ORAL PRES.: Rapid geographic origin discrimination of Moroccan aragan oils based on their SIFT-MS fingerprints.

Mourad Kharbach, Johan Viaene, Jacqueline Vercammen, Yvan Vander Heyden, A. Bouklouze

Research output: Unpublished contribution to conferenceUnpublished abstract


The Argan tree (Argania spinosa L. Skeels) is a tropical plant covering an area of 840 000 ha in Morocco. Argan oil is nowadays a major and internationally well-established actor on both the edible and cosmetic-oil markets. Argan oil has numerous pharmacological properties, such as anti-inflammatory, anti-oxidant and anti-diabetic [1, 2]. The origins of food are essential for import and export trading in order to ensure the traceability for consumers, traders or even food producers. Information about food’s origin is necessary to verify its specifications and to guarantee its quality because foods from different origin have distinct qualities [3,4]. Selected ion flow tube-mass spectrometry (SIFT-MS) is a newer analytical technique, which has the ability to identify and quantify trace gases. Analyte specificity is enabled by using three chemical ionization precursors for analysis (H3O+, NO+ and O2+) [5]. At present, the geographical origin of extra virgin argan oils (EVAO) can be ensured by documented traceability, although chemical analysis may add information that is useful for confirmation. This preliminary study investigated the effectiveness of SIFT-MS and multivariate data analysis to perform rapid screening of 95 commercial EVAO originating from five geographical origins (‘AitBaha’, ‘Agadir’, ‘Essaouira’, ‘Tiznit’ and ‘Taroudant’), declared by protected geographical indication. The new approach using the full scan option of the the technique, is suitable to verify the geographic origin of EVAO based on the fingerprint using the three chemical ionization precursors. The abilities of four multivariate classification methods were compared: partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbor (KNN), and support vector machines (SVM). The volatile profile of the oil headspace was used to measure the fingerprints. The variables that contain information for the aimed classification were investigated, whereas those variables encoding the noise without discriminating power are eliminated.
The results indicate that SIFT-MS data can be used to evaluate of the geographical origin and the classification of the Moroccan Argan oils.
Original languageEnglish
Publication statusUnpublished - 2016
Event19th Forum of Pharmaceutical Sciences - Brussels, Belgium
Duration: 17 Oct 201618 Oct 2016


Conference19th Forum of Pharmaceutical Sciences


Dive into the research topics of 'ORAL PRES.: Rapid geographic origin discrimination of Moroccan aragan oils based on their SIFT-MS fingerprints.'. Together they form a unique fingerprint.

Cite this