Classification of edible fats and oils by principal component analysis of Fourier transform infrared spectra

Principal component analysis of Fourier transform infrared (FTIR) spectra is performed to classify edible fats and oils with regard to their origin. Convenient sampling methods are proposed to record reproducible spectra: horizontal attenuated total reflection for fat samples and mid-IR optical fibre method for oils. The first principal component extracted from the first derivatized spectra of fats allows separation of butters and vegetable margarines on the basis of the sign of the projection. The interpretation of this first principal component establishes that the difference is due to the concentration of unsaturated fatty acids. For the oil samples, the spectral data must be second derivatized and again the first principal component separates sunflower seed oils from the olive and peanut extracts by the different concentrations of linoleic acid. The major interest of these methods using chemometric analysis of spectral data is in their rapidity, since no chemical treatment of samples is required.