Signal Smoothing with PLS Regression

Smoothing of instrumental
signals is an important prerequisite
in data processing. Various smoothing methods were suggested through
the last decades each having their own benefits and drawbacks. Most
of the filtering methods are based on averaging in a certain window
(e.g., Savitzky-Golay) or on frequency-domain representation (e.g.,
Fourier filtering). The present study introduces novel approach to
signal filtering based on signal variance through PLS (projections
on latent structures) regression. The influence of filtering parameters
on the smoothed spectrum is explained and real world examples are
shown.