The present study compares the performance of different multivariate calibration techniques when new samples to be predicted can fall outside the calibration domain. Results of the calibration methods are investigated for extrapolation of different types and various levels. The calibration methods are applied to five near-IR data sets including difficulties often met in practical cases (nonlinearity, nonhomogeneity and presence of irrelevant variables in the set of predictors). The comparison leads to general recommendations about what method to use when samples requiring extrapolation can be expected in a calibration application.

A comparison of multivariate calibration techniques applied to experimental NIR data sets Part II. Predictive ability under extrapolation conditions

PASTI, Luisa;
2001

Abstract

The present study compares the performance of different multivariate calibration techniques when new samples to be predicted can fall outside the calibration domain. Results of the calibration methods are investigated for extrapolation of different types and various levels. The calibration methods are applied to five near-IR data sets including difficulties often met in practical cases (nonlinearity, nonhomogeneity and presence of irrelevant variables in the set of predictors). The comparison leads to general recommendations about what method to use when samples requiring extrapolation can be expected in a calibration application.
Estienne, F; Pasti, Luisa; Centner, V; Walczak, B; Despagne, F; Rimbaud, Dj; DE NOORD, Oe; Massart, Dl
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/1207466
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