Quantification and classification of vegetable oils in extra virgin olive oil samples using a portable near-infrared spectrometer associated with chemometrics

Abstract

Olive oil is an important food product for human health. The addition of vegetable oils is the most common form of oil adulteration. In this paper, a methodology was developed to identify and quantify adulterations in extra virgin olive oil (EVOO) using a portable near-infrared spectrometer (microNIR). Two different spectral acquisition modes were tested: reflectance and transmittance. Samples sets constitute binary blends of EVOO with soybean, sunflower, corn, and canola oil. Partial least squares regression (PLS) models were built to quantify adulterations in extra virgin olive oil. After, the soybean oil content was checked in 12 olive oils acquired in a local market. Also, the physicochemical properties: acidity, peroxide index, and ultraviolet absorbance of the commercial samples were determined following the standard method of Institute Adolfo Lutz and CEE n. 2568/91. The PLS models' accuracy was 0.5 to 1.8 wt% for transmittance mode and 1.7 to 4.6 wt% for reflectance mode. The commercial olive samples' adulterations, the binary blends of commercial samples, the vegetable oils, and the binary blends EVOO/vegetable oils were evaluated by principal component analysis (PCA) and soft independent modeling class analogy (SIMCA). PCA and SIMCA models distinguished the commercial samples according to the information contained in their labels; besides, it identified the olive oil samples on to dataset with the blends. These results are in excellent agreement with the physicochemical results that corroborate with the limits of regulation.