Selected-ion flow-tube mass-spectrometry (SIFT-MS) fingerprinting versus chemical profiling for geographic traceability of Moroccan Argan oils

Mourad Kharbach; Rabie Kamal; Mohammed Alaoui Mansouri; Ilias Marmouzi; Johan Viaene; Yahia Cherrah; Katim Alaoui; Joeri Vercammen; Abdelaziz Bouklouze; Yvan Vander Heyden

doi:10.1016/j.foodchem.2018.04.059

Selected-ion flow-tube mass-spectrometry (SIFT-MS) fingerprinting versus chemical profiling for geographic traceability of Moroccan Argan oils

Mourad Kharbach, Rabie Kamal, Mohammed Alaoui Mansouri, Ilias Marmouzi, Johan Viaene, Yahia Cherrah, Katim Alaoui, Joeri Vercammen, Abdelaziz Bouklouze, Yvan Vander Heyden

Department of Food and Nutrition

Research output: Contribution to journal › Article › Scientific › peer-review

Abstract

This study investigated the effectiveness of SIFT-MS versus chemical profiling, both coupled to multivariate data analysis, to classify 95 Extra Virgin Argan Oils (EVAO), originating from five Moroccan Argan forest locations. The full scan option of SIFT-MS, is suitable to indicate the geographic origin of EVAO based on the fingerprints obtained using the three chemical ionization precursors (H₃O⁺, NO⁺ and O₂ ⁺). The chemical profiling (including acidity, peroxide value, spectrophotometric indices, fatty acids, tocopherols- and sterols composition) was also used for classification. Partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbors (KNN), and support vector machines (SVM), were compared. The SIFT-MS data were therefore fed to variable-selection methods to find potential biomarkers for classification. The classification models based either on chemical profiling or SIFT-MS data were able to classify the samples with high accuracy. SIFT-MS was found to be advantageous for rapid geographic classification.

Original language	English
Journal	Food Chemistry
Volume	263
Pages (from-to)	8-17
Number of pages	10
ISSN	0308-8146
DOIs	https://doi.org/10.1016/j.foodchem.2018.04.059
Publication status	Published - 15 Oct 2018
MoE publication type	A1 Journal article-refereed

Bibliographical note

Funding Information:
The authors are grateful to the staff of the Interscience company Breda (The Nederlands) for providing the SIFT-MS instrument. They appreciate the support of “Mohammed VI Foundation for Research and Protection of the Argan Tree”. The authors also thankful and acknowledge the financial support of FWO-Vlaanderen, VLIR-UOS (Team project-VLIR 345 MA2017), the Vrije Universiteit Brussel ( VUB ) and the Faculty of Medicine and Pharmacy-Rabat ( FMPR ). M.K. is thankful to Pr. Jamal Taoufik and Pr. Mohamed Adnaoui (vice-doyen and doyen of FMPR) for the travel grants support. Appendix A

Publisher Copyright:
© 2018 Elsevier Ltd

Fields of Science

Argan oil
Chemometric class-modeling
Classification methods
Fingerprints
Geographical origin
Selected-ion flow-tube mass spectrometry

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10.1016/j.foodchem.2018.04.059

Cite this

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title = "Selected-ion flow-tube mass-spectrometry (SIFT-MS) fingerprinting versus chemical profiling for geographic traceability of Moroccan Argan oils",

abstract = "This study investigated the effectiveness of SIFT-MS versus chemical profiling, both coupled to multivariate data analysis, to classify 95 Extra Virgin Argan Oils (EVAO), originating from five Moroccan Argan forest locations. The full scan option of SIFT-MS, is suitable to indicate the geographic origin of EVAO based on the fingerprints obtained using the three chemical ionization precursors (H3O+, NO+ and O2 +). The chemical profiling (including acidity, peroxide value, spectrophotometric indices, fatty acids, tocopherols- and sterols composition) was also used for classification. Partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbors (KNN), and support vector machines (SVM), were compared. The SIFT-MS data were therefore fed to variable-selection methods to find potential biomarkers for classification. The classification models based either on chemical profiling or SIFT-MS data were able to classify the samples with high accuracy. SIFT-MS was found to be advantageous for rapid geographic classification.",

keywords = "Argan oil, Chemometric class-modeling, Classification methods, Fingerprints, Geographical origin, Selected-ion flow-tube mass spectrometry",

