Scatter

This package contains three common algorithms for scatter correction in spectroscopy:

• Multiplicative scatter correction (MSC)
• Extended multiplicative scatter correction (EMSC)
• Standard normal variate (SNV)
• Robust normal variate (RNV)

Multiplicative scatter correction

Multiplicative scatter correction (MSC) is a preprocessing technique in spectroscopy that corrects for the influence of light scattering on spectral measurements by dividing each spectrum by a scatter reference spectrum. The current implementation, accepts three types of reference spectra:

Arguments:

Usage examples:

Case 1: usage example for the mean spectrum:

from chemotools.scatter import MultiplicativeScatterCorrection

msc = MultiplicativeScatterCorrection()
spectra_msc = msc.fit_transform(spectra)

Case 2: usage example for the median spectrum:

from chemotools.scatter import MultiplicativeScatterCorrection

msc = MultiplicativeScatterCorrection(use_median=True)
spectra_msc = msc.fit_transform(spectra)

Case 3: usage example for a single reference spectrum:

from chemotools.scatter import MultiplicativeScatterCorrection

msc = MultiplicativeScatterCorrection(reference=reference_spectrum)
spectra_msc = msc.fit_transform(spectra)

Plotting example:

Extended multiplicative scatter correction

Extended multiplicative scatter correction (EMSC) is a preprocessing technique for removing non linear scatter effects from spectra. It is based on fitting a polynomial regression model to the spectrum using a reference spectrum. The reference spectrum can be the mean or median spectrum of a set of spectra or a selected reference. The current implementation is based on the following articles:

• Nils Kristian Afseth, Achim Kohler. Extended multiplicative signal correction in vibrational spectroscopy, a tutorial, doi:10.1016/j.chemolab.2012.03.004

• Valeria Tafintseva et al. Correcting replicate variation in spectroscopic data by machine learning and model-based pre-processing, doi:10.1016/j.chemolab.2021.104350

Arguments:

Usage examples:

from chemotools.scatter import ExtendedMultiplicativeScatterCorrection

emsc = ExtendedMultiplicativeScatterCorrection()
spectra_emsc = emsc.fit_transform(spectra)

Plotting example:

Standard normal variate

Standard normal variate (SNV) is a preprocessing technique in spectroscopy that adjusts for baseline shifts and variations in signal intensity by subtracting the mean and dividing by the standard deviation of each spectrum.

Arguments:

The current implementation does not require any arguments.

Usage example:

from chemotools.scatter import StandardNormalVariate

snv = StandardNormalVariate()
spectra_snv = snv.fit_transform(spectra)

Plotting example:

Robust normal variate

Robust normal variate (RNV) is a preprocessing technique in spectroscopy that adjusts for baseline shifts and variations in signal intensity. In contrast to the standard normal variate, the robust normal variate uses the mean and the standard deviation of a certain percentile of the spectrum to ensure robustness against outliers. The current implementation is based on:

• Q. Guo, W. Wu, D.L. Massart. The robust normal variate transform for pattern recognition with near-infrared data. doi:10.1016/S0003-2670(98)00737-5

Arguments:

Usage example:

from chemotools.scatter import RobustNormalVariate

rnv = RobustNormalVariate()
spectra_rnv = rnv.fit_transform(spectra)

Plotting example: