Multiomics Approach To Decipher the Origin of Chlorophyll Content in Virgin Olive Oil

On the structural and mechanical properties of mixed coconut and olive oil oleogels and bigels

The interaction of monoglycerides and phytosterols in olive- and coconut oil on the structuring of oleogels was analyzed. Specifically, bigels with gelatin hydrogel in different ratios (40:60 and 60:40 w/w) were formed. The physicochemical and microstructural attributes of these systems were assessed. The olive oil to coconut oil ratio (0-100 w/w) and the added oleogelators affected the crystal structure and the mechanical properties of the oleogels. Polarized light microscopy revealed that the addition of coconut oil created a denser triglycerides crystal network and the presence of phytosterols created more needle-like crystals, enhancing the textural properties of the oleogels and of the resulting bigels. The hardness of the oleogels ranged from 0.50 N to 1.24 N and for bigels was 5.96-36.75 N. Bigels hardness decreased as the oleogel ratio in the bigel increased. Microscopy and FTIR revealed that the addition of coconut oil in oleogels hampered the formation of a distinct crystalline monoglycerides network. Also, the absence of new peaks in the bigels indicated that the two structured phases interact with each other mostly physically, without the formation of new chemical bonds. Consequently, the oleogels and bigels developed, comprise a promising hard fat substitute with improved nutritional profile.

A classification and identification model of extra virgin olive oil adulterated with other edible oils based on pigment compositions and support vector machine

Adulteration identification of extra virgin olive oil (EVOO) is a vital issue in the olive oil industry. In this study, chromatographic fingerprint data of pigments combined with machine learning methodologies were successfully identified and classified EVOO, refined-pomace olive oil (R-POO), rapeseed oil (RO), soybean oil (SO), peanut oil (PO), sunflower oil (SFO), flaxseed oil (FO), corn oil (CO), extra virgin olive oil adulterated with rapeseed oil (EVOO-RO) and extra virgin olive oil adulterated with corn oil (EVOO-CO). Support vector machine (SVM) classification of EVOO, other edible oils, and EVOO adulteration identification achieved 100% accuracy for the training set sample and 94.44% accuracy for the test set sample. As a result, this SVM model could identify effectively the adulteration EVOO with the limit of 1% RO and 1% CO. Therefore, the excellent classification and predictive power of this model indicated pigments could be used as potential markers for identifying EVOO adulteration.