Baseline

Baseline correction is a preprocessing technique in spectroscopy that corrects for baseline shifts and variations in signal intensity by subtracting a baseline from a spectrum. The following algorithms are available:

• Baseline
  • Non-negative
    • Arguments:
    • Usage example:
    • Plotting example:
  • Subtract reference spectrum
    • Arguments:
    • Usage example:
    • Plotting example:
  • Constant baseline correction
    • Arguments:
    • Usage example:
      • Case 1: No wavenumbers provided
      • Case 2: Wavenumbers provided
    • Plotting example:
  • Linear baseline correction
    • Usage examples:
    • Plotting example:
  • Polynomial baseline correction
    • Arguments:
    • Usage examples:
    • Plotting example:
  • Cubic spline baseline correction
    • Arguments:
    • Usage examples:
    • Plotting example:
  • Adaptive iteratively reweighed penalized least squares baseline correction (AirPLS)
    • Arguments:
    • Usage examples:
    • Plotting example:
  • Asymmetrically reweighed penalized least squares baseline correction (ArPLS)
    • Arguments:
    • Usage examples:
    • Plotting example:

Non-negative

Non-negative baseline correction is a preprocessing technique in spectroscopy that corrects for baseline by removing negative values from a spectrum. Negative values are either replaced by 0, or set to their absolute value.

Arguments:

Usage example:

from chemotools.baseline import NonNegative

nnz = NonNegative(mode='zero')
nna = NonNegative(mode='abs')
spectra_nnz = nnz.fit_transform(spectra_baseline)
spectra_nna = nna.fit_transform(spectra_baseline)

Plotting example:

Subtract reference spectrum

Subtract reference spectrum is a preprocessing technique in spectroscopy that subtracts a reference spectrum from a target spectrum. The reference spectrum must be a single spectrum. The target spectrum can be a single spectrum or a list of spectra.

Arguments:

The following arguments can be set:

• reference: np.array The reference spectrum. Default: None. When it is set to None, the algorithm will not subtract the reference spectrum.

Usage example:

from chemotools.baseline import SubtractReference

sr = SubtractReference(reference=reference_spectrum)
spectra_sr = sr.fit_transform(spectra)

Plotting example:

Constant baseline correction

Constant baseline correction is a preprocessing technique in spectroscopy that corrects for baseline by subtracting a constant value from a spectrum. The constant value is the mean of a region in the spectrum. This processing step is specially useful in UV-Vis spectroscopy, where there can be a large region in the spectra without any absorption.

Arguments:

The wavenumbers vector must be sorted in ascending order.

Usage example:

Case 1: No wavenumbers provided

from chemotools.baseline import ConstantBaselineCorrection

cbc = ConstantBaselineCorrection(start=0, end=30)
spectra_baseline = cbc.fit_transform(spectra)

Case 2: Wavenumbers provided

from chemotools.baseline import ConstantBaselineCorrection

cbc = ConstantBaselineCorrection(start=950, end=975, wavenumbers=wn)
spectra_baseline = cbc.fit_transform(spectra)

Plotting example:

Linear baseline correction

Linear baseline correction is a preprocessing technique in spectroscopy that corrects for baseline shifts and variations in signal intensity by subtracting a linear baseline from a spectrum. The current implementation subtracts a linear baseline between the first and last point of the spectrum.

Usage examples:

from chemotools.baseline import LinearCorrection

lc = LinearCorrection()
spectra_baseline = lc.fit_transform(spectra)

Plotting example:

Polynomial baseline correction

Polynomial baseline correction is a preprocessing technique in spectroscopy that approximates a baseline by fitting a polynomial to selected points of the spectrum. The selected points often correspond to minima in the spectra, and are selected by their index (not by the wavenumber). If no points are selected, the algorithm will select all the points in the spectrum to fit a polynomial of a given order. This case is often called detrending in other spectral processing software.

Arguments:

Usage examples:

from chemotools.baseline import PolynomialCorrection

pc = PolynomialCorrection(order=2, indices=[0, 75, 150, 200, 337])
spectra_baseline = pc.fit_transform(spectra)

Plotting example:

Cubic spline baseline correction

Cubic spline baseline correction is a preprocessing technique in spectroscopy that approximates a baseline by fitting a cubic spline to selected points of the spectrum. Similar to the PolynomialCorrection, the selected points often correspond to minima in the spectra, and are selected by their index (not by the wavenumber). If no points are selected, the algorithm will select the first and last point of the spectrum.

Arguments:

Usage examples:

from chemotools.baseline import CubicSplineCorrection

cspl = CubicSplineCorrection(indices=[0, 75, 150, 200, 337])
spectra_baseline = cspl.fit_transform(spectra)

Plotting example:

Adaptive iteratively reweighed penalized least squares baseline correction (AirPLS)

It is an automated baseline correction algorithm that uses a penalized least squares approach to fit a baseline to a spectrum. The original algorithm is based on the paper by Zhang et al.. The current implementation is based on the Python implementation by zmzhang.

Arguments:

Usage examples:

from chemotools.baseline import AirPls

airpls = AirPls()
spectra_baseline = airpls.fit_transform(spectra)

Plotting example:

Asymmetrically reweighed penalized least squares baseline correction (ArPLS)

This is an automated baseline correction algorithm that uses a penalized least squares approach to fit a baseline to a spectrum. The original algorithm is based on the paper by Sung-June et al

Arguments:

Usage examples:

from chemotools.baseline import ArPls

arpls = ArPls()
spectra_baseline = arpls.fit_transform(spectra)

Plotting example: