portable Raman spectroscopy system combined with a machine learning algorithm to rapidly identify and classify different types of microplastics in the field. Portable real-time identification tools are important for environmental monitoring programs that need to quickly characterize microplastics without sending samples to a laboratory.
Development of a Near-Infrared Imaging System for Identifying Microplastics in Water
Researchers developed a near-infrared imaging system capable of automatically identifying and characterizing microplastics suspended in water, successfully obtaining material identification images without the manual sorting typically required by conventional methods.
Machine 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.
Machine Learning Method for Microplastic Identification Using a Combination of Machine Learning and Raman Spectroscopy
Researchers developed a machine learning method for identifying microplastics using a combination of multiple spectroscopic techniques, improving classification accuracy beyond single-method approaches and enabling automated polymer identification.
Possibilities of Real Time Monitoring of Micropollutants in Wastewater Using Laser-Induced Raman & Fluorescence Spectroscopy (LIRFS) and Artificial Intelligence (AI)
Researchers developed a real-time monitoring method combining deep-UV laser-induced Raman and fluorescence spectroscopy with AI-based analysis to detect micropollutants in wastewater treatment plants across different treatment stages.