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Papers
61,005 resultsShowing papers similar to Rapid and Nondestructive On-Site Classification Method for Consumer-Grade Plastics Based on Portable NIR Spectrometer and Machine Learning
ClearComparative Study of Chemometric Approaches and Machine Learning for Miniaturized Near-infrared (micronir) Spectroscopy in Plasticwaste Sorting
This study tested a miniaturized near-infrared (NIR) spectroscopy device combined with chemometric and machine learning methods to sort different types of plastic waste. The approach accurately identified polymer types, supporting more efficient plastic recycling operations that could reduce microplastic generation.
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.
Evaluating chemometric strategies and machine learning approaches for a miniaturized near-infrared spectrometer in plastic waste classification
Not relevant to microplastics — this study compares machine learning and chemometric methods (including PCA, SVM, and neural networks) for classifying plastic waste types using a handheld near-infrared spectrometer, focused on improving plastic recycling sorting rather than microplastic detection.
Qualitative discrimination and quantitative prediction of microplastics in ash based on near-infrared spectroscopy
Researchers developed a method using portable near-infrared spectroscopy to quickly detect and measure microplastics in waste incineration ash. The study found that this approach can both identify the type of plastic present and predict its concentration, offering a faster alternative to traditional laboratory testing methods for monitoring microplastic contamination in ash residues.
SpectraNet: A unified deep learning framework for infrared spectroscopy-based prediction of plastic recyclability, type classification, and microplastic identification
Researchers built SpectraNet, a deep learning framework using mid-infrared spectroscopy to perform three tasks—plastic recyclability assessment, polymer type classification, and microplastic identification—supported by an open-access infrared spectral database of plastics and microplastics.
Mid-infrared spectroscopy and machine learning for postconsumer plastics recycling
Mid-infrared spectroscopy combined with machine learning was developed to sort and identify postconsumer plastics, aiming to prevent contamination and improve recycling stream purity. The approach could help close material loops and reduce the volume of plastic ultimately entering the environment.
Classifying polymers with mid-IR spectra and machine learning: From monitoring to detection
Researchers applied machine learning to mid-infrared spectra to automatically classify different types of plastic polymers found in the environment. Accurate polymer identification is essential for microplastic research, and this automated approach could improve monitoring efficiency and data consistency across studies.
Rapid detection of colored and colorless macro- and micro-plastics in complex environment via near-infrared spectroscopy and machine learning.
Researchers developed a near-infrared spectroscopy method combined with machine learning classifiers -- including PLS-DA, random forest, and XGBoost -- to rapidly identify both colored and colorless plastic fragments across different polymer types, thicknesses, and environmental backgrounds. The approach improved detection of colorless plastics that are typically underestimated in environmental surveys, with random forest achieving the highest classification accuracy.
Development of robust models for rapid classification of microplastic polymer types based on near infrared hyperspectral images
Researchers used near-infrared hyperspectral imaging combined with machine learning to classify nine types of microplastic particles, finding reliable results even for small particles on wet filters. This method could enable faster, automated identification of diverse microplastic types in environmental water samples.
Polymer Type Identification of Marine Plastic Litter Using a Miniature Near-Infrared Spectrometer (MicroNIR)
Researchers tested a miniature near-infrared spectrometer (MicroNIR) for rapidly identifying polymer types in marine plastic litter collected from beaches, finding it could accurately distinguish common plastics like polyethylene and polypropylene. Low-cost, portable identification tools are important for large-scale monitoring of marine plastic pollution.
A comparison of machine learning techniques for the detection of microplastics
This German-language study compared machine learning algorithms for classifying microplastics based on their infrared spectra, finding that several methods could reliably distinguish polymer types. Automating microplastic identification through machine learning could greatly increase the speed and throughput of environmental monitoring.
Advancing Plastic Waste Classification and Recycling Efficiency: Integrating Image Sensors and Deep Learning Algorithms
Researchers developed a deep learning approach combined with image sensors to improve plastic waste classification and recycling efficiency. The study demonstrates that this method can distinguish between chemically similar plastics like PET and PET-G that conventional near-infrared spectroscopy struggles to differentiate, potentially improving automated sorting systems.
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.
The potential of NIR spectroscopy in the separation of plastics for pyrolysis
This study examined the potential of near-infrared (NIR) spectroscopy to identify and sort different plastic types for chemical recycling, finding it can effectively distinguish major polymer types. Better plastic sorting technology could improve recycling rates and reduce the amount of plastic that ends up as environmental microplastic pollution.
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.
FTIR-Based Microplastic Classification: A Comprehensive Study on Normalization and ML Techniques
Researchers tested machine learning and deep learning techniques for classifying six common types of microplastics using infrared spectroscopy data. They found that using broader spectral ranges and certain normalization techniques significantly improved classification accuracy. The study demonstrates that automated identification of microplastic types is feasible and could speed up environmental monitoring efforts.
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.
Characterization and identification of microplastics using Raman spectroscopy coupled with multivariate analysis
Researchers developed a new method using Raman spectroscopy combined with machine learning to identify and classify seven types of microplastics with over 98% accuracy for most polymer types. The approach was also able to correctly identify real-world microplastic samples from snack boxes, water bottles, juice bottles, and medicine vials. This technique could make microplastic detection faster and more reliable compared to manual analysis methods.
A Comparative Study of Machine Learning and Deep Learning Models for Microplastic Classification using FTIR Spectra
Researchers compared machine learning and deep learning models for classifying microplastics using FTIR spectra, evaluating multiple algorithmic approaches against standardised spectral datasets. The study assessed classification accuracy and computational efficiency, identifying which model architectures best discriminate between polymer types in environmental microplastic samples.
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.
Spectral Classification of Large-Scale Blended (Micro)Plastics Using FT-IR Raw Spectra and Image-Based Machine Learning
Researchers developed and compared four machine learning classifiers for identifying microplastic types from Fourier transform infrared spectroscopy data using large-scale blended plastic datasets. The study found that a 1D convolutional neural network achieved the best overall accuracy at over 97%, outperforming decision tree and random forest models, offering a scalable alternative to traditional library-search methods for microplastic identification.
Identifying plastics with photoluminescence spectroscopy and machine learning
Researchers showed that combining photoluminescence spectroscopy (shining light on plastic and measuring what comes back) with machine learning can reliably identify different types of plastic materials. This low-cost, widely accessible approach could help scientists track and characterize plastic pollution in the environment at a global scale.
A Hybrid MIR-spectrum Processing Algorithm for Microplastics Analysis
Researchers developed a hybrid algorithm for classifying microplastics using their mid-infrared spectral signatures, targeting polypropylene, polyethylene, and polystyrene. The model combines principal component analysis with machine learning techniques to improve classification accuracy. The study offers an automated approach that could make routine microplastic identification faster and more reliable for environmental monitoring.
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.