Chemometric preprocessing

This example demonstrates the standard preprocessing operations available in SpectroChemPy: normalization, mean-centering, autoscaling, Standard Normal Variate (SNV), and Multiplicative Scatter Correction (MSC).

import spectrochempy as scp

Load the dataset

dataset = scp.read_omnic("irdata/nh4y-activation.spg")
region = dataset[:, 4000.0:2000.0]

Normalization

Scales each spectrum. The default is method='max':

norm = region.normalize(method="max", dim="x")
_ = norm.plot(title="Max-normalized")

Mean-centering

Subtracts the mean along a chosen dimension:

centered = region.center(dim="x")
_ = centered.plot(title="Mean-centered per spectrum")

Autoscaling

Mean-centers and divides by the standard deviation (z-score):

scaled = region.autoscale(dim="x")
_ = scaled.plot(title="Autoscaled (z-score) per spectrum")

SNV and MSC

Standard Normal Variate (SNV) — equivalent to autoscale(dim='x'):

snv = region.snv()
_ = snv.plot(title="SNV corrected")

Multiplicative Scatter Correction (MSC):

msc = region.msc()
_ = msc.plot(title="MSC corrected")

This ends the example. Uncomment the next line to display the figures when running the script directly with Python.

# scp.show()