Raman Spectroscopy

Raman spectroscopy measures shifts in scattered laser light that reveal molecular vibrations, providing chemical fingerprints for applications such as disease detection, bacterial identification, and material analysis. Analysis methods range from preprocessing and chemometrics such as PCA and clustering to neural classifiers, spectral unmixing, and physics-informed inverse models. Current work also explores transformers and foundation models that combine local peak patterns with relationships across a full spectrum, evaluated on classification and regression tasks. A central challenge is whether models generalize across instruments, samples, and datasets; clinical use additionally requires reproducible measurement, external validation, and interpretable results.