Characterization and classification of PGI Moroccan Argan oils based on their FTIR fingerprints and chemical composition

In this work Fourier Transform Infrared Spectroscopy (FTIR) was selected as a reliable, fast and non-destructive technique to record spectroscopic fingerprints of Moroccan Protected Geographical Indication (PGI) Argan oils. Classification and discrimination according to their five geographical origins (Ait-Baha, Agadir, Essaouira, Tiznit and Taroudant) was performed. A total of 120 PGI Argan oil samples were collected during four harvest seasons between 2011 and 2014.First, several physicochemical parameters were measured, i.e. free acidity, peroxide value, spectrophotometric indices, fatty acid composition, tocopherols and sterols content. Secondly, FTIR fingerprints were recorded for all samples. The data was subjected to Principal Component Analysis (PCA) for visualization and to reveal differences between samples. Classification models were developed by Partial Least Squares Discriminant Analysis (PLS-DA). Mathematical data pre-treatments were applied to improve the performance of the multivariate classification models. The results obtained, based on both the chemical composition and the spectroscopic fingerprints, indicate that PCA plots were able to distinguish the five sample classes. PLS-DA models based on either chemical composition or FTIR spectra gave a good prediction and an accurate discrimination between the samples from different regions. The proposed approach with the FTIR spectra provided reliable results to classify the Moroccan PGI Argan oils from different regions in a rapid, inexpensive way requiring no prior separation procedure.