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Papers
61,005 resultsShowing papers similar to Automated identification and quantification of microfibres and microplastics
ClearRobust Automatic Identification of Microplastics in Environmental Samples Using FTIR Microscopy
Researchers developed a robust automated method for identifying microplastics in environmental samples using FTIR microscopy combined with machine learning-based spectral matching, improving the consistency and efficiency of microplastic identification compared to manual evaluation.
Automated Identification and Quantification of Microplastics by FTIR Imaging and Image Analysis
This research developed an automated system using FTIR imaging and chemometric analysis to identify and count microplastic particles smaller than 500 micrometers. Automating this step addresses a major bottleneck in microplastic research, allowing for faster and more consistent analysis of environmental samples.
An automated approach for microplastics analysis using focal plane array (FPA) FTIR microscopy and image analysis
Researchers developed an automated approach using focal plane array FT-IR spectroscopy for microplastic analysis, enabling faster and more comprehensive identification of particles in environmental samples with less manual effort.
Development of a rapid detection protocol for microplastics using reflectance-FTIR spectroscopic imaging and multivariate classification
Reflectance-FTIR spectroscopy was evaluated as a faster and more automated detection method for microplastics in environmental samples, with results showing strong potential for high-throughput screening. The method could reduce the time and cost of routine microplastic monitoring programs.
Computer-Assisted Analysis of Microplastics in Environmental Samples Based on μFTIR Imaging in Combination with Machine Learning
Researchers developed machine learning approaches for automated microplastic identification in environmental samples from micro-FTIR imaging data, demonstrating improved accuracy and speed compared to traditional spectral library search methods for scalable analysis.
Focal plane array detector-based micro-Fourier-transform infrared imaging for the analysis of microplastics in environmental samples
Researchers developed an automated protocol using focal plane array FT-IR imaging to identify and count microplastics on filters without manual sorting, dramatically increasing throughput compared to manual methods. The approach represents an important step toward standardized, high-throughput microplastic monitoring in aquatic environments.
A New Chemometric Approach for Automatic Identification of Microplastics from Environmental Compartments Based on FT-IR Spectroscopy
Researchers developed a new chemometric approach for automatic identification of microplastics from environmental samples, designed to handle the challenges of biofilm contamination and surface aging that typically impede standard spectroscopic characterisation methods.
A novel method for purification, quantitative analysis and characterization of microplastic fibers using Micro-FTIR
Researchers developed an improved method for purifying, quantifying, and characterizing microplastic fibers using micro-FTIR spectroscopy, addressing the challenge that fibers are harder to process and identify than other microplastic shapes. The method improvements enable more accurate characterization of this common but technically challenging category of environmental microplastics.
Automated rapid & intelligent microplastics mapping by FTIR microscopy: A Python–based workflow
An algorithm was developed for automated FTIR microscopy that skips empty areas and non-plastic particles on filters, dramatically reducing scan times while maintaining accuracy. Faster automated analysis makes it practical to screen more environmental microplastic samples, improving the quality of contamination assessments.
A comparison of spectroscopic analysis methods for microplastics: Manual, semi-automated, and automated Fourier transform infrared and Raman techniques
Researchers compared manual, semi-automated, and fully automated methods for identifying microplastics using FTIR and Raman spectroscopy. They found that the semi-automated approach was the best balance of accuracy and efficiency, detecting 22% more microplastic particles than manual analysis while taking less time. The fully automated method was fastest but had an 80% false positive rate, while Raman microscopy was better for very small particles but took nine times longer.
Classification and Quantification of Microplastics (<100 μm) Using a Focal Plane Array–Fourier Transform Infrared Imaging System and Machine Learning
Researchers developed a method using focal plane array Fourier transform infrared imaging to classify and quantify microplastics smaller than 100 micrometers. The technique allows simultaneous chemical identification and size measurement of individual particles across a filter sample, significantly improving throughput compared to manual analysis. The study demonstrates that automated spectroscopic imaging can reliably detect and categorize very small microplastics that are often missed by conventional methods.
