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 languageEnglish
Pages (from-to)8-17
Number of pages10
JournalFood Chemistry
Publication statusPublished - 15 Oct 2018

    Research areas

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

ID: 38145141