Orthogonal signal correction, wavelet analysis, and multivariate calibration of complicated process fluorescence data

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.