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Robust preprocessing and model selection for spectral data
Author(s) -
Verboven Sabine,
Hubert Mia,
Goos Peter
Publication year - 2012
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.2446
Subject(s) - outlier , partial least squares regression , principal component analysis , calibration , preprocessor , mean squared error , data pre processing , computer science , principal component regression , multivariate statistics , selection (genetic algorithm) , model selection , latent variable , robust regression , data set , regression , statistics , data mining , artificial intelligence , mathematics
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 © 2012 John Wiley & Sons, Ltd.