The Argan tree (Argania spinosa L. Skeels) is a tropical plant and represents the only endemic species of the genus Argania in Morocco. Currently, in Morocco the Argan forest covers an area of 840 000 ha including the fertile Souss valley region, the foothills of the Anti-Atlas mountains, and the coast region between Essaouira and Agadir [1-2].
Nowadays 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 at relatively low levels. Analyte speciﬁcity is enabled by using three chemical ionization precursors for analysis (H3O+, NO+ and O2+) .
2 Material and methods
This preliminary study investigated the effectiveness of SIFT-MS and multivariate data analysis to perform rapid screening of 95 commercial EVAO characterized by five different geographical origins (‘AitBaha’, ‘Agadir’, ‘Essaouira’, ‘Tiznit’ and ‘Taroudant’) declared by protected geographical indication. The abilities of four multivariate classification methods were compared for each data set: Partial-Least-Squares Discriminant-Analysis (PLS-DA), Soft Independent Modeling of Class Analogy (SIMCA), K-Nearest Neighbor (KNN), and Support Vector Machines (SVM).
3 Results and discussion
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 possible conﬁrmation. The new approach using full scan is suitable to verify the geographic origin of EVAO based on three chemical ionization precursors for analysis (H3O+, NO+, and O2+). In this study, we have compared the abilities of four different multivariate classification methods: partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbor (KNN), and support vector machines (SVM) applied to the volatile proﬁle of the headspace as a ﬁngerprint. The selected variables that contain information for the aimed classification based on three algorithms such as interval partial least squares (iPLS) , variable importance in projection (VIP) and Uninformative variable elimination in PLS (UVE-PLS) were investigated, whereas those variables encoding the noise and/or with no discriminating power are eliminated.
SIFT-MS data with chemometric tools can be used for the evaluation of the quality and the classification of the Moroccan Argan oils.
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