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

被引：41

作者：

Kharbach, Mourad ^{[1
,2
]}

Kamal, Rabie ^{[3
]}

Alaoui Mansouri, Mohammed ^{[2
,4
]}

Marmouzi, Ilias ^{[2
]}

Viaene, Johan ^{[1
]}

Cherrah, Yahia ^{[2
]}

Alaoui, Katim ^{[3
]}

Vercammen, Joeri ^{[5
]}

Bouklouze, Abdelaziz ^{[2
]}

Vander Heyden, Yvan ^{[1
]}

机构：

[1] VUB, Dept Analyt Chem Appl Chemometr & Mol Modelling, CePhaR, Laarbeeklaan 103, B-1090 Brussels, Belgium

[2] Univ Mohammed V Rabat, Fac Med & Pharm, Lab Pharmacol & Toxicol, Biopharmaceut & Toxicol Anal Res Team, Rabat, Morocco

[3] Univ Mohammed V Rabat, Fac Med & Pharm, Lab Pharmacol & Toxicol, Pharmacodynamy Res Team, Rabat, Morocco

[4] Univ Liege ULg, Lab Analyt Chem, Dept Pharm, CIRM,Quartier Hop, Ave Hippocrate 15,B36, B-4000 Liege, Belgium

[5] Interscience, Ave JE Lenoir 2, B-1348 Louvain La Neuve, Belgium

来源：

FOOD CHEMISTRY | 2018年 / 263卷

关键词：

Argan oil; Selected-ion flow-tube mass spectrometry; Geographical origin; Chemometric class-modeling; Classification methods; Fingerprints; VIRGIN OLIVE OILS; FATTY-ACID-COMPOSITION; VARIABLE SELECTION; VOLATILE COMPOUNDS; CLASSIFICATION; ADULTERATION; QUALITY; ORIGIN; TRIGLYCERIDES; CHEMOMETRICS;

D O I：

10.1016/j.foodchem.2018.04.059

中图分类号：

O69 [应用化学];

学科分类号：

081704 ;

摘要：

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 O-2(+)). 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.

引用

页码：8 / 17

页数：10

共 40 条

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