Abstract
Smooth factor analysis (SFA) is introduced as an effective method of
removing heavy noise from spectral data sets. A modified form of the
nonlinear iterative partial least squares (NIPALS) algorithm involving the
smoothing of factors at each step is used in SFA. Compared with the
conventional smoothing techniques for individual spectra, SFA is much more
effective in the treatment of very noisy spectra (∼40% noise
level). Smooth factor analysis invokes a large number of smooth factors to
retain pertinent spectral information for high fidelity without
distortion. This approach can be used as an effective general pretreatment
procedure for multivariate spectral data analysis, such as principal
component analysis (PCA) and partial least squares (PLS). This SFA method
was also applied to the real experimental data, and its results
successfully demonstrated the powerful potential for effective noise
removal. Furthermore, this treatment is found to be very helpful to assist
effective interpretation of two-dimensional correlation spectroscopy
(2D-COS) spectra with very high noise level, which was not possible
before.
© 2018 The Author(s)
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