Perspective on essential information in ...
Type de document :
Article dans une revue scientifique: Article original
URL permanente :
Titre :
Perspective on essential information in multivariate curve resolution
Auteur(s) :
Ruckebusch, Cyril [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Vitale, Raffaele [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Ghaffari, M. [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Hugelier, Siewert [Auteur]
Department of Chemistry [Leuven]
Omidikia, N. [Auteur]
Department of Chemistry

Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Vitale, Raffaele [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Ghaffari, M. [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Hugelier, Siewert [Auteur]
Department of Chemistry [Leuven]
Omidikia, N. [Auteur]
Department of Chemistry
Titre de la revue :
TrAC Trends in Analytical Chemistry
Nom court de la revue :
TrAC Trends in Analytical Chemistry
Numéro :
132
Pagination :
116044
Date de publication :
2020-11
ISSN :
01659936
Discipline(s) HAL :
Chimie/Chimie théorique et/ou physique
Résumé en anglais : [en]
We propose to take a new perspective on the construction and interpretation of multivariate curve resolution (MCR) models for the decomposition of spectral mixture data. We start by introducing archetypes, i.e. points that ...
Lire la suite >We propose to take a new perspective on the construction and interpretation of multivariate curve resolution (MCR) models for the decomposition of spectral mixture data. We start by introducing archetypes, i.e. points that approximate the convex hull of a data cloud and correspond to the most linearly dissimilar observations. Identifying archetypes is a way to select essential samples (ESs) and essential variables (EVs) of a data matrix before MCR decomposition. Working with ESs and EVs, we then identify three main implications. The first is data reduction, which brings simplicity and computational speed. The second is prioritization, with the ESs and EVs profiles being the most dominant features to solve the MCR problem. The third is interpretability: the reduced data sets provide more direct insights and better understanding of final MCR models. Overall, the selection of ESs and EVs offers new opportunities that are worth being explored.Lire moins >
Lire la suite >We propose to take a new perspective on the construction and interpretation of multivariate curve resolution (MCR) models for the decomposition of spectral mixture data. We start by introducing archetypes, i.e. points that approximate the convex hull of a data cloud and correspond to the most linearly dissimilar observations. Identifying archetypes is a way to select essential samples (ESs) and essential variables (EVs) of a data matrix before MCR decomposition. Working with ESs and EVs, we then identify three main implications. The first is data reduction, which brings simplicity and computational speed. The second is prioritization, with the ESs and EVs profiles being the most dominant features to solve the MCR problem. The third is interpretability: the reduced data sets provide more direct insights and better understanding of final MCR models. Overall, the selection of ESs and EVs offers new opportunities that are worth being explored.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
CNRS
ENSCL
Université de Lille
ENSCL
Université de Lille
Collections :
Équipe(s) de recherche :
Dynamics, Nanoscopy & Chemometrics (DyNaChem)
Date de dépôt :
2021-12-08T09:53:14Z
2024-02-14T07:24:49Z
2024-02-14T07:24:49Z