One hundred and four edible oil and fat samples from 18 different sources, either vegetable (Brazil nut, coconut, corn, sunflower, walnut, virgin olive, peanut, palm, canola, soybean, sunflower) or animal (tallow and hydrogenated fish), have been analyzed by high-performance gas chromatography (HPGC) and near-infrared spectroscopy (NIRS). Fatty acids were quantified by HPGC. The near-infrared spectral features of the most noteworthy bands were studied and discussed to design a filter-type NIR instrument. An arborescent structure, based on stepwise linear discriminant analysis (SLDA), was built to classify the samples according to their sources. Seven discriminant functions permitted a successive discrimination of saturated fats, corn, soybean, sunflower, canola, peanut, high oleic sunflower, and virgin olive oils. The discriminant functions were based on the absorbance values, between three and five, from the 1700–1800 and 2100–2400 nm regions. Chemical explanations are given in support of the selected wavelengths. The arborescent structure was then checked with a test set, and 90% of the samples were correctly classified.

The authenticity of products labeled as virgin olive oil is of great importance from commercial and health aspects. The combination of Fourier transform-Raman (FT-Raman) spectroscopy with multivariate procedures has been used for predicting the level of adulteration in a set of virgin olive oil samples that were adulterated with soybean, corn, and raw olive residue (olive pomace) oils at 1, 5, and 10%, respectively. Six genuine virgin olive oil samples, differing in their chemical composition, were selected from a set of 1428 European samples. The best result in prediction of adulteration was an adjusted R 2 value of 0.964, determined by regression on principal components (PCR), giving 100% correct discrimination between genuine and adulterated samples and 91.3% correct classifications at different adulteration levels. Keywords: Olive oil; adulteration; FT-raman spectroscopy; chemometrics; authentication

The detection of the presence of refined hazelnut oil in refined olive oil at low percentages is still a challenge with the current official standards. FT-Raman and FT-MIR spectroscopies have been used to determine the level of detection of the presence of hazelnut oil in olive oil. Spectroscopic analysis has been made not only with the entire oil but also with its unsaponifiable matter. Univariate and multivariate statistical models have been designed with this objective. This study shows that a complete discrimination between olive and hazelnut oils is possible and that adulteration can be detected if the presence of hazelnut oil in olive oil is >8% and if the blends are of Turkish olive and hazelnut oils. The limit of detection is higher when the blends are of edible oils from diverse geographical origins.