author = "Mourad Kharbach and Rabie Kamal and Mansouri, {Mohammed Alaoui} and Ilias Marmouzi and Johan Viaene and Yahia Cherrah and Katim Alaoui and Joeri Vercammen and Abdelaziz Bouklouze and {Vander Heyden}, Yvan",

note = "Funding Information: The authors are grateful to the staff of the Interscience company Breda (The Nederlands) for providing the SIFT-MS instrument. They appreciate the support of “Mohammed VI Foundation for Research and Protection of the Argan Tree”. The authors also thankful and acknowledge the financial support of FWO-Vlaanderen, VLIR-UOS (Team project-VLIR 345 MA2017), the Vrije Universiteit Brussel ( VUB ) and the Faculty of Medicine and Pharmacy-Rabat ( FMPR ). M.K. is thankful to Pr. Jamal Taoufik and Pr. Mohamed Adnaoui (vice-doyen and doyen of FMPR) for the travel grants support. Appendix A Publisher Copyright: {\textcopyright} 2018 Elsevier Ltd",

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doi = "10.1016/j.foodchem.2018.04.059",

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AU - Kamal, Rabie

AU - Mansouri, Mohammed Alaoui

AU - Marmouzi, Ilias

AU - Viaene, Johan

AU - Cherrah, Yahia

AU - Alaoui, Katim

AU - Vercammen, Joeri

AU - Bouklouze, Abdelaziz

AU - Vander Heyden, Yvan

N1 - Funding Information: The authors are grateful to the staff of the Interscience company Breda (The Nederlands) for providing the SIFT-MS instrument. They appreciate the support of “Mohammed VI Foundation for Research and Protection of the Argan Tree”. The authors also thankful and acknowledge the financial support of FWO-Vlaanderen, VLIR-UOS (Team project-VLIR 345 MA2017), the Vrije Universiteit Brussel ( VUB ) and the Faculty of Medicine and Pharmacy-Rabat ( FMPR ). M.K. is thankful to Pr. Jamal Taoufik and Pr. Mohamed Adnaoui (vice-doyen and doyen of FMPR) for the travel grants support. Appendix A Publisher Copyright: © 2018 Elsevier Ltd

PY - 2018/10/15

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N2 - This study investigated the effectiveness of SIFT-MS versus chemical profiling, both coupled to multivariate data analysis, to classify 95 Extra Virgin Argan Oils (EVAO), originating from five Moroccan Argan forest locations. The full scan option of SIFT-MS, is suitable to indicate the geographic origin of EVAO based on the fingerprints obtained using the three chemical ionization precursors (H3O+, NO+ and O2 +). The chemical profiling (including acidity, peroxide value, spectrophotometric indices, fatty acids, tocopherols- and sterols composition) was also used for classification. Partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbors (KNN), and support vector machines (SVM), were compared. The SIFT-MS data were therefore fed to variable-selection methods to find potential biomarkers for classification. The classification models based either on chemical profiling or SIFT-MS data were able to classify the samples with high accuracy. SIFT-MS was found to be advantageous for rapid geographic classification.

AB - This study investigated the effectiveness of SIFT-MS versus chemical profiling, both coupled to multivariate data analysis, to classify 95 Extra Virgin Argan Oils (EVAO), originating from five Moroccan Argan forest locations. The full scan option of SIFT-MS, is suitable to indicate the geographic origin of EVAO based on the fingerprints obtained using the three chemical ionization precursors (H3O+, NO+ and O2 +). The chemical profiling (including acidity, peroxide value, spectrophotometric indices, fatty acids, tocopherols- and sterols composition) was also used for classification. Partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbors (KNN), and support vector machines (SVM), were compared. The SIFT-MS data were therefore fed to variable-selection methods to find potential biomarkers for classification. The classification models based either on chemical profiling or SIFT-MS data were able to classify the samples with high accuracy. SIFT-MS was found to be advantageous for rapid geographic classification.

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JO - Food Chemistry

JF - Food Chemistry

ER -

Selected-ion flow-tube mass-spectrometry (SIFT-MS) fingerprinting versus chemical profiling for geographic traceability of Moroccan Argan oils

Abstract

Bibliographical note

Fields of Science

Access to Document

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Cite this