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Papers
61,005 resultsShowing papers similar to RepDwNet: Lightweight Deep Learning Model for Special Biological Blood Raman Spectra Analysis
ClearRepDwNet: Lightweight Deep Learning Model for Spectral blood Raman spectra classification
This paper is not about microplastics; it introduces RepDwNet, a lightweight deep learning model for classifying blood Raman spectra using portable spectrometers, combining multi-scale convolutions and residual connections for efficient spectral analysis.
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.
Deep learning-enabled Inference of 3D molecular absorption distribution of biological cells from IR spectra
Researchers built a deep learning computer model that can reconstruct the 3D internal structure and chemical makeup of tiny biological cells using only infrared light measurements. This near-real-time approach could speed up analysis of biological samples without physically slicing or destroying them.
Fast Detection and Classification of Microplastics below 10 μm Using CNN with Raman Spectroscopy
Researchers combined artificial intelligence with Raman spectroscopy to rapidly detect and classify microplastic particles smaller than 10 micrometers -- a size range that is especially concerning because these tiny particles can penetrate human tissues. The AI-based method dramatically reduced the time needed to identify plastic types compared to traditional approaches, making it more practical to monitor the smallest and most potentially harmful microplastics.
PlasticNet: Deep Learning for Automatic Microplastic Recognition via FT-IR Spectroscopy
Researchers developed PlasticNet, a deep learning algorithm that automatically identifies microplastic types from infrared spectral data, outperforming conventional library matching approaches. Automating microplastic identification could dramatically speed up the analysis of environmental samples and reduce human error.
Identification of extracellular vesicles from their Raman spectra via self-supervised learning
Researchers developed a deep learning method to identify and classify tiny biological particles called extracellular vesicles — which cells release and which may signal disease — using Raman spectroscopy without any chemical labels. The model achieved over 92% accuracy in distinguishing vesicles from different biological sources, including cancer patients versus healthy controls.
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.
Construction of an Intelligent Identification Model for Drugs in Near Infrared Spectroscopy and Research on Drog Classification based on Improved Deep Algorithm
Researchers built an intelligent near-infrared spectroscopy model to identify pharmaceutical compounds, training and validating a machine learning classifier on spectral data from multiple drug types. The model achieved high classification accuracy and demonstrated the potential of NIR spectroscopy combined with AI for rapid, non-destructive drug identification.
Deep learning analysis for rapid detection and classification of household plastics based on Raman spectroscopy
Researchers developed a deep learning system that can identify eight common household plastic types using Raman spectroscopy with 97% accuracy. This is faster and more reliable than traditional methods for classifying plastics. Better plastic identification tools like this are important for microplastic research because they allow scientists to quickly determine what types of plastic particles are contaminating environmental and food samples.
Deep Learning for Reconstructing Low-Quality FTIR and Raman Spectra─A Case Study in Microplastic Analyses
Researchers developed a deep learning method to reconstruct low-quality FTIR and Raman spectra, demonstrating its effectiveness for automated microplastic analysis where rapid measurement workflows produce noisy, challenging spectral datasets.
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.
Automatic classification of microplastics and natural organic matter mixtures using a deep learning model
Researchers developed a deep learning model using a convolutional neural network with spatial attention to classify microplastics mixed with natural organic matter from Raman spectra. The model achieved 99.54% accuracy compared to just 31.44% from conventional spectral library software, demonstrating that AI-based approaches can dramatically improve microplastic identification accuracy while reducing the need for time-intensive preprocessing steps.
Recent Advances in Raman Spectral Classification with Machine Learning
This review summarized recent advances in applying machine learning to Raman spectral classification, addressing the challenges of weak signals, complex spectra, and high-dimensional data that limit traditional chemometric methods. The advances have significant implications for automated, high-throughput microplastic polymer identification.
An automated deep learning pipeline based on advanced optimisations for leveraging spectral classification modelling
Researchers developed an automated system that uses advanced optimization methods to automatically design and tune deep learning models for analyzing spectral data — light-based fingerprints used to identify materials. Applied to wheat variety classification, the system achieved 94.9% accuracy with a simpler model architecture than previously reported methods.
