Projects per year
Abstract
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 each observed spectrum into a series of peaks plus a smoothlyvarying baseline, corrupted by additive white noise. The peaks are modelled as Lorentzian, Gaussian, or pseudoVoigt functions, while the baseline is estimated using a penalised cubic spline. This latent continuous representation accounts for differences in resolution between measurements. The posterior distribution can be incrementally updated as more data becomes available, resulting in a scalable algorithm that is robust to local maxima. By incorporating this representation in a Bayesian hierarchical regression model, we can quantify the relationship between molecular concentration and peak intensity, thereby providing an improved estimate of the limit of detection, which is of major importance to analytical chemistry.
Original language  English 

Journal  Annals of Applied Statistics 
Publication status  Accepted/In press  24 Jan 2018 
Keywords
 chemometrics
 functional data analysis
 multivariate calibration
 nanotechnology
 sequential Monte Carlo
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Projects
 1 Finished

In Situ Nanoparticle Assemblies for Healthcare Diagnostics and Therapy
EPSRC (Engineering and Physical Sciences Research Council)
1/05/14 → 31/10/18
Project: Research
Datasets

Data for: "Bayesian Modelling and Quantification of Raman Spectroscopy"
Gracie, K. (Creator), Moores, M. (Contributor), Carson, J. (Contributor), Girolami, M. A. (Supervisor), Graham, D. (Supervisor), Faulds, K. (Supervisor) & Mabbott, S. (Other), University of Strathclyde, 21 Feb 2019
DOI: 10.15129/6bce0b39fca7499fbcc2a105b3834f4e
Dataset