Correct baseline along the time axis

Estimates a baseline along the time axis of each chromatogram and subtracts it, using any of the estimators described in baseline_estimators.

Usage

correct_baseline(
  x,
  baseline = c("als", "arpls", "aspls", "snip"),
  attach_baseline = FALSE,
  zero_floor = FALSE,
  cl = 2,
  show_progress = NULL,
  ...
)

Arguments

x: A numeric matrix (time × wavelength), a list of such matrices, or a plain numeric vector for a single trace. Row names (or, for a vector, names) must correspond to time points and column names to wavelengths.
baseline: Baseline estimator to use along the time axis. Either asymmetric least squares ("als", the default, via asysm), asymmetrically reweighted penalized least squares ("arpls"), adaptive smoothness parameter penalized least squares ("aspls"), or statistics-sensitive non-linear iterative peak-clipping ("snip"). See baseline_estimators for a description of each.
attach_baseline: Logical. Whether to retain the estimated baseline alongside the corrected data, as an "estimated_baseline" attribute on each returned matrix, retrievable with get_baseline. The baseline is subtracted either way; this argument only controls whether the estimate is kept for inspection. Defaults to FALSE: the baseline is estimated separately for each wavelength, so it is the same size as the data and retaining it doubles memory use.
zero_floor: Logical. Whether to clip negative values in the corrected data to zero. Defaults to FALSE, because clipping also removes the negative dips that show where a baseline was over-subtracted. It can be useful with baseline = "als", which can overshoot the signal; "snip" cannot overshoot by construction, so clipping is never needed there. The zero_floor argument of subtract_blanks, by contrast, defaults to TRUE, since a negative value there means the blank exceeded the sample rather than that an estimator overshot.
cl: Either an integer specifying the number of cores to use for parallel processing or a cluster object created by makeCluster. Defaults to 2. On Windows, integer values are ignored.
show_progress: Logical. Whether to show a progress bar. Defaults to TRUE if pbapply is installed.
...: Additional arguments to the selected baseline estimator: e.g. stiffness and p for "als" (asysm), stiffness for "arpls", stiffness and k for "aspls", or m for "snip".

Value

The baseline-corrected data, in the same shape as x, with row and column names (or, for a vector, names) unchanged.

Details

Unlike preprocess, this function only subtracts the baseline; it does not interpolate, smooth, or rescale.

References

Baek, S.-J., Park, A., Ahn, Y.-J., and Choo, J. 2015. Baseline correction using asymmetrically reweighted penalized least squares smoothing. Analyst 140:250-257. doi:10.1039/C4AN01061B .
Zhang, F., Tang, X., Tong, A., Wang, B., Wang, J., Lv, Y., Tang, C., and Wang, J. 2020. Baseline correction for infrared spectra using adaptive smoothness parameter penalized least squares method. Spectroscopy Letters 53:222-233. doi:10.1080/00387010.2020.1730908 .
Ryan, C.G., Clayton, E., Griffin, W.L., Sie, S.H., and 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 B 34:396-402. doi:10.1016/0168-583X(88)90063-8 .
Erb, D. 2025. pybaselines: A Python library of algorithms for the baseline correction of experimental data. doi:10.5281/zenodo.5608581 .

Author

Ethan Bass

Examples

data(Sa_pr)
bc <- correct_baseline(Sa_pr[1], cl = 1, show_progress = FALSE)