Development of a machine learning-based method for the analysis of microplastics in environmental samples using µ-Raman spectroscopy
Researchers developed a machine learning system to identify microplastics in environmental samples using Raman spectroscopy — a technique that identifies materials by how they scatter light — training it on over 64,000 spectra and achieving recall above 99% and precision above 97%. Combining the AI with human review reduced analysis time from several hours to under one hour per sample, making microplastic monitoring far more practical at scale.
Artificial Intelligence (AI) Based Rapid Water Testing System
Researchers developed an AI-powered portable water testing system that combines multiple sensing techniques, including capacitance, resistance, UV, infrared, and Raman spectroscopy, to detect contaminants in real time. The system can identify a wide range of pollutants including microplastics, heavy metals, and organic compounds within seconds. The device aims to provide an accessible, rapid monitoring tool for water quality assessment in both industrial and domestic settings.
Deep learning-powered efficient characterization and quantification of microplastics
Researchers developed an artificial intelligence framework that uses deep learning to automatically identify and quantify microplastics from infrared spectra and visual images. The system achieved high accuracy in classifying plastic types and counting particles, dramatically reducing the time needed compared to manual analysis. This tool could make large-scale microplastic monitoring faster and more consistent across different research laboratories.
Slim Deep Learning Approach for Microplastics Image Classification in the Marine Environment
Researchers developed a lightweight convolutional neural network called the Slim-DL-Model for classifying microplastics in marine environment images, designed to overcome the computational demands of existing architectures like VGG16 and ResNet for real-time field applications. The model achieves competitive classification accuracy while significantly reducing computational requirements, enabling deployable microplastic monitoring systems.
Machine learning outperforms humans in microplastic characterization and reveals human labelling errors in FTIR data
Researchers developed a small but powerful neural network that can identify microplastic types from infrared spectroscopy data more accurately than human experts. The AI model classified 16 different categories of microplastics and even revealed errors in human-labeled data. This technology could dramatically speed up microplastic analysis in environmental and health studies, making it easier to understand the scale and types of microplastic contamination people are exposed to.
Identification of microplastics using a convolutional neural network based on micro-Raman spectroscopy
Researchers combined micro-Raman spectroscopy with a neural network to identify microplastics, achieving over 99% accuracy across 10 different plastic types. The system was also tested on real environmental samples and performed well at classifying unknown particles. This AI-powered approach could make microplastic identification faster and more reliable for environmental monitoring.
Top-Down Ramanomics Instrumentation Overview: from Quantitative Ramanomics with Deep Convolutional Neural Networks for Intraoperative Point-of-Care Testing Applications to Molecular Optical Laser Examiners. Part I (Bibliographic Review)
This instrumentation review covered top-down Raman spectroscopy approaches — from quantitative bulk Ramanomics to single-cell techniques — describing advances in hardware and data analysis that enable chemical profiling of complex biological and environmental samples. The review contextualizes the role of Raman methods in microplastic and microbiome research.
Quantitative analysis of microplastics in water environments based on Raman spectroscopy and convolutional neural network
Researchers developed a method combining Raman spectroscopy with a convolutional neural network to measure microplastic concentrations in water. The approach achieved high accuracy across six different sizes of polyethylene particles in five real-world water environments, outperforming other machine learning models and offering a practical tool for quantitative microplastic monitoring.
Identification of Polymers with a Small Data Set of Mid-infrared Spectra: A Comparison between Machine Learning and Deep Learning Models
Researchers compared multiple machine learning and deep learning models for identifying polymer types from mid-infrared spectral data using a small reference dataset, finding that certain deep learning architectures outperformed traditional methods even with limited training examples, supporting automated microplastic identification.
Raman Spectroscopy Application in Food Waste Analysis: A Step towards a Portable Food Quality-Warning System
Researchers explored using Raman spectroscopy combined with machine learning to detect food waste and quality issues, proposing the technology as a portable food monitoring system with sustainability benefits.