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serrsBayes provides model-based quantification of
surface-enhanced resonance Raman spectroscopy (SERRS) using sequential
Monte Carlo (SMC) algorithms. The details of the Bayesian model and
informative priors are provided in the arXiv preprint, Moores et
al. (2016; v2 2018) “Bayesian
modelling and quantification of Raman spectroscopy.” Development of
this software was supported by the UK Engineering & Physical
Sciences Research Council (EPSRC) programme grant “In Situ
Nanoparticle Assemblies for Healthcare Diagnostics and Therapy”
(ref: EP/L014165/1).
Stable releases, including binary packages for Windows & Mac OS, are available from CRAN:
install.packages("serrsBayes")The current development version can be installed from GitHub:
devtools::install_github("mooresm/serrsBayes")To simulate a synthetic Raman spectrum with known parameters:
set.seed(1234)
library(serrsBayes)
wavenumbers <- seq(700,1400,by=2)
spectra <- matrix(nrow=1, ncol=length(wavenumbers))
peakLocations <- c(840, 960, 1140, 1220, 1290)
peakAmplitude <- c(11500, 2500, 4000, 3000, 2500)
peakScale <- c(10, 15, 20, 10, 12)
signature <- weightedLorentzian(peakLocations, peakScale, peakAmplitude, wavenumbers)
baseline <- 1000*cos(wavenumbers/200) + 2*wavenumbers
spectra[1,] <- signature + baseline + rnorm(length(wavenumbers),0,200)
plot(wavenumbers, spectra[1,], type='l', xlab=expression(paste("Raman shift (cm"^{-1}, ")")), ylab="Intensity (a.u.)")
lines(wavenumbers, baseline, col=2, lty=4)
lines(wavenumbers, baseline + signature, col=4, lty=2, lwd=2)
Fit the model using SMC:
lPriors <- list(scale.mu=log(11.6) - (0.4^2)/2, scale.sd=0.4, bl.smooth=10^11, bl.knots=50,
beta.mu=5000, beta.sd=5000, noise.sd=200, noise.nu=4)
tm <- system.time(result <- fitSpectraSMC(wavenumbers, spectra, peakLocations, lPriors))Sample 200 particles from the posterior distribution:
print(tm)
#> user system elapsed
#> 257.355 3.859 286.824
samp.idx <- sample.int(length(result$weights), 200, prob=result$weights)
plot(wavenumbers, spectra[1,], type='l', xlab=expression(paste("Raman shift (cm"^{-1}, ")")), ylab="Intensity (a.u.)")
for (pt in samp.idx) {
bl.est <- result$basis %*% result$alpha[,1,pt]
lines(wavenumbers, bl.est, col="#C3000009")
lines(wavenumbers, bl.est + result$expFn[pt,], col="#0000C309")
}
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.