Sharpness-Aware Minimization (SAM) is applied to improve the generalization of machine learning models for classifying bacterial Raman spectra, a technique that could enable rapid, portable diagnostics for antimicrobial resistance. The study shows that SAM can increase classification accuracy by up to 10.5% on a single data split and by an average of 2.7% across multiple splits compared to the traditional Adam optimizer. These gains demonstrate SAM’s potential to enhance the clinical utility of AI-powered Raman spectroscopy tools.
By Kaitlin Zareno, Jarett Dewbury, Siamak K. Sorooshyari, Hossein Mobahi, Loza F. Tadesse
arXiv:2606. 14169v1 Announce Type: new Abstract: Raman spectroscopy provides label-free, chemically specific characterization of biological systems and has become an important tool for cancer diagnosis, molecular subtyping, microbiological identification, and intraoperative decision support.
By Bogdan Oancea, Ana Maria Seciu-Grama, Nicoleta Siminea, Laura Mihaela Stefan, Alice Stoica, Joel Sjoberg, Marian Necula, Ana-Maria Prelipcean, Corneliu Ovidiu Vrancianu, Eduard Milea, Andrei P\u{a}un, Ion Petre, Mihaela P\u{a}un
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.
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 a Raman spectroscopy and machine‑learning framework that uses t‑SNE, K‑means, Decision Trees, and NNLS‑based spectral decomposition to authenticate edible oils. In pure oils, Decision Trees achieved 100% accuracy using only four Raman variables out of 1866 features, while in matrix‑containing samples, NNLS‑based PI‑AI improved classification to about 85–86% with only four to five key variables. The approach yields highly compact, interpretable spectral representations that enable accurate oil identification with minimal data footprint.
By Amrita Shaw, Chandrasekar S. N., Sai Muthukumar V., Jhinuk Gupta, Deepak L. N. Kallepalli
arXiv:2607. 10196v1 Announce Type: new Abstract: Access to sufficiently large biomedical datasets remains a major obstacle for machine learning in Raman spectroscopy-based diagnostics.
By Andrei Iu\c{s}an, Iulian Vasile, Daria Voiculescu, Ion Petre, Andrei P\u{a}un, Bogdan Oancea, Mihaela P\u{a}un
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
Fruit ripeness prediction (FRP) is a classification-based agricultural computer vision task that has attracted much attention, thanks to its wide-ranging advantages in agriculture field for both pre-harvest and post-harvest management. Accurate and timely FRP can be achieved using machine/deep learning-based hyperspectral image classification techniques.
arXiv:2608. 01202v1 Announce Type: cross Abstract: Fruit ripeness prediction (FRP) is a classification-based agricultural computer vision task that has attracted much attention, thanks to its wide-ranging advantages in agriculture field for both pre-harvest and post-harvest management.
By Ahmed Baha Ben Jmaa, Faten Chaieb, Anna Fabija\'nska
arXiv:2606. 13978v1 Announce Type: cross Abstract: This paper evaluates a signal-processing and supervised-learning pipeline for classifying SDSS DR17 astronomical spectra into stars, galaxies, and quasars.
By Bruno Santos Meneses Barreto, Marcio Eisencraft
Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging. Conventional interpretation is labor-intensive, relies on prior knowledge and reference spectra, and is difficult to scale, whereas most machine-learning methods are tailored to individual tasks or datasets, require large labeled training sets, and transfer poorly across analytical objectives and experimental datasets.
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.