RamanPFN: learning from Raman spectral structure with a tabular foundation model

2026-08-03

AI Summary

This study addresses the challenges of scarce labeled data, complex band structures, and difficulty in modeling long-range spectral correlations in Raman spectroscopy prediction. To overcome these issues, the authors propose an explicit spectral representation method that integrates global group unmixing with local vibrational subspace encoding, and for the first time incorporate this representation into the zero-shot context learning framework TabPFN. Without requiring task-specific fine-tuning, the approach simultaneously captures both long-range band dependencies and fine-grained local peak characteristics, enabling reusable high-dimensional spectral inference. Experimental results across 150 tasks demonstrate a 19.6% average reduction in RMSE for regression tasks and a 9.0% decrease in error rate for classification tasks.

Abstract

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. Latent-variable chemometrics accommodates collinear small-sample data but can obscure fine peak morphology, whereas deep spectral networks resolve this structure only after task-specific training. TabPFN avoids task-specific parameter fitting through pretrained in-context inference, but processes very wide inputs as feature-subsampled views that do not preserve joint visibility of related bands. We present RamanPFN, a spectral representation framework that encodes these dependencies before TabPFN inference. Global Compositional Unmixing constructs non-negative coordinates over the complete spectrum so that distant bands with shared latent variation occupy a common predictive axis. Local Vibrational Subspace Encoding represents contiguous wavenumber regions with multiple orthogonal modes that retain independent changes in peak shape, intensity and position. The representations are evaluated separately and combined at the prediction level. Evaluation covered 150 tasks from 74 public Raman datasets. RamanPFN reduced root-mean-square error by 19.6% on average across 129 regression targets relative to direct TabPFN inference and further reduced the remaining classification error by 9.0% across 21 classification tasks. These results establish explicit spectral representation as an effective interface between high-dimensional Raman measurements and reusable tabular inference.

Problem

• Raman spectroscopy
• spectral representation
• tabular foundation model
• peak morphology
• latent-variable chemometrics

Innovation

• RamanPFN
• Global Compositional Unmixing
• Local Vibrational Subspace Encoding
• tabular foundation model
• spectral representation