Probabilistic signal estimation for vibrational spectroscopy with a flexible non-stationary Gaussian process baseline model

Vibrational spectroscopy techniques enable accurate chemical detection and quantification, but the extraction of spectral peak parameters is frequently hampered by an underlying baseline. Because the signal and baseline are additive, it is difficult to distinguish between signal peaks and baseline effects when the baseline is not smooth. Using surface enhanced Raman spectroscopy (SERS) and near-infrared (NIR) spectroscopy as examples, we show how to estimate the signal and the baseline jointly while imposing a high-capacity non-stationary Gaussian process on the baseline. This allows us to both obtain accurate estimation and meaningful uncertainty estimates on interpretable peak parameters. We demonstrate this on artificially generated SERS maps, a challenging real-world SERS case, and a benchmark NIR dataset.