Benchmarking Deep Learning Models for Raman Spectroscopy Across Open-Source Datasets

arXiv:2601.16107v2 Announce Type: replace Abstract: Deep learning classifiers for Raman spectroscopy are increasingly reported to outperform classical chemometric approaches. However, their evaluations are often conducted in isolation or compared against traditional machine learning methods or trivially adapted vision-based architectures that were not originally proposed for Raman spectroscopy. As a result, direct comparisons between existing deep learning models developed specifically for Raman spectral analysis on shared open-source datasets remain scarce. In this work, we focus on supervised Raman spectra classification where each spectrum is assigned to a predefined material, bacterial/yeast isolate, drug treatment or pharmaceutical compound. To the best of our knowledge, this study presents one of the first benchmarks comparing three or more published Raman-specific deep learning classifiers across multiple open-source Raman datasets. We evaluate five representative Deep Learning (DL) architectures along with two conventional Machine Learning (ML) methods under a unified training and hyperparameter tuning protocol across three open-source Raman datasets selected to support standard evaluation, fine-tuning, and explicit distribution-shift testing. In this comparative study, we primarily focus on classification because the selected open-source datasets provide classification annotations, while annotations for complete structure elucidation are not available. We report classification accuracies and macro-averaged F1 scores to provide a fair and reproducible comparison of the supervised ML and DL models for Raman spectra based classification.

Abstract:Deep learning classifiers for Raman spectroscopy are increasingly reported to outperform classical chemometric approaches. However, their evaluations are often conducted in isolation or compared against traditional machine learning methods or trivially adapted vision-based architectures that were not originally proposed for Raman spectroscopy. As a result, direct comparisons between existing deep learning models developed specifically for Raman spectral analysis on shared open-source datasets remain scarce. In this work, we focus on supervised Raman spectra classification where each spectrum is assigned to a predefined material, bacterial/yeast isolate, drug treatment or pharmaceutical compound. To the best of our knowledge, this study presents one of the first benchmarks comparing three or more published Raman-specific deep learning classifiers across multiple open-source Raman datasets. We evaluate five representative Deep Learning (DL) architectures along with two conventional Machine Learning (ML) methods under a unified training and hyperparameter tuning protocol across three open-source Raman datasets selected to support standard evaluation, fine-tuning, and explicit distribution-shift testing. In this comparative study, we primarily focus on classification because the selected open-source datasets provide classification annotations, while annotations for complete structure elucidation are not available. We report classification accuracies and macro-averaged F1 scores to provide a fair and reproducible comparison of the supervised ML and DL models for Raman spectra based classification.

20 pages, 4 figures. Substantially revised following peer review including additional baselines, five independent evaluation runs, expanded reproducibility details and confusion matrices. Published in Digital Discovery

Machine Learning (cs.LG)

arXiv:2601.16107 [cs.LG]

(or arXiv:2601.16107v2 [cs.LG] for this version)

https://doi.org/10.1039/D6DD00044D DOI(s) linking to related resources

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Benchmarking Deep Learning Models for Raman Spectroscopy Across Open-Source Datasets

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