Baseline Correction for Spectral Data

Applies baseline correction to each spectrum (row) of a spectral matrix. Multiple correction algorithms are available and selected via the method argument.

Usage

correct_baseline(X, method = c("asls", "linear"), ...)

bline(X, ...)

Arguments

X

Numeric matrix or metabom8 dat object containing spectra in rows and spectral variables in columns.

method

Character specifying the baseline correction algorithm. One of:

"asls"

Asymmetric least squares baseline estimation.

"linear"

Linear baseline estimated from edge regions.

...

Additional parameters passed to the selected method.

Arguments for method = "asls"

lambda

Numeric smoothing parameter controlling baseline stiffness. Larger values produce smoother baselines. Default 1e7.

iter_max

Maximum number of iterations used in the baseline estimation procedure. Default 30.

Arguments for method = "linear"

ppm

Numeric vector describing the spectral axis (e.g. chemical shift). Must have length equal to ncol(X).

edge_frac

Fraction of points at each spectrum edge used to estimate the baseline. Default 0.1.

Value

Numeric matrix containing baseline-corrected spectra with the same dimensions as X. If X is a metabom8 dat object, the corrected matrix replaces the X component while preserving associated metadata.

Details

Baseline correction is performed independently for each spectrum.

The "asls" method estimates a smooth baseline using asymmetric least squares smoothing, which is well suited for spectra with positive peaks such as NMR metabolomics data.

The "linear" method estimates a straight baseline from the edges of each spectrum and subtracts the fitted trend.

The returned object includes a .stamp attribute recording the baseline correction method and parameters used.

References

Eilers PHC, Boelens HFM (2005). Baseline correction with asymmetric least squares smoothing.

See also

Other preprocessing: align_segment(), align_spectra(), binning(), calibrate(), correct_lw(), pqn(), print_preprocessing()

Examples

data(hiit_raw)

plot_spec(hiit_raw$X[1,], hiit_raw$ppm, shift=c(3.1,4), backend='base')
hiit_proc <-
  hiit_raw |>
  excise() |>
  correct_baseline()

plot_spec(hiit_proc$X[1,], hiit_proc$ppm, shift=c(3.1,4), backend='base', add=TRUE, col='red')