We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Papers
20 resultsShowing papers similar to Investigation of new analysis methods for simultaneous and rapid identification of five different microplastics using ATR-FTIR spectroscopy and chemometrics
ClearA 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 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.
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.
An investigation on the applications of advanced Infrared Spectroscopy, Spectral Imaging and Machine Learning for Polymer Characterization, including microplastics
This study integrated advanced infrared spectroscopy, spectral imaging, chemometrics, and machine learning to identify and characterize microplastics and polymer degradation products. The combination of techniques improved both the accuracy and throughput of MP analysis compared to conventional methods.
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.
Refined Analysis of Microplastics: Integrating Infrared and Raman Spectroscopy
This study optimized the use of Fourier Transform Infrared Spectroscopy (FTIR) and Raman spectroscopy for characterizing microplastics in aquatic environments, finding that integrating both techniques improves identification accuracy and physicochemical characterization.
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.
Comparison of μ-ATR-FTIR spectroscopy and py-GCMS as identification tools for microplastic particles and fibers isolated from river sediments
Researchers compared two identification methods — micro-ATR-FTIR spectroscopy and pyrolysis-GC-MS — for characterizing microplastics extracted from river sediments, finding that the methods generally agreed on dominant polymers but differed in sensitivity to certain types. The comparison provides practical guidance for choosing analytical methods in freshwater microplastic monitoring programs.
Microplastics in different water samples (seawater, freshwater, and wastewater): Methodology approach for characterization using micro-FTIR spectroscopy
Researchers developed a standardized methodology for detecting and characterizing small microplastics (10-500 micrometers) in different water types using micro-FTIR spectroscopy. The study tested various sample preparation approaches for seawater, freshwater, and wastewater, establishing reliable protocols for rinsing, digestion, and microplastic collection that can be used to assess treatment plant removal efficiency.
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.
Simultaneous identification of microparticles material nature and size distribution using spectral resonances in MIR-ATR spectroscopy
Researchers developed a mid-infrared ATR spectroscopy technique that can simultaneously identify the material type and size distribution of microparticles in liquid samples using spectral resonance patterns. The method works for both bacteria in biological fluids and microplastics in water. Simultaneous size and composition analysis speeds up environmental microplastic characterization.
Harmonizing infrared spectroscopic techniques for microplastic identification: a comparative evaluation of ATR and µFTIR transmission and reflection modes
Researchers systematically compared the performance of Attenuated Total Reflectance (ATR) and micro-Fourier Transform Infrared Spectroscopy (muFTIR) in both transmission and reflection modes for identifying microplastics from twelve common real-world plastic products, providing guidance on optimizing spectroscopic technique selection.
Batch analysis of microplastics in water using multi-angle static light scattering and chemometric methods
This study presents a batch analysis approach using multi-angle light scattering combined with chemometrics to measure microplastic size and concentration in water samples more quickly than single-particle methods. Faster analytical approaches are needed to scale up environmental microplastic monitoring.
A facile approach to microplastic identification and quantification using differential scanning calorimetry
Researchers developed a simpler differential scanning calorimetry method to identify and quantify six types of semi-crystalline microplastic polymers in water samples, offering a lower-cost alternative to μFTIR that also provides mass concentration data.
The power of a multi-technique approach for the reliable quantification of microplastics in water
Researchers applied a multi-technique analytical approach combining several spectroscopic and microscopic methods to improve the reliability of microplastic quantification in environmental samples. The combined approach reduced false positives and improved polymer identification accuracy compared to any single method used alone.
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.
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.
Sequential quantification of number and mass of microplastics in municipal wastewater using Fourier-transform infrared spectroscopy and pyrolysis gas chromatography-mass spectrometry
Researchers developed a sequential analytical method combining FTIR microscopy and pyrolysis-GC/MS to identify and quantify microplastics in municipal wastewater samples, with FTIR providing polymer type and physical dimensions and Pyr-GC/MS providing chemical composition. The combined approach improves accuracy compared to using either method alone.
Validation of an FT-IR microscopy method for the determination of microplastic particles in surface waters
Researchers validated an FT-IR microscopy method for reliably detecting and quantifying microplastic particles in aquatic and solid samples. Validated, standardized analytical methods are essential for producing comparable data across laboratories and building a reliable global picture of microplastic contamination.
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.