We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Random forest microplastic classification using spectral subsamples of FT-IR hyperspectral images
AI summary Read the abstract
A random forest classification model was built using spectral subsamples of FT-IR hyperspectral data to rapidly identify the 11 most common environmental microplastic polymer types. By selecting highly discriminative wavenumbers, the model reduced computational load while maintaining strong classification accuracy.
In this work, a random decision forest model is built for fast identification of Fourier-transform infrared spectra of the eleven most common types of microplastics in the environment. The random decision forest input data is reduced to a combination of highly discriminative single wavenumbers selected using a machine learning classifier. This dimension reduction allows input from systems with individual wavenumber measurements, and decreases prediction time. The training and testing spectra are extracted from Fourier-transform infrared hyperspectral images of pure-type microplastic samples, automatizing the process with reference spectra and a fast background correction and identification algorithm. Random decision forest classification results are validated using procedurally generated ground truth. The classification accuracy achieved on said ground truths are not expected to carry over to environmental samples as those usually contain a broader variety of materials.
More Papers Like This
Random Forest single channel classifier for FTIR Microplastic images
AI summary Read the abstract
Researchers applied random decision forest machine learning to identify microplastics from Fourier-transform infrared (FTIR) spectral data, using a small number of highly informative wavelengths to classify particles accurately and rapidly. Faster automated spectral classification could significantly increase the throughput of microplastic analysis in environmental monitoring.
Development of robust models for rapid classification of microplastic polymer types based on near infrared hyperspectral images
AI summary Read the abstract
Researchers used near-infrared hyperspectral imaging combined with machine learning to classify nine types of microplastic particles, finding reliable results even for small particles on wet filters. This method could enable faster, automated identification of diverse microplastic types in environmental water samples.
Spectral Classification of Large-Scale Blended (Micro)Plastics Using FT-IR Raw Spectra and Image-Based Machine Learning
AI summary Read the abstract
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.
Exploratory analysis of hyperspectral FTIR data obtained from environmental microplastics samples
AI summary Read the abstract
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.
Microplastic
AI summary Read the abstract
This FTIR hyperspectral image dataset shows a plankton environmental sample spiked with microplastics of five polymer types (PE, PP, PS, PMMA, PAN) in the 10-200 micrometer size range, used to develop and train random decision forest classifiers for automated microplastic identification. The dataset supports machine learning approaches to faster and more consistent microplastic detection in complex biological samples.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.