alsace (version 1.8.0)

fitpeaks: Fit chromatographic peaks with a gaussian profile

Description

Find chromatographic peaks, and fit peak parameters using a gaussian profile. The algorithm is extremely simple and could be replaced by a more sophisticated algorithm. In particular one can expect bad fits if peaks are overlapping significantly.

Usage

findpeaks(y, span = NULL)
fitpeaks(y, pos)

Arguments

y

response (numerical vector)

span

number of points used in the definition of what constitutes a "local" maximum. If not given, a default value of 20 percent of the number of time points is used.

pos

locations of local maxima in vector y

Value

Function findpeaks simply returns the locations of the local maxima, expressed as indices.Function fitpeaks returns a matrix, whose columns contain the following information:

rt

location of the maximum of the peak (x)

sd

width of the peak (x)

FWHM

full width at half maximum (x)

height

height of the peak (y)

area

peak area

Again, the first three elements (rt, sd and FWHM) are expressed as indices, so not in terms of the real retention times. The transformation to "real" time is done in function getAllPeaks.

Details

Finding peaks with function findpeaks is based on the position of local maxima within a window of width span.

Peak parameters are calculated using fitpeaks, assuming a normal distribution. Peak width is given as a standard deviation, calculated from the full width at half maximum (FWHM); the peak area is given by the ratio of the peak height and the density.

See Also

getAllPeaks

Examples

data(tea)
new.lambdas <- seq(260, 500, by = 2)
tea <- lapply(tea.raw, preprocess, dim2 = new.lambdas)
tea.split <- splitTimeWindow(tea, c(12, 14), overlap = 10)

Xl <- tea.split[[2]]
Xl.opa <- opa(Xl, 4)

Xl.als <- doALS(Xl, Xl.opa)

tpoints <- getTime(Xl.als)
plot(tpoints, Xl.als$CList[[2]][,2], type = "l", col = "gray")
pk.pos <- findpeaks(Xl.als$CList[[2]][,2], span = 11)
abline(v = tpoints[pk.pos], col = 4)

pks <- fitpeaks(Xl.als$CList[[2]][,2], pk.pos)
apply(pks, 1,
      function(pkmodel) {
        lines(tpoints,
              dnorm(1:length(tpoints), pkmodel["rt"], pkmodel["sd"]) *
              pkmodel["area"],
              col = 2)
        invisible()
      })
## reasonably close fit, apart from the small peak in the middle...