R/SPC_DSM.R

In NMRphasing: Phase Error Correction and Baseline Correction for One Dimensional ('1D') 'NMR' Data

#' SPC_DSM
#' @description A single linear model with dispersion summation minimization.
#' @details This function is to process phase error correction through a single linear model with dispersion summation minimization,
#' followed by polynomial baseline correction if necessary
#' @param specdat A complex number vector of observed frequency domain data
#' @param withBC A logical parameter that enables/disables baseline correction after baseline correction
#' @return A numeric vector of phase corrected absorption spectrum
#' @concept phase correction
#' @author Aixiang Jiang
#' @references
#'
#' Binczyk, F., Tarnawski, R., & Polanska, J. (2015). Strategies for optimizing the phase correction algorithms in Nuclear Magnetic Resonance spectroscopy. Biomedical Engineering Online, 14 Suppl 2(Suppl 2), S5. https://doi.org/10.1186/1475-925X-14-S2-S5
#'
#' Liland KH, Almøy T, Mevik B (2010), Optimal Choice of Baseline
#' Correction for Multivariate Calibration of Spectra, Applied Spectroscopy 64, pp. 1007-1016.

#' @import baseline
#'
#' @examples
#' data("fdat")
#' spc_dsm_phased1 <- SPC_DSM(fdat$frequency_domain)

#' @export


SPC_DSM = function (specdat, withBC = TRUE){

  hdat=cbind(Re(specdat), Im(specdat))

  pspec=hdat[,1]**2+hdat[,2]**2
  maxi=which.max(pspec)
  ph0Initial = -atan2(hdat[maxi,2],hdat[maxi,1])
  ph1Initial=0.005

  #### get optimized parameters of ph0 and ph1
  optimRes=stats::optim(par=c(ph0Initial,ph1Initial),fn=sumD, specDat=hdat)

  bestPh=optimRes$par

  nn=dim(hdat)[1]
  angles=bestPh[1]+bestPh[2]*c(1:nn)/nn

  dat3col=cbind(hdat, angles)
  phasedDat=t(apply(dat3col, 1, phaseCorr2))

  phasedAll = phasedDat[,1]

  if(withBC == TRUE){
    tryBL=myBaseline(phasedAll,bsDf=5, BL_method="modpolyfit")
    phasedAll = as.numeric(tryBL)
  }

  return(phasedAll)

}