Bayesian modelling and quantification of Raman spectroscopy
Raman spectroscopy is a technique for detecting and identifying molecules such as DNA. It is sensitive at very low concentrations and can accurately quantify the amount of a given molecule in a sample. The presence of a large, nonuniform background presents a major challenge to analysis of these spectra. We introduce a sequential Monte Carlo (SMC) algorithm to separate the observed spectrum into a series of peaks plus a smoothly-varying baseline, corrupted by additive white noise. Our model-based approach accounts for differences in resolution and experimental conditions. By incorporating this representation into a Bayesian functional regression, we can quantify the relationship between molecular concentration and peak intensity, resulting in an improved estimate of the limit of detection. We also calculate the model evidence using SMC to investigate long-range dependence between peaks.