We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Imaging and spectroscopic analysis of pathogens in water, and their classification with machine learning algorithms
Summary
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.
Garantizar la seguridad y calidad de los recursos hídricos es fundamental, lo que requiere la clasificación rápida y precisa de posibles patógenos. Los métodos de detección convencionales son a menudo lentos, y obtener resultados precisos puede tomar varios días. Dada la necesidad crítica de respuestas rápidas para prevenir epidemias, la automatización en la clasificación de patógenos permite una detección oportuna, minimizando los tiempos de respuesta ante riesgos de contaminación. Este proyecto propone un enfoque integral para automatizar la identificación y clasificación de especies de cianobacterias utilizando sistemas avanzados de imagen y espectroscopía Raman. Mediante la utilización de técnicas como la iluminación de campo oscuro y la generación de espectros Raman de cultivos puros, el proyecto busca desarrollar modelos de clasificación automática implementables en dispositivos portátiles y de bajo costo
Sign in to start a discussion.
More Papers Like This
Raman 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.