Title
Robust preprocessing and model selection for spectral dataRobust preprocessing and model selection for spectral data
Author
Faculty/Department
Faculty of Applied Economics
Faculty of Sciences. Mathematics and Computer Science
Research group
Engineering Management
Department of Mathematics - Computer Sciences
Publication type
article
Publication
Chichester,
Subject
Mathematics
Physics
Chemistry
Computer. Automation
Source (journal)
Journal of chemometrics. - Chichester
Volume/pages
26(2012):6, p. 282-289
ISSN
0886-9383
ISI
000305510100010
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
To calibrate spectral data, one typically starts with preprocessing the spectra and then applies a multivariate calibration method such as principal component regression or partial least squares regression. In the model selection step, the optimal number of latent variables is determined in order to minimize the prediction error. To protect the analysis against the harmful influence of possible outliers in the data, robust calibration methods have been developed. In this paper, we focus on the preprocessing and the model selection step. We propose several robust preprocessing methods as well as robust measures of the root mean squared error of prediction (RMSEP). To select the optimal preprocessing method, we summarize the results for the different RMSEP values by means of a desirability index, which is a concept from industrial quality control. These robust RMSEP values are also used to select the optimal number of latent variables. We illustrate our newly developed techniques through the analysis of a real data set containing near-infrared measurements of samples of animal feed. Copyright (c) 2012 John Wiley & Sons, Ltd.
E-info
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