arXiv:2606. 06554v1 Announce Type: cross Abstract: Reliable polymer identification is essential for ensuring the quality and safety of recycled plastics, yet conventional sorting and spectroscopic techniques often struggle to deliver robust discrimination.
By Roshni Mahtani, Il\'an Carretero, Laura Monroy, Aldo Moreno-Oyervides, Oscar El\'ias Bonilla-Manrique, Roc\'io del Amor
Raman spectroscopy enables non-destructive, label-free molecular characterization across materials science, biomedicine and process monitoring. Predictive Raman datasets often contain few labelled spectra and thousands of ordered wavenumbers, with informative variation within bands and across distant spectral regions.
Hyperspectral imaging (HSI) offers nondestructive assessment of fish freshness by detecting biochemical alterations across spectral bands. However, conventional deep learning approaches do not fully address the particular characteristics of HSI data, such as spectral dominance over spatial textures, ordinal label structure, and a small number of training samples.
arXiv:2608. 12227v1 Announce Type: cross Abstract: Hyperspectral imaging (HSI) offers nondestructive assessment of fish freshness by detecting biochemical alterations across spectral bands.
By Kazi Nabiul Alam, Pooneh Bagheri Zadeh, Akbar Sheikh-Akbari
arXiv:2608. 02157v1 Announce Type: new Abstract: Raman spectroscopy enables non-destructive, label-free molecular characterization across materials science, biomedicine and process monitoring.
By Xingyu Pan, Huan Wang, Jinjia Guo, Zhenlin Zhao, Siming Dong, Jixi Lu
The paper presents the first publicly available hyperspectral imaging (HSI) dataset of shredded black plastics from end‑of‑life vehicle waste, covering four industrial polymers across RGB, VNIR, SWIR, and MWIR modalities. It introduces a multi‑modal spectral‑spatial framework that combines foreground isolation, pixel‑wise classification, and object‑level majority voting, leveraging hyperspectral transformers and chemometric band selection to accurately classify complex black plastics. The study benchmarks nine processing methods—including chemometric, machine learning, and deep learning architectures—providing a reproducible, comprehensive benchmark for industrial hyperspectral object analysis.
By Elias Arbash, Andr\'ea de Lima Ribeiro, Filipa Sim\~oes, Ahmed Jamal Afifi, Aldino Rizaldy, Yuleika Madriz, Samuel Thiele, Sandra Lorenz, Margret Fuchs, Pedram Ghamisi, Paul Scheunders, Richard Gloaguen