2016/04/25 by Matthew T. Moores, Kirsten Gracie, Moores, Matthew +9
Biochemistry, Genetics and Molecular Biology · Chemistry · Decision Sciences · #62F15 #62H12 #65D10 #92E99 #Advanced Statistical Process Monitoring #Applications (stat.AP) #Computation (stat.CO) #FOS: Computer and information sciences #Spectroscopy Techniques in Biomedical and Chemical Research #Spectroscopy and Chemometric Analyses
paper · pdf · doi:10.48550/arxiv.1604.07299
openalex publication_date 2016/04/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Raman spectroscopy can be used to identify molecules such as DNA by the characteristic scattering of light from a laser. 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. To overcome this challenge, 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. The peaks are modelled using Lorentzian or Gaussian broadening functions, while the baseline is estimated using a penalised cubic spline. This latent continuous representation accounts for differences in resolution between measurements. By incorporating this representation in a Bayesian model, we can quantify the relationship between molecular concentration and peak intensity, thereby providing an improved estimate of the limit of detection (LOD), which is of major importance in analytical chemistry.