University of Oulu

El Maouardi M, Alaoui Mansouri M, De Braekeleer K, Bouklouze A, Vander Heyden Y. Evaluation of Multivariate Filters on Vibrational Spectroscopic Fingerprints for the PLS-DA and SIMCA Classification of Argan Oils from Four Moroccan Regions. Molecules. 2023; 28(15):5698.

Evaluation of multivariate filters on vibrational spectroscopic fingerprints for the PLS-DA and SIMCA classification of Argan oils from four Moroccan regions

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Author: El Maouardi, Meryeme1,2; Alaoui Mansouri, Mohammed3; De Braekeleer, Kris4;
Organizations: 1Biopharmaceutical and Toxicological Analysis Research Team, Laboratory of Pharmacology and Toxicology, Faculty of Medicine and Pharmacy, University Mohammed V, Rabat 10100, Morocco
2Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090 Brussels, Belgium
3Nano and Molecular Systems Research Unit, University of Oulu, FIN-90014 Oulu, Finland
4Pharmacognosy, Bioanalysis & Drug Discovery Unit, Faculty of Pharmacy, University Libre Brussels, 1050 Brussels, Belgium
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 4.9 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2023
Publish Date: 2023-10-10


This study aimed to develop an analytical method to determine the geographical origin of Moroccan Argan oil through near-infrared (NIR) or mid-infrared (MIR) spectroscopic fingerprints. However, the classification may be problematic due to the spectral similarity of the components in the samples. Therefore, unsupervised and supervised classification methods—including principal component analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA)—were evaluated to distinguish between Argan oils from four regions. The spectra of 93 samples were acquired and preprocessed using both standard preprocessing methods and multivariate filters, such as External Parameter Orthogonalization, Generalized Least Squares Weighting and Orthogonal Signal Correction, to improve the models. Their accuracy, precision, sensitivity, and selectivity were used to evaluate the performance of the models. SIMCA and PLS-DA models generated after standard preprocessing failed to correctly classify all samples. However, successful models were produced after using multivariate filters. The NIR and MIR classification models show an equivalent accuracy. The PLS-DA models outperformed the SIMCA with 100% accuracy, specificity, sensitivity and precision. In conclusion, the studied multivariate filters are applicable on the spectroscopic fingerprints to geographically identify the Argan oils in routine monitoring, significantly reducing analysis costs and time.

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Series: Molecules
ISSN: 1420-3049
ISSN-E: 1420-3049
ISSN-L: 1420-3049
Volume: 28
Issue: 15
Article number: 5698
DOI: 10.3390/molecules28155698
Type of Publication: A1 Journal article – refereed
Field of Science: 1182 Biochemistry, cell and molecular biology
Funding: The authors are grateful to the funding of the VLIR-UOS (Team project-VLIR 345 MA2017), the Vrije Universiteit Brussel, the Université Libre de Bruxelles and the University Mohammed V in Rabat.
Copyright information: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (