Detection of ternary mixtures of virgin olive oil with canola, hazelnut or safflower oils via non-targeted ATR-FTIR fingerprinting and chemometrics

Detection of extra virgin olive oil (EVOO) adulteration with ternary mixtures of undeclared foreign oils has rarely been reported in literature. The aim of the study was to develop a fit-for-purpose and highly sensitive non-targeted fingerprinting method via ATR-FTIR spectroscopy and qualitative modelling to detect ternary blends of a cheap unsaturated oil (safflower), two high in oleic acid oils (canola and hazelnut oils) at a standard adulteration level (20% v/v). An in-house spectral library for authentic EVOOs was exploited as reference data source. The 2nd derivative ATR-FTIR spectra were carefully explored for outliers throughout the multivariate calibration procedure using the Partial Least Squares Projection to Latent Structures-Discriminant Analysis (PLS-DA) and the Soft Independent Modelling of Class Analogy (SIMCA) methods. Stepwise external validation of the resulting models signified 100% sensitivity in detecting challenging cases of adulteration, i.e. with 5% canola and 15% hazelnut oils. Taken the natural variability in composition of authentic EVOOs from different producing regions and harvest year and the complexity of the studied mixtures, the overall predictive power of every resulting model was found quite high, >92%. A prospective for expanding application at lower adulteration levels i.e. 10% v/v was also evidenced.