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Papers
61,005 resultsShowing papers similar to FTIR-Based Microplastic Classification: A Comprehensive Study on Normalization and ML Techniques
ClearA 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.
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.
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.
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.
Optimized recognition of microplastic ATR-FTIR spectra with deep learning
Researchers developed an optimized deep learning method for identifying microplastics from ATR-FTIR spectra, improving classification accuracy for weathered and environmentally contaminated MP samples that challenge standard spectral library matching approaches.
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.
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.
Spectral Classification of Large-Scale Blended (Micro)Plastics Using FT-IR Raw Spectra and Image-Based Machine Learning
Researchers developed and compared four machine learning classifiers for identifying microplastic types from Fourier transform infrared spectroscopy data using large-scale blended plastic datasets. The study found that a 1D convolutional neural network achieved the best overall accuracy at over 97%, outperforming decision tree and random forest models, offering a scalable alternative to traditional library-search methods for microplastic identification.
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.
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.
Enhancing microplastic classification through filter-interfered FTIR spectra using dimensionality reduction and deep learning in low-dimensional spaces
Researchers developed a method to improve microplastic classification from FTIR spectra that are interfered with by filter backgrounds, using deep learning to extract polymer-specific spectral features even when filter absorption overlaps with plastic signatures, improving accuracy for environmental samples.
Identification of Polymers with a Small Data Set of Mid-infrared Spectra: A Comparison between Machine Learning and Deep Learning Models
Researchers compared multiple machine learning and deep learning models for identifying polymer types from mid-infrared spectral data using a small reference dataset, finding that certain deep learning architectures outperformed traditional methods even with limited training examples, supporting automated microplastic identification.
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.
Application of a Hybrid Fusion Classification Process for Identification of Microplastics Based on Fourier Transform Infrared Spectroscopy
A hybrid machine learning approach was developed to improve the identification of microplastics using infrared spectroscopy, overcoming the limitations of standard library-matching methods. The new method better handles weathered or contaminated microplastic particles, which are harder to identify using conventional approaches.
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.
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.
Development of a machine‐learning model for microplastic analysis in an FT‐IR microscopy image
Researchers developed a machine-learning model using a 1D convolutional neural network to classify FT-IR microscopy spectra of microplastics into 16 polymer types. The model addresses inaccuracies caused by secondary materials on real environmental samples, improving the speed and reliability of automated microplastic identification.
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.
Deep convolutional neural networks for aged microplastics identification by Fourier transform infrared spectra classification
This study developed a deep learning model using convolutional neural networks to automatically identify aged microplastics from their infrared spectra. Aging changes the chemical signature of plastics, making them harder to identify with conventional spectral databases. The AI approach achieved high accuracy and could significantly speed up the analysis of environmental samples where weathered microplastics are the norm.
A Hybrid MIR-spectrum Processing Algorithm for Microplastics Analysis
Researchers developed a hybrid algorithm for classifying microplastics using their mid-infrared spectral signatures, targeting polypropylene, polyethylene, and polystyrene. The model combines principal component analysis with machine learning techniques to improve classification accuracy. The study offers an automated approach that could make routine microplastic identification faster and more reliable for environmental monitoring.
Deep Learning for Reconstructing Low-Quality FTIR and Raman Spectra─A Case Study in Microplastic Analyses
Researchers developed a deep learning method to reconstruct low-quality FTIR and Raman spectra, demonstrating its effectiveness for automated microplastic analysis where rapid measurement workflows produce noisy, challenging spectral datasets.
The Identification and Classification of Microplastics by FTIR Using Gaussian Mixture and Naive Bayes
Researchers developed a machine learning approach using Gaussian Mixture models and Naive Bayes classification to automate the identification and classification of microplastics from FTIR spectral data, addressing the challenge of variable-length spectral outputs. The method successfully standardized data preprocessing to equal-length inputs and achieved high classification accuracy, offering a tool to support and accelerate manual polymer identification.
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.