  Raman spectroscopy is an effective, low-cost, non-intrusive technique often
used for chemical identification. Typical approaches are based on matching
observations to a reference database, which requires careful preprocessing, or
supervised machine learning, which requires a fairly large number of training
observations from each class. We propose a new machine learning technique for
Raman spectrum matching, based on contrastive representation learning, that
requires no preprocessing and works with as little as a single reference
spectrum from each class. On three datasets we demonstrate that our approach
significantly improves or is on par with the state of the art in prediction
accuracy, and we show how to compute conformal prediction sets with specified
frequentist coverage. Based on our findings, we believe contrastive
representation learning is a promising alternative to existing methods for
Raman spectrum matching.
