Exploratory analysis of hyperspectral imaging data

Olarini, Alessandra; Cocchi, Marina; Motto-Ros, Vincent; Duponchel, Ludovic; Ruckebusch, Cyril

Type de document :

Article dans une revue scientifique: Article original

DOI :

10.1016/j.chemolab.2024.105174

URL permanente :

http://hdl.handle.net/20.500.12210/118835

Titre :

Exploratory analysis of hyperspectral imaging data

Auteur(s) :

Olarini, Alessandra [Auteur]
Università degli Studi di Modena e Reggio Emilia = University of Modena and Reggio Emilia [UNIMORE]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Cocchi, Marina [Auteur]
Università degli Studi di Modena e Reggio Emilia = University of Modena and Reggio Emilia [UNIMORE]
Motto-Ros, Vincent [Auteur]
Université Claude Bernard Lyon 1 [UCBL]
Duponchel, Ludovic [Auteur]

Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Ruckebusch, Cyril [Auteur]

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

Titre de la revue :

Chemometrics Intell. Lab. Syst.

Nom court de la revue :

Chemometrics Intell. Lab. Syst.

Numéro :

252

Pagination :

Date de publication :

2024-11-19

ISSN :

0169-7439

Mot(s)-clé(s) en anglais :

Spectral imaging
Essential information
Clustering
Spectral unmixing
Raman
LIBS

Discipline(s) HAL :

Chimie/Chimie théorique et/ou physique

Résumé en anglais : [en]

Characterizing sample composition and visualizing the distribution of its chemical compounds is a prominent topic in various research and applied fields. Integrating spatial and spectral information, hyperspectral imaging ...
Lire la suite >Characterizing sample composition and visualizing the distribution of its chemical compounds is a prominent topic in various research and applied fields. Integrating spatial and spectral information, hyperspectral imaging (HSI) plays a pivotal role in this pursuit. While self-modelling curve resolution techniques, like multivariate curve resolution - alternating least squares (MCR-ALS), and clustering methods, such as K-means, are widely used for HSI data analysis, their effectiveness in complex scenarios, where the structure of the data deviates from the models’ assumptions, deserves further investigation. The choice of a data analysis method is most often driven by research question at hand and prior knowledge of the sample. However, overlooking the structure of the investigated data, i.e. linearity, geometry, homogeneity, might lead to erroneous or biased results. Here, we propose an exploratory data analysis approach, based on the geometry of the data points cloud, to investigate the structure of HSI datasets and extract their main characteristics, providing insight into the results obtained by the above-mentioned methods. We employ the principle of essential information to extract archetype (most linearly dissimilar) spectra and archetype single-wavelength images. These spectra and images are then discussed and contrasted with MCR-ALS and K-means clustering results. Two datasets with varying characteristics and complexities were investigated: a powder mixture analyzed with Raman spectroscopy and a mineral sample analyzed with Laser Induced Breakdown Spectroscopy (LIBS). We show that the proposed approach enables to summarize the main characteristics of hyperspectral imaging data and provides a more accurate understanding of the results obtained by traditional data modelling methods, driving the choice of the most suitable one.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
CNRS

Collections :

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

Équipe(s) de recherche :

Propriétés magnéto structurales des matériaux (PMSM)

Date de dépôt :

2024-11-21T22:02:48Z
2024-12-04T08:12:31Z

Numéro de version	Lien	Date de modification
2	20.500.12210/118835*	2024-12-04T08:06:06Z
1	20.500.12210/118835.1	2024-11-21T22:02:48Z

Exploratory analysis of hyperspectral imaging data

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Exploratory analysis of hyperspectral imaging data

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