Olive oil quantification of edible vegetable oil blends using triacylglycerols chromatographic fingerprints and chemometric tools.

The present work studies the effectiveness of the use of triacylglycerols (TAGs) for the quantification of olive oil in blends with vegetable oils. The determinations were obtained using high-performance liquid chromatography (HPLC) coupled to a Charged Aerosol Detector (CAD), in combination with Partial Least Squares (PLS) regression and using interval PLS (iPLS) for variable selection. Results revealed that PLS models can predict olive oil concentrations with reasonable errors. Variable selection through iPLS did not improve predictions significantly, but revealed the chemical information important in the chromatogram to quantify olive oil in vegetable oil blends.

Discriminating olive and non-olive oils using HPLC-CAD and chemometrics.

This work presents a method for an efficient differentiation of olive oil and several types of vegetable oils using chemometric tools. Triacylglycerides (TAGs) profiles of 126 samples of different categories and varieties of olive oils, and types of edible oils, including corn, sunflower, peanut, soybean, rapeseed, canola, seed, sesame, grape seed, and some mixed oils, have been analyzed. High-performance liquid chromatography coupled to a charged aerosol detector was used to characterize TAGs. The complete chromatograms were evaluated by PCA, PLS-DA, and MCR in combination with suitable preprocessing. The chromatographic data show two clusters; one for olive oil samples and another for the non-olive oils. Commercial oil blends are located between the groups, depending on the concentration of olive oil in the sample. As a result, a good classification among olive oils and non-olive oils and a chemical justification of such classification was achieved.