The paper presents a physics‑aware, multi‑domain self‑supervised learning framework for Raman spectroscopy that uses a masked autoencoder with a sparse Voigt‑profile decoder and a denoising objective. It learns transferable spectral representations from diverse biological, chemical, forensic, and materials datasets, and demonstrates improved performance on substance classification, component identification, concentration estimation, and spectral denoising. The study shows that self‑supervised pretraining is especially beneficial for tasks with limited labeled data, that attention‑based probing reveals richer information than linear probing, and that larger encoders benefit more from pretraining.
By Charalampos Davalas, Georgios Levantinos, Anastasios Dimitriou, Nikolaos Papanikolaou, Georgios Th. Papadopoulos, Dimitrios Michail
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
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.
The paper introduces a wavenumber-aware deep learning approach that represents each Raman spectral point as an intensity–wavenumber pair and uses Transformer positional encodings derived from the wavenumber. This method remains robust to noise, sparse sampling, and partial spectral coverage across various neural network architectures, outperforming index-based and learned encodings when only part of the spectrum is observed. The approach was validated on Raman spectra from three bacterial species, their synthetic mixtures, and a public 30-isolate bacterial benchmark, demonstrating consistent performance on unseen sampling grids.
By Jihwan Kim, Jaeseong Heo, Yujin Kim, Sang Bin Yoon, Chanjoong Kim, Kwang-Im Oh, Hyon Bin Na, Kyungwon Kwak, Myeongkee Park, Joonyoung F. Joung
arXiv:2605.02003v3 Announce Type: replace
Abstract: Machine Learning (ML) has transformed many scientific fields, yet key applications still lack standardized benchmarks. Raman spectroscopy, a widely...
By Mario Koddenbrock, Christoph Lange, Robin Legner, Martin J\"ager, Martin K\"ogler, Mariano N. Cruz Bournazou, Peter Neubauer, Felix Biessmann, Erik Rodner
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 iPINN, an inverse physics‑informed neural network designed for broadband coherent anti‑Stokes Raman spectroscopy (BCARS) phase retrieval. iPINN predicts Lorentzian peak parameters from raw BCARS spectra and reconstructs the resonant susceptibility using a differentiable analytical forward model, employing a transformer encoder and a multi‑view consistency loss to handle varying non‑resonant background conditions. On a public benchmark it achieves the lowest mean absolute error (0.016) compared to other methods, and demonstrates depth‑invariant accuracy across multiple solvents and focal positions.
By Ravi Teja Vulchi, Carl Messerschmidt, Mohammadsadegh Vafaeinezhad, Rajendhar Junjuri, Tobias Meyer-Zedler, Juergen Popp, Thomas Bocklitz
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
DeepSciReasoner is a new framework for predicting scientific spectra that respects hard physical constraints such as conservation laws and spectral geometry. By combining structure‑aware decoding with constraint‑preserving reasoning, it can accurately predict electronic density of states, phonon density of states, XANES, and Raman spectra while maintaining physical consistency. The authors demonstrate substantial improvements in prediction accuracy across these diverse spectral types.
By Yingheng Wang, Tao Yu, Shufeng Kong, Francesco Ricci, John M. Gregoire, Carla P. Gomes
arXiv:2610.02224v1 Announce Type: new
Abstract: Rapid and reliable identification of pharmaceutical residues is important for safeguarding public health, ensuring food safety, and enabling practical...
By Quach Thi Thai Binh, Ton Nu Quynh Trang, Thang B. Phan, Vu Thi Hanh Thu, Nguyen Tuan Hung
arXiv:2606. 27096v1 Announce Type: new Abstract: Transformer-based models have recently attracted increasing attention for Raman spectral classification.
By Jamile Mohammad Jafari, Thomas Bocklitz
arXiv:2607. 27645v1 Announce Type: cross Abstract: Wavelength selection is one of the important preprocessing methods in near-infrared spectroscopy to improve prediction accuracy and interpretability of spectral data.
By Mitsunobu Kanebako, Ami S. Koshikawa, Masaru Hitomi, Takuro Tanaka, Mahito Chiba, Maiko Mori, Masayuki Ohzeki