Title :

The MCR-ALS Trilinearity Constraint for Data With Missing Values

Author(s) :

Gomez Sanchez, Adrian [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
Loza-Alvarez, Pablo [Auteur]
Barcelona Institute of Science and Technology [BIST]
Ruckebusch, Cyril [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
De Juan, Anna [Auteur]
Universitat de Barcelona [UB]

Journal title :

J. Chemometr.

Abbreviated title :

J. Chemometr.

Volume number :

-

Pages :

-

Publication date :

2024-11-18

ISSN :

0886-9383

English keyword(s) :

constraints
missing data
Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS)
Nonlinear Iterative Partial Least Squares (NIPALS)
trilinearity

HAL domain(s) :

Chimie/Chimie théorique et/ou physique

English abstract : [en]

Trilinearity is a property of some chemical data that leads to unique decompositions when curve resolution or multiway decomposition methods are used. Curve resolution algorithms, such as Multivariate Curve Resolution–Alternating ...
Show more >Trilinearity is a property of some chemical data that leads to unique decompositions when curve resolution or multiway decomposition methods are used. Curve resolution algorithms, such as Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS), can provide trilinear models by implementing the trilinearity condition as a constraint. However, some trilinear analytical measurements, such as excitation–emission matrix (EEM) measurements, usually exhibit systematic patterns of missing data due to the nature of the technique, which imply a challenge to the classical implementation of the trilinearity constraint. In this instance, extrapolation or imputation methodologies may not provide optimal results. Recently, a novel algorithmic strategy to constrain trilinearity in MCR-ALS in the presence of missing data was developed. This strategy relies on the sequential imposition of a classical trilinearity restriction on different submatrices of the original investigated dataset, but, although effective, was found to be particularly slow and requires a proper submatrix selection criterion. In this paper, a much simpler implementation of the trilinearity constraint in MCR-ALS capable of handling systematic patterns of missing data and based on the principles of the Nonlinear Iterative Partial Least Squares (NIPALS) algorithm is proposed. This novel approach preserves the trilinearity of the retrieved component profiles without requiring data imputation or subset selection steps and, as with all other constraints designed for MCR-ALS, offers the flexibility to be applied component-wise or data block-wise, providing hybrid bilinear/trilinear models. Furthermore, it can be easily extended to cope with any trilinear or higher-order dataset with whatever pattern of missing values.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
CNRS

Collections :

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

Research team(s) :

Dynamics, Nanoscopy & Chemometrics (DyNaChem)

Submission date :

2024-11-21T22:03:23Z
2024-12-04T08:33:10Z
2024-12-04T08:34:30Z