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

Author:
Kharbach, Mourad, Kamal, Rabie, Mansouri, Mohammed Alaoui, Marmouzi, Ilias, Viaene, Johan, Cherrah, Yahia, Alaoui, Katim, Vercammen, Joeri, Bouklouze, Abdelaziz, Vander Heyden, Yvan
Source:
Food chemistry 2018 v.263 pp. 8-17
ISSN:
0308-8146
Subject:
acidity, biomarkers, discriminant analysis, fatty acids, forests, ionization, least squares, mass spectrometry, models, oils, peroxide value, provenance, sterols, support vector machines, traceability
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.
Agid:
6356318