Development of a novel semi-automated analytical system of microplastics using reflectance-FTIR spectrometry: designed for the analysis of large microplastics
A semi-automated reflectance-FTIR spectrometry system was developed for microplastic analysis, designed specifically for large microplastics and capable of dramatically accelerating the otherwise labor-intensive identification process while maintaining accuracy in polymer type determination.
Automated Machine-Learning-DrivenAnalysis of Microplasticsby TGA-FTIR for Enhanced Identification and Quantification
Researchers developed an automated machine-learning-driven analysis pipeline for characterizing microplastics using thermogravimetric analysis coupled with FTIR, achieving rapid polymer identification and quantification that could enable high-throughput environmental monitoring.
An ensemble machine learning method for microplastics identification with FTIR spectrum
Researchers developed an ensemble machine learning method to automatically identify microplastics using Fourier transform infrared (FTIR) spectroscopy data. The approach combines multiple classification algorithms to improve accuracy over individual methods for detecting and categorizing microplastic particles. The study suggests this automated approach could help standardize and accelerate microplastic monitoring in marine environments.
Know What You Don’t Know: Assessment of Overlooked Microplastic Particles in FTIR Images
A reference image dataset containing over 1,200 microplastic and non-microplastic particles was developed to evaluate whether FTIR-based data analysis routines miss any particles during automated microplastic identification. Many existing routines overlooked a significant fraction of particles, particularly smaller ones. Better evaluation tools are needed to ensure that automated microplastic analysis is complete and accurate.
High Throughput FTIR Analysis of Macro and Microplastics with Plate Readers
This study developed high-throughput FTIR plate reader methods to analyze larger microplastics and macroplastics more efficiently than traditional manual ATR approaches. Faster and more automated chemical identification of plastic particles is critical for scaling up environmental monitoring programs.
Automatic microplastic classification using dual-modality spectral and image data for enhanced accuracy
A dual-modality classification system combining FTIR spectral data and microscope images achieved 99% accuracy in automatically identifying five common microplastic polymer types. The study deployed a web application (MPsSpecClassify) that enables researchers to efficiently classify microplastics, addressing the time-consuming and error-prone nature of manual spectral analysis.
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.
A comprehensive and fast microplastics identification based on near-infrared hyperspectral imaging (HSI-NIR) and chemometrics
Researchers developed a near-infrared hyperspectral imaging method combined with chemometric analysis for rapid, high-throughput identification of microplastic types in mixed samples, achieving high classification accuracy and offering a faster alternative to FTIR and Raman methods for routine monitoring.
A machine learning algorithm for high throughput identification of FTIR spectra: Application on microplastics collected in the Mediterranean Sea
Researchers developed a machine learning method to automatically identify the chemical composition of microplastics from FTIR spectroscopy data collected during the Tara Mediterranean expedition. The algorithm performed well for common polymers like polyethylene and was applied to classify over 4,000 unidentified microplastic spectra. The study demonstrates that automated identification tools can significantly speed up large-scale microplastic pollution surveys while maintaining acceptable accuracy.
Reference database design for the automated analysis of microplastic samples based on Fourier transform infrared (FTIR) spectroscopy
A reference database for automated FTIR-based microplastic identification was developed using hierarchical cluster analysis of reference spectra, enabling both single-particle identification and chemical imaging analysis. The database design improves the reproducibility and comparability of automated microplastic identification across different laboratories and instrumentation types.
Identification and Quantification of Microplastics in the Marine Environment Using the Laser Direct Infrared (LDIR) Technique
Researchers evaluated the laser direct infrared (LDIR) technique for identifying and quantifying marine microplastics, demonstrating it as a faster and more automated alternative to conventional FTIR methods with comparable accuracy.
Investigation of new analysis methods for simultaneous and rapid identification of five different microplastics using ATR-FTIR spectroscopy and chemometrics
Researchers developed and evaluated ATR-FTIR spectroscopy combined with chemometric analysis for simultaneous rapid identification of five common microplastic polymer types in water samples. The method achieved high classification accuracy across polymer types, offering a faster and more automated alternative to conventional single-polymer identification approaches.
Comparison of two rapid automated analysis tools for large FTIR microplastic datasets
Researchers compared two automated analysis tools for large FTIR microplastic datasets and found significant differences in polymer identification results, highlighting the urgent need for standardized data analysis methods in microplastic research.