Authentication of extra virgin Argan oil by selected-ion flow-tube mass-spectrometry fingerprinting and chemometrics

• Published in: Food chemistry Vol. 383; p. 132565
• Main Authors: Kharbach, Mourad, Yu, Huiwen, Kamal, Rabie, Marmouzi, Ilias, Alaoui, Katim, Vercammen, Joeri, Bouklouze, Abdelaziz, Vander Heyden, Yvan
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 30.07.2022
• Subjects: Adulteration; Chemometrics; Extra virgin Argan oil; Food Contamination - analysis; Ions - analysis; Mass Spectrometry - methods; Oil authentication; Oil fraud; Olive Oil - chemistry; Plant Oils - chemistry; Selected-Ion Flow-Tube Mass Spectrometry; Volatile organic compounds; R2c; RMSEP; SVM; LQAO; RMSECV; R2p; Extra virgin Argan oil; PLS; SIFT-MS; SO; Adulteration; OO; VOCs; SIMCA; Oil authentication; PLS-DA; AO; Volatile organic compounds; RMSEC; R2cv; VIP; Oil fraud; Selected-Ion Flow-Tube Mass Spectrometry; iPLS; EVAO
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2022.132565

[Display omitted] •SIFT-MS application in oil authentication using H3O+, NO+, and O2+ reagent ions.•SIFT-MS spectra combined with chemometrics for extra virgin Argan oil (EVAO) fraud.•SIFT-MS allows the identification and quantification of volatile compounds (oil fragrance).•Classification of pure and adulterated EVAO was established.•Quantification of three adulterant oils in EVAO was done at low levels. Recognized for its nutritional and therapeutic use, extra-virgin Argan Oil (EVAO) is frequently adulterated. Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) spectra were applied to quantify adulterants (i.e., Argan oil of lower quality (LQAO), olive oil (OO), and sunflower oil (SO)) in EVAO. Four data sets, i.e., using H3O+, NO+, O2+ reagent ions, and the combined data were considered. Soft independent modelling of class analogy (SIMCA), and partial least squares discriminant analysis (PLS-DA) were assessed to distinguish adulterated- from pure EVAO. The effectiveness of SIFT-MS associated with PLS and support vector machine (SVM) regression to quantify trace adulterants in EVAO was evaluated. Variable Importance in Projection (VIP), and interval-PLS (iPLS) were also investigated to extract useful features. Different models were built to predict the EVAO authenticity and the degree of adulteration. High accuracy was achieved. SIFT-MS spectra handled with the appropriate chemometric tools were found suitable for the quality evaluation of EVAO.