Detection of common adulterants in olive oils by bench top 60 MHz 1H NMR with partial least squares regression

Abstract

Olive oil is the oil obtained from the fruit of the olive tree, Olea europaea L., and is commonly used in cooking, cosmetics, and soaps. The high value and demand for olive oil products has led to some producers adding cheaper edible non-olive oils to olive oil to increase their profitability. Many of these adulterant oils have similar taste profiles to extra virgin olive oil; however, these can cause allergic reactions in individuals with sensitivities to the non-olive oil. Given the implications of olive oil adulteration, there is a demand for methods to identify and quantify adulterant oils in olive oil samples. This work explores the utility of low-field benchtop nuclear magnetic resonance (NMR) with a novel chemometrics workflow for the quantification of non-olive oils in authentic extra virgin olive oil samples. The measurements were obtained using a 60 MHz 1H NMR. The icoshift algorithm was utilized for alignment, and partial least squares regression models of the entire NMR spectrum were used for quantification. Here, 17 different varieties of edible non-olive oils were investigated. The models were highly linear, accurate, and precise. Most of these adulterant oils were detectable below 10% v/v in extra virgin olive oil samples.