L. Eriksson et al., Orthogonal signal correction, wavelet analysis, and multivariate calibration of complicated process fluorescence data, ANALYT CHIM, 420(2), 2000, pp. 181-195
In this paper, multivariate calibration of complicated process fluorescence
data is presented. Two data sets related to the production of white sugar
are investigated. The first data set comprises 106 observations and 571 spe
ctral variables, and the second data set 268 observations and 3997 spectral
variables, in both applications, a single response, ash content, is modell
ed and predicted as a function of the spectral variables. Both data sets co
ntain certain features making multivariate calibration efforts non-trivial.
The objective is to show how principal component analysis (PCA) and partia
l least squares (PLS) regression can be used to overview the data sets and
to establish predictively sound regression models. It is shown how a recent
ly developed technique for signal filtering, orthogonal signal correction (
OSC), can be applied in multivariate calibration to enhance predictive powe
r. In addition, signal compression is tested on the larger data set using w
avelet analysis. It is demonstrated that a compression down to 4% of the or
iginal matrix size - in the variable direction - is possible without loss o
f predictive power. It is concluded that the combination of OSC for pre-pro
cessing and wavelet analysis for compression of spectral data is promising
for future use. (C) 2000 Elsevier Science B.V. All rights reserved.