Title :

Selection of essential spectra to improve the multivariate curve resolution of minor compounds in complex pharmaceutical formulations

Author(s) :

Coic, Laureen [Auteur]
Centre International de Rencontres Mathématiques [CIRM]
Université de Liège
Sacre, Pierre-Yves [Auteur]
Dispas, Amandine [Auteur]
De Bleye, Charlotte [Auteur]
Fillet, Marianne [Auteur]
Université de Liège
Ruckebusch, Cyril [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Hubert, Philippe [Auteur]
Ziemons, Eric [Auteur]
Centre International de Rencontres Mathématiques [CIRM]
Université de Liège

Journal title :

Analytica Chimica Acta

Abbreviated title :

Anal. Chim. Acta

Volume number :

1198

Pages :

-

Publication date :

2022-02-14

ISSN :

0003-2670

English keyword(s) :

Raman
FT-IR
Hyperspectral imaging
MCR-ALS
Data reduction
Essential spectral pixels (ESPs)
Falsi fied medicines

HAL domain(s) :

Chimie/Chimie théorique et/ou physique

English abstract : [en]

Multivariate curve resolution unmixing of hyperspectral imaging data can be challenging when low sources of variance are present in complex samples, as for minor (low-concentrated) chemical compounds in pharmaceutical ...
Show more >Multivariate curve resolution unmixing of hyperspectral imaging data can be challenging when low sources of variance are present in complex samples, as for minor (low-concentrated) chemical compounds in pharmaceutical formulations. In this work, it was shown how the reduction of hyperspectral imaging data matrices through the selection of essential spectra can be crucial for the analysis of complex unknown pharmaceutical formulation applying Multivariate Curve Resolution – Alternating Least Squares (MCR-ALS). Results were obtained on simulated datasets and on real FT-IR and Raman hyperspectral images of both genuine and falsified tablets. When simulating the presence of minor compounds, different situations were investigated considering the presence of single pixels of pure composition as well as binary and ternary mixtures. The comparison of the results obtained applying MCR-ALS on the reduced data matrices with those obtained on the full matrices revealed unequivocal: more accurate decomposition could be achieved when only essential spectra were analyzed. Indeed, when analyzing the full dataset, MCR-ALS failed resolving minor compounds even though pure spectra were provided as initial estimation, as shown for Raman hyperspectral imaging data obtained on a medicine sample containing 7 chemical compounds. In contrast, when considering the reduced dataset, all minor contributions (down to 1 pixel over 17,956) were successfully unmixed. The same conclusion could be drawn from the results obtained analysing FT-IR hyperspectral imaging data of a falsified medicine.Show less >

Language :

Anglais

Peer reviewed article :

Oui

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

Submission date :

2024-02-28T22:32:09Z
2024-03-19T15:22:24Z