Baseline Estimation and Removal

Usage

signal_baseline(object, ...)

signal_correct(object, ...)

baseline_snip(object, ...)

baseline_rubberband(object, ...)

baseline_linear(object, ...)

# S4 method for class 'GammaSpectrum'
signal_baseline(object, method = c("SNIP", "rubberband", "linear"), ...)

# S4 method for class 'GammaSpectra'
signal_baseline(object, method = c("SNIP", "rubberband", "linear"), ...)

# S4 method for class 'GammaSpectrum'
signal_correct(object, method = c("SNIP", "rubberband", "linear"), ...)

# S4 method for class 'GammaSpectra'
signal_correct(object, method = c("SNIP", "rubberband", "linear"), ...)

# S4 method for class 'GammaSpectrum'
baseline_linear(object, from = NULL, to = NULL)

# S4 method for class 'GammaSpectra'
baseline_linear(object, from = NULL, to = NULL)

# S4 method for class 'GammaSpectrum'
baseline_rubberband(object, noise = 0, spline = TRUE, ...)

# S4 method for class 'GammaSpectra'
baseline_rubberband(object, noise = 0, spline = TRUE, ...)

# S4 method for class 'GammaSpectrum'
baseline_snip(object, LLS = FALSE, decreasing = FALSE, n = 100, ...)

# S4 method for class 'GammaSpectra'
baseline_snip(object, LLS = FALSE, decreasing = FALSE, n = 100, ...)

Arguments

object: A GammaSpectrum or GammaSpectra object.
...: Extra parameters to be passed to further methods.
method: A character string specifying the method to be used for baseline estimation (see details). Any unambiguous substring can be given.
from: An integer giving the first channel to be used for linear interpolation. If NULL (the default), channel \(1\) is used. Only used if method is "linear".
to: An integer giving the last channel to be used for linear interpolation. If NULL (the default), channel \(max\) is used. Only used if method is "linear".
noise: A length-one numeric vector giving the noise level. Only used if method is "rubberband".
spline: A logical scalar: should spline interpolation through the support points be used instead of linear interpolation? Only used if method is "rubberband".
LLS: A logical scalar: should the LLS operator be applied on x before employing SNIP algorithm? Only used if method is "SNIP".
decreasing: A logical scalar: should a decreasing clipping window be used? Only used if method is "SNIP".
n: An integer value giving the number of iterations. Only used if method is "SNIP".

Value

baseline_*() returns a BaseLine object.
signal_correct() returns a corrected GammaSpectrum or GammaSpectra object (same as object).

Details

The following methods are available for baseline estimation:

SNIP: Sensitive Nonlinear Iterative Peak clipping algorithm.
rubberband: A convex envelope of the spectrum is determined and the baseline is estimated as the part of the convex envelope lying below the spectrum. Note that the rubber band does not enter the concave regions (if any) of the spectrum.
linear: Linear baseline estimation.

Note

baseline_rubberband() is slightly modified from C. Beleites' hyperSpec::spc.rubberband().

References

Liland, K. H. (2015). 4S Peak Filling - baseline estimation by iterative mean suppression. MethodsX, 2, 135-140. doi:10.1016/j.mex.2015.02.009 .

Morháč, M., Kliman, J., Matoušek, V., Veselský, M. & Turzo, I. (1997). Background elimination methods for multidimensional gamma-ray spectra. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 401(1), p. 113-132. doi:10.1016/S0168-9002(97)01023-1

Morháč, M. & Matoušek, V. (2008). Peak Clipping Algorithms for Background Estimation in Spectroscopic Data. Applied Spectroscopy, 62(1), p. 91-106. doi:10.1366/000370208783412762

Ryan, C. G., Clayton, E., Griffin, W. L., Sie, S. H. & Cousens, D. R. (1988). SNIP, a statistics-sensitive background treatment for the quantitative analysis of PIXE spectra in geoscience applications. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 34(3), p. 396-402. doi:10.1016/0168-583X(88)90063-8

Author

N. Frerebeau

Examples

## Import a CNF file
spc_file <- system.file("extdata/LaBr.CNF", package = "gamma")
spc <- read(spc_file)

## Remove the first 35 channels
spc <- signal_slice(spc, -c(1:35))

## Linear baseline
bsl_linear <- baseline_linear(spc, from = 250, to = 750)
plot(spc, bsl_linear)


## SNIP baseline
bsl_snip <- baseline_snip(spc, LLS = FALSE, decreasing = FALSE, n = 100)
plot(spc, bsl_snip)


## Rubberband baseline
bsl_rubber <- baseline_rubberband(spc)
plot(spc, bsl_rubber)


## Remove baseline
spc_clean1 <- signal_correct(spc)
spc_clean2 <- spc - bsl_snip
all(spc_clean1 == spc_clean2)
#> [1] TRUE

plot(spc_clean1)