Atr–Ftir spectroscopy combined with chemometrics for rapid classification of extra virgin olive oils and edible oils from different cultivars available on the Turkish markets

In this study, a combination of attenuated total reflectance–Fourier transform infrared (ATR–FTIR) spectroscopy and chemometric techniques was used to differentiate the type of extra virgin olive oils (VOO) and edible oils available on the Turkish markets. A total of 144 spectra of samples, including extra VOO, hazelnut oil (HNO), cottonseed oil (CSO), sunflower oil (SFO) and soybean oil (SBO), was recorded. Differentiation of edible oils was performed using principal component analysis (PCA), hierarchical cluster analysis (HCA), linear discriminant analysis (LDA) and soft independent modeling of class analogies (SIMCA). The spectra collected from wavenumber region of 4000–650 cm-1 and 28 different wavenumber ranges were evaluated for optimal models. All multivariate analysis provided excellent discriminations between the edible oil classes with low classification error. LDA models constructed with five predictors and a total of 100% of edible oil samples from different types were correctly classified. Furthermore, no misclassification was reported for the supervised SIMCA models constructed for the discrimination of VOOs from SFO, SBO and CSO samples, with an accuracy of 95%. Consequently, ATR–FTIR spectroscopy combined with multivariate data analyses provides excellent illustrations of the relative positions of the different types of commercial edible oils according to their quality and purity.