Smooth Factor Analysis (SFA) to Effectively Remove High Levels of Noise from Spectral Data Sets.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).  This SFA method was also applied to the real experimental data, and its results successfully demonstrated the powerful potential for effective noise removal. 
SFA
A modified form of the nonlinear iterative partial least squares (NIPALS) algorithm involving the smoothing of factors at each step is used in SFA. 
NIPALS
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). 
PCA
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). 
PLS