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Papers
61,005 resultsShowing papers similar to Development of a novel semi-automated analytical system of microplastics using reflectance-FTIR spectrometry: designed for the analysis of large microplastics
ClearA 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.
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.
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.
Robust 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.
Generation of macro- and microplastic databases by high-throughput FTIR analysis with microplate readers
Researchers developed a high-throughput FTIR analysis technique using microplate readers to rapidly characterize large microplastics and macroplastics, which have traditionally required slower manual methods. They created a reference database of over 6,000 spectra covering more than 600 plastic, organic, and mineral materials across multiple measurement modes. The study addresses key analytical bottlenecks in plastic pollution research by enabling faster, non-destructive identification of larger plastic particles.
Automated identification and quantification of microfibres and microplastics
Researchers developed an automated method using FTIR imaging data analysis to simultaneously identify and quantify both microplastics and microfibers in environmental samples. Automation improves throughput and consistency compared to manual identification, addressing a key bottleneck in large-scale microplastic monitoring.
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.
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.
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.
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.
Identification methods in microplastic analysis: a review
This review compared identification methods used in microplastic analysis — including visual inspection, FTIR, Raman spectroscopy, and thermal analysis — evaluating their accuracy, throughput, and suitability for different sample types.
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.
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.
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.
Development of automated microplastic identification workflow for Raman micro-imaging and evaluation of the uncertainties during micro-imaging
Researchers developed an automated identification workflow for Raman micro-imaging of microplastics, validating it with artificial samples of known polymer microspheres and showing that the workflow reliably identifies plastic type and estimates particle size across a range of sizes.
Contributions of Fourier transform infrared spectroscopy in microplastic pollution research: A review
This review covers advances in Fourier transform infrared (FTIR) spectroscopy techniques — including chemical imaging — for identifying polymer types in microplastic samples and tracing their fate in different environmental matrices.
High-Throughput Analyses of Microplastic Samples Using Fourier Transform Infrared and Raman Spectrometry
Researchers developed GEPARD, an open-source software package that combines optical particle analysis with automated FT-IR and Raman microspectroscopy to enable high-throughput identification and characterization of microplastics. The system steers spectroscopic measurements based on optically detected particles, enabling efficient polymer typing, size distribution measurement, and color classification.
Study on Rapid Recognition of Marine Microplastics Based on Raman Spectroscopy
Researchers developed a rapid identification system for marine microplastics using Raman spectroscopy, enabling quick determination of plastic type and size. Fast, accurate identification tools are critical for monitoring the growing problem of microplastic pollution in ocean 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.
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.
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.
Generation of synthetic FTIR spectra to facilitate chemical identification of microplastics
Researchers generated synthetic FTIR spectra of microplastics using computational methods to augment training datasets for automated spectral identification algorithms. The synthetic spectra closely matched experimentally measured spectra, and classifiers trained on augmented datasets showed improved accuracy for identifying underrepresented polymer types in real-world samples.
Analytical tools in advancing microplastics research for identification and quantification across environmental media: from sample to insight
Researchers reviewed the analytical tools most commonly used for identifying and quantifying microplastics, focusing on FTIR and Raman spectroscopy as the two primary methods. The review compared their strengths and limitations and provided guidance for choosing between them based on particle size, sample matrix, and research objectives.
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.