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An investigation of orthogonal signal correction algorithms and their characteristics
Author(s) -
Svensson O.,
Kourti T.,
MacGregor J. F.
Publication year - 2002
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.700
Subject(s) - orthogonalization , algorithm , calibration , computer science , component (thermodynamics) , point (geometry) , orthographic projection , group (periodic table) , signal (programming language) , interpretation (philosophy) , orthogonal transformation , component analysis , principal component analysis , orthogonal matrix , projection (relational algebra) , orthogonal array , mathematics , artificial intelligence , orthogonal basis , statistics , machine learning , taguchi methods , physics , geometry , chemistry , organic chemistry , quantum mechanics , thermodynamics , programming language
Six different algorithms for orthogonal signal correction (OSC) are studied and compared both from an algorithmic point of view and from a prediction and analysis point of view. The algorithms have appeared under the names OSC (three alternative algorithms), direct orthogonalization (DO) and orthogonal projection to latent structures (OPLS). These algorithms can be divided into two groups. The first group has the ability to reduce the number of PLS components in the calibration models significantly by removing only one orthogonal component. The second group reduces the complexity of the calibration model by one PLS component for each orthogonal component removed. The methods are evaluated and compared using both simulated and real calibration data sets. In some cases the OSC algorithms can have quite different behaviors, such as when non‐linearities are present. However, in all cases we have studied, none of the OSC algorithms provided a significant improvement in the calibration models over using PLS on the raw data. The main advantage with OSC may lie in the possibly easier interpretation and understanding from the analysis of corrected data. Analysis of the orthogonal information removed with OSC might also be beneficial. Copyright © 2002 John Wiley & Sons, Ltd.