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Papers
61,005 resultsShowing papers similar to A3M-CIR: An Attention-Aware Adversarial Masking Based Self-Supervised Chemometric Framework for Robust Infrared Spectral Analysis
ClearAutomated 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.
Machine learning outperforms humans in microplastic characterization and reveals human labelling errors in FTIR data
Researchers developed a small but powerful neural network that can identify microplastic types from infrared spectroscopy data more accurately than human experts. The AI model classified 16 different categories of microplastics and even revealed errors in human-labeled data. This technology could dramatically speed up microplastic analysis in environmental and health studies, making it easier to understand the scale and types of microplastic contamination people are exposed to.
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.
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.
Evaluation-driven preprocessing and interpretable machine learning for large-scale FTIR polymer classification in microplastics research
Scientists developed a new computer program called xpectrass that can automatically identify different types of plastic particles (microplastics) using a special light analysis technique. The program correctly identified plastic types with high accuracy across thousands of samples, which could help researchers better track microplastic pollution in our food, water, and environment. This improved identification system is important because understanding what types of plastics are contaminating our world is a key step in protecting human health from microplastic exposure.
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.
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.
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.
Leveraging deep learning for automatic recognition of microplastics (MPs) via focal plane array (FPA) micro-FT-IR imaging
Researchers developed PlasticNet, a deep learning neural network for identifying microplastics in environmental samples using infrared imaging, achieving over 95% accuracy across 11 common plastic types. The study demonstrates that this approach overcomes challenges posed by surface modifications and additives that make conventional spectral classification difficult.
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.
Deep learning-enabled Inference of 3D molecular absorption distribution of biological cells from IR spectra
Researchers built a deep learning computer model that can reconstruct the 3D internal structure and chemical makeup of tiny biological cells using only infrared light measurements. This near-real-time approach could speed up analysis of biological samples without physically slicing or destroying them.
Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models
Researchers developed a machine learning model that uses images of FTIR spectra to automatically identify chemical functional groups in unknown substances. This approach could speed up the identification of microplastic polymer types in environmental samples, making large-scale monitoring more efficient.
Machine learning powered framework for detection of micro- and nanoplastics using optical photothermal infrared spectroscopy
A machine learning framework was developed to detect and classify micro- and nanoplastics using optical photothermal infrared spectroscopy, addressing the lack of standardized detection methods in the field. The approach improves accuracy and consistency in identifying plastic particles, potentially enabling better monitoring of environmental and human health risks.
Training and evaluating machine learning algorithms for ocean microplastics classification through vibrational spectroscopy
Researchers evaluated multiple machine learning algorithms for automatically classifying ocean microplastics using infrared spectroscopy data across 13 polymer types. The study found that Support Vector Machine classifiers provided the best balance of simplicity and accuracy, offering a practical tool for faster and more reliable identification of microplastic contaminants.
Microplastic Analysis in Soil Using Ultra-High-Resolution UV–Vis–NIR Spectroscopy and Chemometric Modeling
Researchers tested a new method using UV-visible-near infrared spectroscopy combined with machine learning to identify microplastics in soil samples. They found the technique could rapidly and accurately distinguish between different plastic polymers and natural soil particles. The study offers a promising alternative to current labor-intensive identification methods, potentially making large-scale microplastic soil monitoring more practical.
A Universal Approach to Mie Scatter Correction in FTIR Analysis of Microsized Samples
Researchers developed a deep-learning-based method to correct Mie scattering distortions in infrared microspectroscopy, enabling accurate chemical identification of microscopic samples including microplastic beads. The universal approach works across different sample types and spectroscopic setups without requiring prior knowledge of sample absorption properties, offering a significant improvement for microplastic analysis and other applications.
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.
Membrane filter removal in FTIR spectra through dictionary learning for exploring explainable environmental microplastic analysis
Researchers developed a machine learning method to remove the interfering signal from filter membranes in infrared spectra used to identify microplastics, improving classification accuracy by 1.5-fold and maintaining explainability — making it easier to reliably identify plastic types in environmental water samples collected with filters.
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.
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.
Machine learning driven methodology for enhanced nylon microplastic detection and characterization
Researchers combined machine learning with advanced infrared spectroscopy to create a more reliable and standardized way to detect nylon microplastics. When applied to commercial nylon teabags, they found an average of 106 microplastic particles released per bag. This kind of standardized detection method is important because inconsistent measurement techniques have made it difficult to compare microplastic studies and accurately assess human exposure levels.
Enhanced Identification of Weathered Plastics Through the Improvement of Infrared Spectral Libraries
Researchers developed an improved infrared spectral library specifically designed to identify weathered and degraded plastics that conventional libraries often misidentify. The new library increased match rates by 7.3% for thermally oxidized plastics and improved identification of mechanically abraded samples, addressing a significant gap in accurate microplastic detection and environmental risk assessment.
Saccharide concentration prediction from proxy sea surface microlayer samples analyzed via infrared spectroscopy and quantitative machine learning
Researchers developed infrared spectroscopy combined with machine learning to analyze dissolved organic carbon in sea surface microlayer samples more efficiently. The sea surface microlayer is a critical zone where microplastics concentrate and interact with marine chemistry.
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.