Visible Raman spectroscopy for the discrimination of olive oils from different vegetable oils and the detection of adulteration

Abstract

AbstractWe have investigated the potential of Raman spectroscopy with excitation in the visible spectral range (VIS Raman) as a tool for the classification of different vegetable oils and the quantification of adulteration of virgin olive oil as an example. For the classification, principal component analysis (PCA) was applied, where 96% of the spectral variation was characterized by the first two components. A significant similarity between sunflower oil and extra‐virgin olive oil was found using this approach. Therefore, sunflower oil is a potential candidate for adulteration in most commercially available olive oils. Beside the classification of the different vegetable oils, we have successfully applied Raman spectroscopy in combination with partial least‐squares (PLS) regression analysis for very fast monitoring of adulteration of extra‐virgin olive oil with sunflower oil. Different mixtures of extra‐virgin olive oil with three different sunflower oil types were prepared between 5 and 100% (v/v) in 5% increments of sunflower oil. While in the present context the adulteration usually refers to the addition of reasonable amounts of the adulterant (given the similarity with the basic product), we show that the technique proposed can also be used for trace analysis of the adulterant. Without using techniques like surface‐enhanced Raman scattering (SERS), a quantitative detection limit down to 500 ppm (0.05%) could be achieved, a limit irrelevant for adulteration in commercial terms but significant for trace analysis. The qualitative detection limit even was at considerably lower concentration values. Based on PCA, a clear discrimination between pure extra‐virgin olive oil and olive oil adulterated with sunflower oil was achieved. The adulterant content was successfully determined using PLS regression with a high correlation coefficient and small root mean‐square error for both prediction and validation. Copyright © 2009 John Wiley & Sons, Ltd.