We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
FRDA: Fingerprint Region based Data Augmentation using explainable AI for FTIR based microplastics classification
AI summary Read the abstract
Researchers developed a new machine learning approach called FRDA that uses explainable artificial intelligence to improve the accuracy of microplastic identification from infrared spectroscopy data. The method generates realistic synthetic training data focused on the most chemically informative regions of the spectra, helping overcome the problem of limited and imbalanced datasets. The technique significantly improved classification accuracy, especially for difficult-to-identify plastic copolymers and mixtures.
Marine microplastics (MPs) contamination has become an enormous hazard to aquatic creatures and human life. For MP identification, many Machine learning (ML) based approaches have been proposed using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR-FTIR). One major challenge for training MP identification models now is the imbalanced and inadequate samples in MP datasets, especially when these conditions are combined with copolymers and mixtures. To improve the ML performance in identifying MPs, data augmentation method is an effective approach. This work utilizes Explainable Artificial Intelligence (XAI) and Gaussian Mixture Models (GMM) to reveal the influence of FTIR spectral regions in identifying each type of MPs. Based on the identified regions, this work proposes a Fingerprint Region based Data Augmentation (FRDA) method to generate new FTIR data to supplement MP datasets. The evaluation results show that FRDA outperforms the existing spectral data augmentation approaches.
More Papers Like This
Generation of synthetic FTIR spectra to facilitate chemical identification of microplastics
AI summary Read the abstract
Researchers generated synthetic FTIR spectra of microplastics using computational methods to augment training datasets for automated spectral identification algorithms. The synthetic spectra closely matched experimentally measured spectra, and classifiers trained on augmented datasets showed improved accuracy for identifying underrepresented polymer types in real-world samples.
Identification method of microplastics based on Raman-infrared spectroscopy fusion
AI summary Read the abstract
Researchers developed a method that combines Raman and infrared spectroscopy data to improve the identification of microplastics, compensating for the limitations of each technique when used alone. Machine learning was used to process the combined spectral data, improving accuracy for detecting plastics with pigments or additives that interfere with standard measurements.
Machine learning outperforms humans in microplastic characterization and reveals human labelling errors in FTIR data
AI summary Read the abstract
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
AI summary Read the abstract
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.
Membrane filter removal in FTIR spectra through dictionary learning for exploring explainable environmental microplastic analysis
AI summary Read the abstract
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.
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.