Search results for "VIRGIN OLIVE OILS"
showing 4 items of 4 documents
Relationship between olive oil consumption and ankle-brachial pressure index in a population at high cardiovascular risk.
2020
[Background and aims]: The aim of this study was to ascertain the association between the consumption of different categories of edible olive oils (virgin olive oils and olive oil) and olive pomace oil and ankle-brachial pressure index (ABI) in participants in the PREDIMED-Plus study, a trial of lifestyle modification for weight and cardiovascular event reduction in individuals with overweight/obesity harboring the metabolic syndrome.
Classification of extra virgin olive oils according to their geographical origin using phenolic compound profiles obtained by capillary electrochroma…
2009
Abstract A simple and reliable method for the evaluation of the phenolic fraction of extra virgin olive oils (EVOO) by capillary electrochromatography (CEC) with UV–Vis detection, using lauryl acrylate (LA) ester-based monolithic columns, has been developed. The percentages of the porogenic solvents in the polymerization mixture, and the mobile phase composition, were optimized. The optimum monolith was obtained with a monomers/porogens ratio of 40:60% (wt/wt) using a LA/1,3-butanediol diacrylate ratio of 70:30% (wt/wt) and a 1,4-butanediol/1-propanol ratio of 25:75% (wt/wt). A satisfactory resolution between the phenolic compounds was achieved in less than 25 min with a 15:85 (v/v) ACN–wat…
Rapid Evaluation of Oxidized Fatty Acid Concentration in Virgin Olive Oils Using Metal Oxide Semiconductor Sensors and Multiple Linear Regression
2009
This works aims to set up a rapid and nondestructive method to evaluate the advanced oxidation of virgin olive oils (VOOs). An electronic nose based on an array of six metal oxide semiconductor sensors was used, jointly with multiple linear regression (MLR), to predict the oxidized fatty acid (OFA) concentration in VOO samples characterized by different oxidative status. An MLR model constructed using five predictors was able to predict OFA concentration with an average validation error of 9%.