Olive oil quality classification and measurement of its organoleptic attributes by untargeted GC–MS and multivariate statistical-based approach. (15th January 2019)

• Record Type: Journal Article
• Authors: Sales, C.; Portolés, T.; Johnsen, L.G.; Danielsen, M.; Beltran, J.
• Is Part Of: Food chemistry. Volume 271(2019)
• Page Start: 488
• Page End: 496
• Publication Date: 2019-01-15
• Subjects: Dynamic headspace -- Olive oil -- GC–MS -- PARAFAC2 -- Foodomics; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2018.07.200
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Dynamic headspace-GC–MS is an efficient methodology for olive oil classification. PARADISe software for advanced deconvolution in non-targeted approaches. Important complement to the official "PANEL TEST" method for quality control. Abstract: The capabilities of dynamic headspace entrainment followed by thermal desorption in combination with gas chromatography (GC) coupled to single quadrupole mass spectrometry (MS) have been tested for the determination of volatile components of olive oil. This technique has shown a great potential for olive oil quality classification by using an untargeted approach. The data processing strategy consisted of three different steps: component detection from GC–MS data using novel data treatment software PARADISe, a multivariate analysis using EZ-Info, and the creation of the statistical models. The great number of compounds determined enabled not only the development of a quality classification method as a complementary tool to the official established method "PANEL TEST" but also a correlation between these compounds and different types of defect. Classification method was finally validated using blind samples. An accuracy of 85% in oil classification was obtained, with 100% of extra virgin samples correctly classified.