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Papers
61,005 resultsShowing papers similar to Comparison of two rapid automated analysis tools for large FTIR microplastic datasets
ClearToward the Systematic Identification of Microplastics in the Environment: Evaluation of a New Independent Software Tool (siMPle) for Spectroscopic Analysis
A new free software tool called siMPle was developed to standardize microplastic identification from FTIR spectroscopy across instruments from different manufacturers, using a shared database and automated analysis pipeline. Testing across four different instrument types confirmed the tool produces consistent and comparable results, addressing a major bottleneck in microplastics monitoring.
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.
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 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.
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.
Machine Learning Microplastic Characterisation Surpasses Human Performance and Uncovers Labelling Errors in Public FTIR Data
Researchers developed a machine learning system for automated FTIR-based microplastic characterization that surpassed human expert performance in classification accuracy and identified labeling errors in publicly available FTIR datasets. The system offers a faster, more consistent alternative to manual spectral analysis and highlights quality issues in existing reference databases used for microplastic identification.
Automated analysis of microplastics based on vibrational spectroscopy: are we measuring the same metrics?
Researchers compared three automated vibrational spectroscopy methods for microplastic analysis and found significant discrepancies in particle counts, size distributions, and polymer identification, highlighting the urgent need for standardized measurement protocols.
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.
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.
Inter-equipment reliable identification of microplastics by micro-FTIR using low computational resources
Researchers developed a low-computational-resource method for reliable microplastic identification by micro-FTIR spectroscopy that performs consistently across different instruments. The approach, funded under the EU PlasticTrace metrology project, addresses inter-equipment variability — a major barrier to harmonized microplastic monitoring — enabling more comparable results across laboratories.
Inter-equipment reliable identification of microplastics by micro-FTIR using low computational resources
Researchers developed a low-computational-resource method for reliable microplastic identification by micro-FTIR spectroscopy that performs consistently across different instruments. The approach, funded under the EU PlasticTrace metrology project, addresses inter-equipment variability — a major barrier to harmonized microplastic monitoring — enabling more comparable results across laboratories.
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.
Inter-instrument definition of valid criteria for the automatic identification of microplastics by micro-Raman spectroscopy
Researchers developed a standardized methodology for automatically identifying microplastics using micro-Raman spectroscopy across different laboratory instruments. They determined optimal match algorithms and threshold values that achieved a 95% true positive rate with minimal false positives, even when spectra were collected on different spectrometers. The study addresses a key barrier to reliable, reproducible microplastic identification in environmental and health research.
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.
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.
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.
Implications of method- and instrument-based size detection limits in μFTIR-based microplastic analysis
This study quantified how both instrument detection limits and methodological choices in micro-FTIR analysis affect reported microplastic concentrations, finding these factors substantially influence numerical results and urging standardization before cross-study comparisons are made.
Automated Machine-Learning-Driven Analysis of Microplastics by TGA-FTIR for Enhanced Identification and Quantification
Researchers developed an automated machine-learning system to identify and measure microplastics using a combination of heat analysis and infrared spectroscopy. The system can distinguish between different plastic types more accurately and faster than manual methods. Better detection tools like this are important because reliable measurement of microplastics in food, water, and the environment is essential for understanding human exposure levels.
Data and Code for High Throughput FTIR Analysis of Macro and Microplastics with Plate Readers
This dataset and code support a method using FTIR plate readers to analyze large numbers of macro- and microplastic samples simultaneously. High-throughput FTIR analysis dramatically increases the speed of plastic identification compared to traditional one-sample approaches. The open-source tools make large-scale environmental microplastic monitoring more accessible.
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.
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.
Critical Review of Processing and Classification Techniques for Images and Spectra in Microplastic Research
This review critically evaluates image analysis and spectroscopic methods used to identify and classify microplastics, including optical microscopy, electron microscopy, FTIR, and Raman spectroscopy. The authors highlight the need for standardized color classification, improved spectral libraries, and shared data tools to make microplastics studies more comparable.
Exploratory analysis of hyperspectral FTIR data obtained from environmental microplastics samples
Hyperspectral infrared imaging is an effective method for finding and characterizing microplastics in environmental samples, and this paper explores analytical approaches for extracting useful information from the large datasets it generates. Better analytical tools make it faster and more accurate to identify and classify microplastics 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.