Derivative FTIR spectroscopy for cluster analysis and classification of morocco olive oils

Abstract Fourier transform infrared (FTIR) spectra were employed for differentiation and classification of olive oils from several producing regions of Morocco. A preliminary treatment of the FTIR data was done by a derivative elaboration based on the Savitzky–Golay algorithm to reduce the noise and extract a largest number of analytical information from the spectra. A multivariate statistical procedure based on cluster analysis (CA) coupled to partial least squares-discriminant analysis (PLS-DA), was elaborated, providing an effective classification method. On the basis of a hierarchical agglomerative CA and principal component analysis (PCA), four distinctive clusters were recognised. The PLS-DA procedure was then applied to classify samples from the same regions, picked in different times, or unknown olive oil samples. The model was optimised by applying the Martens’ Uncertainty Test that provided to select the wavelength zones giving the most useful analytical information. The proposed method furnished results reliable in classifying olive oils from different lands with the advantages of being rapid, inexpensive and requiring no prior separation procedure.