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Papers
20 resultsShowing papers similar to Building an ensemble learning model for gastric cancer cell line classification via rapid raman spectroscopy
ClearRecent Advances in Raman Spectral Classification with Machine Learning
This review summarized recent advances in applying machine learning to Raman spectral classification, addressing the challenges of weak signals, complex spectra, and high-dimensional data that limit traditional chemometric methods. The advances have significant implications for automated, high-throughput microplastic polymer identification.
Identification of extracellular vesicles from their Raman spectra via self-supervised learning
Researchers developed a deep learning method to identify and classify tiny biological particles called extracellular vesicles — which cells release and which may signal disease — using Raman spectroscopy without any chemical labels. The model achieved over 92% accuracy in distinguishing vesicles from different biological sources, including cancer patients versus healthy controls.
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.
Rapid identification of microplastic using portable Raman system and extra trees algorithm
Researchers developed a portable Raman spectroscopy system combined with a machine learning algorithm to rapidly identify and classify different types of microplastics in the field. Portable real-time identification tools are important for environmental monitoring programs that need to quickly characterize microplastics without sending samples to a laboratory.
Multi Analyte Concentration Analysis of Marine Samples Through Regression Based Machine Learning
Researchers used Raman spectroscopy combined with machine learning to identify concentrations of multiple chemical compounds in marine water samples. The study demonstrates that this approach offers a low-cost, portable method for monitoring ocean chemistry, which is relevant for understanding environmental health in marine ecosystems.
Characterization and identification of microplastics using Raman spectroscopy coupled with multivariate analysis
Researchers developed a new method using Raman spectroscopy combined with machine learning to identify and classify seven types of microplastics with over 98% accuracy for most polymer types. The approach was also able to correctly identify real-world microplastic samples from snack boxes, water bottles, juice bottles, and medicine vials. This technique could make microplastic detection faster and more reliable compared to manual analysis methods.
Classification of household microplastics using a multi-model approach based on Raman spectroscopy
Researchers developed a machine learning approach combined with Raman spectroscopy to identify and classify microplastics commonly found in household products. By using multiple models together, they achieved over 98% accuracy in identifying seven types of standard and real-world microplastic samples, even after environmental weathering. This multi-model approach could provide a faster, more reliable tool for detecting and monitoring microplastic contamination in everyday settings.
Deep learning analysis for rapid detection and classification of household plastics based on Raman spectroscopy
Researchers developed a deep learning system that can identify eight common household plastic types using Raman spectroscopy with 97% accuracy. This is faster and more reliable than traditional methods for classifying plastics. Better plastic identification tools like this are important for microplastic research because they allow scientists to quickly determine what types of plastic particles are contaminating environmental and food samples.
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.
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.
Customizable Machine-Learning Models for Rapid Microplastic Identification Using Raman Microscopy
A strategy for building customizable machine learning models for Raman spectroscopy-based microplastic identification was developed using a high-resolution full-window spectral database, enabling generation of random forest, K-nearest neighbor, and neural network classifiers that remain accurate despite equipment variability. The approach addresses a key barrier to developing shared analytical tools across research groups using different instruments.
Raman Spectroscopy and Machine Learning for Microplastics Identification and Classification in Water Environments
Researchers combined Raman spectroscopy with machine learning algorithms for automated identification and classification of microplastics in water environments, achieving high accuracy in distinguishing different polymer types based on spectral fingerprints.
Raman Spectroscopy Enhanced By Machine Learning For Effective Microplastic Detection In Aquatic Systems
Researchers explored combining Raman spectroscopy with machine learning techniques to improve microplastic detection and classification in aquatic systems. The study found that deep learning models, particularly convolutional neural networks, achieved high classification accuracy and significantly reduced reliance on labor-intensive manual spectral analysis for real-time environmental monitoring.
Label-Free Human Disease Characterization through Circulating Cell-Free DNA Analysis Using Raman Spectroscopy
Researchers used Raman spectroscopy to characterize the biomolecular profile of circulating cell-free DNA (ccfDNA) from healthy individuals and patients with cancer and diabetes, establishing reference Raman spectra for health and disease states. The label-free approach shows potential as a diagnostic tool for detecting disease-specific molecular signatures in liquid biopsy samples.
Rapid classification of micro-particles using multi-angle dynamic light scatting and machine learning approach
Researchers developed a prototype instrument using multi-angle dynamic light scattering combined with machine learning to enable rapid classification of micro-particles, demonstrating its potential for applications in biomedical diagnostics and materials science. The approach achieved accurate particle differentiation without requiring complex sample preparation, offering a faster alternative to conventional particle characterization methods.
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.
Label-free human-disease characterization through circulating cell free DNA analysis using Raman Spectroscopy
Not relevant to microplastics — this study uses Raman spectroscopy to analyse cell-free DNA in blood as a diagnostic tool for cancer and diabetes, with no connection to microplastic research.
Imaging and spectroscopic analysis of pathogens in water, and their classification with machine learning algorithms
Researchers developed an integrated approach for automated classification of cyanobacterial pathogens in water using dark-field illumination imaging combined with Raman spectroscopy, with machine learning algorithms applied for rapid species identification. The system aims to reduce pathogen detection times in water quality monitoring compared to conventional culture-based methods.
Rapid Identification of Plastic Beverage Bottles by Using Raman Spectroscopy Combined With Machine Learning Algorithm
Researchers collected 40 commercial plastic beverage bottles, recorded their Raman spectra, and used a convolutional neural network to classify them into PET, PE, and three PET subcategories. Spectral preprocessing combined with the CNN model enabled rapid and accurate identification of bottle polymer types, demonstrating the potential for Raman spectroscopy with machine learning in forensic and environmental plastic characterization.
Raman Spectroscopic Imaging of Human Bladder Resectates towards Intraoperative Cancer Assessment
Researchers used Raman spectroscopy imaging to distinguish between healthy and cancerous human bladder tissue without the need for chemical stains or labels. The technique successfully identified cancer regions in tissue samples from ten patients, using advanced data analysis to map molecular differences. While not directly related to microplastics, this spectroscopy method is also used in microplastic research and demonstrates the power of label-free chemical imaging in medical applications.