Title :

Kernel-Partial Least Squares regression coupled to pseudo-sample trajectories for the analysis of mixture designs of experiments

Author(s) :

Vitale, Raffaele [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Palací-López, Daniel [Auteur]
Kerkenaar, Harmen H.M. [Auteur]
Postma, Geert J. [Auteur]
Buydens, Lutgarde M.C. [Auteur]
Ferrer, Alberto [Auteur]

Journal title :

Chemometrics and Intelligent Laboratory Systems

Volume number :

175

Pages :

37-46

Publication date :

2018-04

HAL domain(s) :

Chimie/Chimie théorique et/ou physique

English abstract : [en]

This article explores the potential of Kernel-Partial Least Squares (K-PLS) regression for the analysis of data proceeding from mixture designs of experiments. Gower's idea of pseudo-sample trajectories is exploited for ...
Show more >This article explores the potential of Kernel-Partial Least Squares (K-PLS) regression for the analysis of data proceeding from mixture designs of experiments. Gower's idea of pseudo-sample trajectories is exploited for interpretation purposes. The results show that, when the datasets under study are affected by severe non-linearities and comprise few observations, the proposed approach can represent a feasible alternative to classical methodologies (i.e. Scheffé polynomial fitting by means of Ordinary Least Squares - OLS - and Cox polynomial fitting by means of Partial Least Squares - PLS). Furthermore, a way of recovering the parameters of a Scheffé model (provided that it holds and has the same complexity as the K-PLS one) from the trend of the aforementioned pseudo-sample trajectories is illustrated via a simulated case-study.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Non spécifiée

Popular science :

Non

Administrative institution(s) :

CNRS
ENSCL
Université de Lille

Collections :

• Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516

Submission date :

2021-11-16T08:23:26Z
2024-02-23T08:57:48Z