Direct Orthogonalization: some case studies

Abstract The effects of a Direct Orthogonalization before applying PCR and PLS are studied for several data sets. In all cases the number of PLS factors needed to obtain the optimal model decreases but the number of PLS and DO factors together is the same as when PLS alone is used. However, the quality of the calibration model (measured as RMSECV) is usually not better when using DO, nor does the predictive quality (RMSEP) change significantly in most cases. The method may be used, however, to obtain a better understanding of the variation present in the data.

[1]  Ronald D. Snee,et al.  Validation of Regression Models: Methods and Examples , 1977 .

[2]  Hilko van der Voet,et al.  Comparing the predictive accuracy of models using a simple randomization test , 1994 .

[3]  Hilko van der Voet,et al.  Corrigendum to “Comparing the predictive accuracy of models using a simple randomization test” [Chemometrics and Intelligent Laboratory Systems 25 (1994) 313–323]☆ , 1995 .

[4]  Olof Svensson,et al.  An evaluation of orthogonal signal correction applied to calibration transfer of near infrared spectra , 1998 .

[5]  Onno E. de Noord,et al.  The influence of data preprocessing on the robustness and parsimony of multivariate calibration models , 1994 .

[6]  Tom Fearn,et al.  On orthogonal signal correction , 2000 .

[7]  Desire L. Massart,et al.  Detection of inhomogeneities in sets of NIR spectra , 1996 .

[8]  Riccardo Leardi,et al.  Genetic Algorithms as a Tool for Wavelength Selection in Multivariate Calibration , 1995 .

[9]  D. Massart,et al.  Elimination of uninformative variables for multivariate calibration. , 1996, Analytical chemistry.

[10]  P. Geladi,et al.  Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat , 1985 .

[11]  S. Wold,et al.  Orthogonal signal correction of near-infrared spectra , 1998 .

[12]  Desire L. Massart,et al.  Random correlation in variable selection for multivariate calibration with a genetic algorithm , 1996 .

[13]  I. E. Frank.  Intermediate least squares regression method , 1987 .