R/baseline.fillPeaks.R

In baseline: Baseline Correction of Spectra

## $Id: baseline.fillPeaks.R 170 2011-01-03 20:38:25Z bhm $


#' @title Fill peaks
#' 
#' @description An iterative algorithm using suppression of baseline by means in local
#' windows
#' 
#' @details In local windows of buckets the minimum of the mean and the previous
#' iteration is chosen as the new baseline
#' 
#' @aliases baseline.fillPeaks fillPeaks
#' @param spectra Matrix with spectra in rows
#' @param lambda 2nd derivative penalty for primary smoothing
#' @param hwi Half width of local windows
#' @param it Number of iterations in suppression loop
#' @param int Number of buckets to divide spectra into
#' @return \item{baseline }{Matrix of baselines corresponding to spectra
#' \code{spectra}} \item{corrected }{Matrix of baseline corrected spectra}
#' @author Kristian Hovde Liland and Bjørn-Helge Mevik
#' @references Kristian Hovde Liland, 4S Peak Filling - baseline estimation by
#' iterative mean suppression, MethodsX 2015
#' @keywords baseline spectra
#' @examples
#' 
#' data(milk)
#' bc.fillPeaks <- baseline(milk$spectra[1,, drop=FALSE], lambda=6,
#' 	hwi=50, it=10, int=2000, method='fillPeaks')
#' \dontrun{
#' 	plot(bc.fillPeaks)
#' }
#' @export
baseline.fillPeaks <- function(spectra, lambda, hwi, it, int){
  ## Iterative baseline correction algorithm based on mean suppression
  ## By Kristian Hovde Liland

  # INPUT:
  # spectra - rows of spectra
  # lambda  - 2nd derivative penalty of primary smoothing
  # hwi     - half width of local window
  # it      - number of iterations in suppression loop
  # int     - number of bucket intervals to make of data (or vektor of bucket boundaries)
  #
  # OUTPUT:
  # baseline  - estimated baseline
  # corrected - baseline corrected spectra
  
  # Initialization
  np <- dim(spectra)
  if (missing(int)) int <- np[1]-1
  baseline  <- matrix(0,np[1],np[2])
  
  # Empty matrix (5 x p)
  W <- matrix(0.0, 5, np[2])
  
  # Diagonal sparse matrix in compact format (5 x p)
  DD <- .create_DD(np[2])*10^lambda
  DD[3,] <- DD[3,] + 1

  # ------==== S1: Smoothing ====------
  Yorig   <- spectra
  if(lambda > 0){
    spectra <- t(Solve.banded(DD,2,2,t(spectra)))
  }
  
  # Exponential decrease in interval width
  if(it != 1){
    d1 <- log10(hwi)
    d2 <- 0
    w <- ceiling((10)^c(d1+(0:(it-2))*(d2-d1)/(floor(it)-1), d2))
  } else {
    w <- hwi
  }

  # Compute bucket locations
  if(length(int)==1){
    lims   <- seq(from = 1, to = np[2], length = int + 1)
  } else {
    lims  <- int
    int <- length(int)-1
  }
  lefts  <- ceiling(lims[-(int+1)])
  rights <- floor(lims[-1])
  minip  <- round((lefts + rights)/2)
  
  # Iterate through spectra
  for(s in 1:np[1]){

    # ------==== S2: Subsampling ====------
    xx <- numeric(int)
    for (i in 1:int) xx[i] <- min(spectra[s,lefts[i]:rights[i]])
    
    # ------==== S3: Suppression ====------
    for(k in 1:it){
      # Current window width
      w0 <- w[k]
      
      # Point-wise iteration to the right
      for(i in 2:(int-1)){
        # Interval cut-off close to edges
        v <- min(c(i-1,w0,int-i))
        
        # Baseline suppression
        a <- mean(xx[(i-v):(i+v)])
        xx[i] <- min(a,xx[i])
      }
      
      # Point-wise iteration to the left
      for(i in 2:(int-1)){
        j <- int-i+1
        # Interval cut-off close to edges
        v <- min(c(i-1,w0,int-i))
        
        # Baseline suppression
        a <- mean(xx[(j-v):(j+v)])
        xx[j] <- min(a,xx[j])
      }
    }
    
    # Prepare minimum vector
    minip[1] <- 1
    minip[int] <- np[2]
    
    # ------==== S4: Stretch ====------
    xxx <- approx(minip, xx, 1:np[2])$y
    baseline[s,] <- xxx
  }
  list(baseline = baseline, corrected = Yorig - baseline)
}

.baseline.fillPeaks_old <- function(spectra, lambda, hwi, it, int){
  ## Iterative baseline correction algorithm based on mean suppression
  ## By Kristian Hovde Liland
  
  # INPUT:
  # spectra - rows of spectra
  # lambda  - 2nd derivative penalty of primary smoothing
  # hwi     - half width of local window
  # it      - number of iterations in suppression loop
  # int     - number of bucket intervals to make of data (or vektor of bucket boundaries)
  #
  # OUTPUT:
  # baseline  - estimated baseline
  # corrected - baseline corrected spectra
  
  # Initialization
  np <- dim(spectra)
  if (missing(int)) int <- np[1]-1
  baseline  <- matrix(0,np[1],np[2])
  
  # Sparse empty matrix (m x m)
  speye <- as.matrix.csr(0,np[2],np[2])
  
  # Diagonal sparse matrix (m x m)
  diag(speye) <- 1
  D <- diff(speye,differences=2)
  
  # ------==== S1: Smoothing ====------
  Yorig   <- spectra
  if(lambda > 0){
    U       <- chol(speye+10^lambda*t(D)%*%D)
    spectra <- t(backsolve(U, t(spectra)))
  }
  
  # Exponential decrease in interval width
  if(it != 1){
    d1 <- log10(hwi)
    d2 <- 0
    w <- ceiling((10)^c(d1+(0:(it-2))*(d2-d1)/(floor(it)-1), d2))
  } else {
    w <- hwi
  }
  
  # Compute bucket locations
  if(length(int)==1){
    lims   <- seq(from = 1, to = np[2], length = int + 1)
  } else {
    lims  <- int
    int <- length(int)-1
  }
  lefts  <- ceiling(lims[-(int+1)])
  rights <- floor(lims[-1])
  minip  <- round((lefts + rights)/2)
  
  # Iterate through spectra
  for(s in 1:np[1]){
    
    # ------==== S2: Subsampling ====------
    xx <- numeric(int)
    for (i in 1:int) xx[i] <- min(spectra[s,lefts[i]:rights[i]])
    
    # ------==== S3: Suppression ====------
    for(k in 1:it){
      # Current window width
      w0 <- w[k]
      
      # Point-wise iteration to the right
      for(i in 2:(int-1)){
        # Interval cut-off close to edges
        v <- min(c(i-1,w0,int-i))
        
        # Baseline suppression
        a <- mean(xx[(i-v):(i+v)])
        xx[i] <- min(a,xx[i])
      }
      
      # Point-wise iteration to the left
      for(i in 2:(int-1)){
        j <- int-i+1
        # Interval cut-off close to edges
        v <- min(c(i-1,w0,int-i))
        
        # Baseline suppression
        a <- mean(xx[(j-v):(j+v)])
        xx[j] <- min(a,xx[j])
      }
    }
    
    # Prepare minimum vector
    minip[1] <- 1
    minip[int] <- np[2]
    
    # ------==== S4: Stretch ====------
    xxx <- approx(minip, xx, 1:np[2])$y
    baseline[s,] <- xxx
  }
  list(baseline = baseline, corrected = Yorig - baseline)
}