Deep Convolutional Neural Networks for Aged Microplastics Identification by Fourier Transform Infrared Spectra Classification

Deep Learning Approaches for Microplastic Identification in Microscopic Water Samples

Characterization of Microplastics Using Fourier Infrared Spectroscopy

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Researchers applying Fourier transform infrared spectroscopy to microplastic characterization demonstrated its effectiveness as an analytical tool for identifying polymer types and chemical composition, providing a robust method for environmental monitoring of microplastic contamination.

Advanced Microplastic Identification in Marine Environments via Hybrid Deep Learning

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Researchers propose a hybrid deep learning architecture combining 3D convolutional neural networks and Vision Transformers applied to hyperspectral imagery to detect and classify microplastics in turbid marine environments, capturing both local spectral signatures and global contextual patterns that single-model approaches miss.

Enhancing microplastic classification through filter-interfered FTIR spectra using dimensionality reduction and deep learning in low-dimensional spaces

Microplastic Characterization by Infrared Spectroscopy

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Infrared spectroscopy is an indispensable tool for characterizing the chemical composition of microplastics, enabling identification of polymer types across diverse environmental samples. Accurate chemical characterization is foundational to realistic risk assessment, as different polymer types carry different toxicological profiles and environmental persistence.