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serrsBayes

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 Moores et al. (2026) “Bayesian modelling and quantification of Raman spectroscopy.,” published in the 2025 MATRIX Annals, Part II (Springer). 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).

Installation Instructions

Stable releases, including binary packages for Windows & Mac OS, are available from CRAN:

https://CRAN.R-project.org/package=serrsBayes

install.packages("serrsBayes")

The current development version can be installed from GitHub:

devtools::install_github("mooresm/serrsBayes")

Example Usage

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)

Simulated Raman spectrum from 700 to 1400 per cm with five peaks at 840, 960, 1140, 1220 and 1290 per cm, the largest at 840. The noisy observed spectrum (black) closely follows the true signal plus baseline (blue, dashed), and the true baseline (red, dash-dot) rises smoothly from about 500 to 3500.

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 
#> 164.263   1.925  43.341
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")
}

The same simulated spectrum (black) overlaid with 200 posterior samples of the fitted spectrum (blue) and the estimated baseline (red). The samples form narrow bands that follow all five peaks and the smoothly rising baseline, showing that the model recovers both components.

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.