In spectroscopy, multivariate calibrations more than often include a pre-processing step to reduce the effect of unwanted (not Y-related) sources of variability. Because there are many types of background noise, there are many pre-treatment methods. It is therefore tedious to select and/or combine the best pre-treatments. This article proposes to combine several pre-treatments through the use of sequential and orthogonalized partial least squares (SO-PLS), thus leading to a boosting method. The performances and properties of this new method, called Sequential Preprocessing through ORThogonalization (SPORT), are compared to those of a previously published stacking method. SPORT demonstrates very good calibration performances, but also the ability to make significant pretreatment selections.

Sequential preprocessing through ORThogonalization (SPORT) and its application to near infrared spectroscopy

Biancolillo A.;
2020-01-01

Abstract

In spectroscopy, multivariate calibrations more than often include a pre-processing step to reduce the effect of unwanted (not Y-related) sources of variability. Because there are many types of background noise, there are many pre-treatment methods. It is therefore tedious to select and/or combine the best pre-treatments. This article proposes to combine several pre-treatments through the use of sequential and orthogonalized partial least squares (SO-PLS), thus leading to a boosting method. The performances and properties of this new method, called Sequential Preprocessing through ORThogonalization (SPORT), are compared to those of a previously published stacking method. SPORT demonstrates very good calibration performances, but also the ability to make significant pretreatment selections.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/144076
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