MultiplicativeScatterCorrection#

class chemotools.scatter.MultiplicativeScatterCorrection(method: Literal['mean', 'median'] = 'mean', reference: ndarray | None = None, weights: ndarray | None = None)[source]

Bases: TransformerMixin, OneToOneFeatureMixin, BaseEstimator

Multiplicative scatter correction (MSC) is a preprocessing technique for removing scatter effects from spectra. It is based on fitting a linear regression model to the spectrum using a reference spectrum. The reference spectrum is usually a mean or median spectrum of a set of spectra.

Parameters:

  • reference (np.ndarray of shape (n_freatures), optional, default=None) – The reference spectrum to use for the correction. If None, the mean spectrum will be used. The default is None.

  • use_mean (bool, optional, default=True) – Whether to use the mean spectrum as the reference. The default is True.

  • use_median (bool, optional, default=False) – Whether to use the median spectrum as the reference. The default is False.

Variables:

  • n_features_in (int) – The number of features in the training data.

  • reference (np.ndarray) – The reference spectrum used for the correction.

Raises:

ValueError – If no reference is provided.

References

[1] Åsmund Rinnan, Frans van den Berg, Søren Balling Engelsen,

“Review of the most common pre-processing techniques for near-infrared spectra,” TrAC Trends in Analytical Chemistry 28 (10) 1201-1222 (2009).

Examples

>>> from chemotools.datasets import load_fermentation_train
>>> from chemotools.scatter import MultiplicativeScatterCorrection
>>> # Load sample data
>>> X, _ = load_fermentation_train()
>>> # Initialize MultiplicativeScatterCorrection
>>> msc = MultiplicativeScatterCorrection()
MultiplicativeScatterCorrection()
>>> # Fit and transform the data
>>> X_scaled = msc.fit_transform(X)

Attributes

ALLOWED_METHODS

ALLOWED_METHODS = ['mean', 'median']
fit(X: ndarray, y=None) → MultiplicativeScatterCorrection[source]

Fit the transformer to the input data. If no reference is provided, the mean or median spectrum will be calculated from the input data.

Parameters:

  • X (np.ndarray of shape (n_samples, n_features)) – The input data to fit the transformer to.

  • y (None) – Ignored to align with API.

Returns:

self – The fitted transformer.

Return type:

MultiplicativeScatterCorrection

transform(X: ndarray, y=None) → ndarray[source]

Transform the input data by applying the multiplicative scatter correction.

Parameters:

  • X (np.ndarray of shape (n_samples, n_features)) – The input data to transform.

  • y (None) – Ignored to align with API.

Returns:

X_transformed – The transformed data.

Return type:

np.ndarray of shape (n_samples, n_features)

This Page