Caractérisation de l'huile d'olive algérienne

Abstract

Olive oil is one of the oldest vegetable oils and the only one that can be consumed in its raw form without prior treatment. The well-known health benefits of olive oil are linked to its fatty acid composition in which oleic acid is the main component, and the presence of minor biomolecules, such as vitamins and natural antioxidants. The strong demand for quality virgin olive oil is not only due to its health virtues, but also to its organoleptic properties. In Algeria, plantations are characterized by great diversity and for the most part, by a traditional olive orchard. The consumption of oil maâsras is today a basic diet in many rural areas of the country. In these regions, oils are considered to be of good quality. The present study on olive oil produced in several olive growing regions of Algeria has the dual purpose of studying the chemical parameters and fatty acid composition by conventional analytical methods, and of characterizing these oils by spectral methods, including 3D- fluorescence spectroscopy. Different chemometric methods: Principal Component Analysis (PCA), Canonical Discriminant Analysis (CDA), Independent Component Analysis (ICA), and PARAFAC (PARAllel FACtor analysis) are used to treat the data. The first results indicate that the chemical composition of olive oils from different regions of Algeria is quite variable. The Principal Component Analysis (PCA) was used for an initial exploratory analysis of the data. The first three latent variables explain 64.1% of the total variance of the samples from the five regions. Then, a Canonical Discriminant Analysis (CDA) showed that the most discriminant variables are K270 and Delta K for oil samples from the region of Constantine, the peroxide and γ-tocopherol for samples from the regions of Tizi Ouzou and Sétif, respectively. The variations due to the presence of Rayleigh and Raman scattering in excitation– emission fluorescence matrices, distort the PARAFAC model. A solution to eliminate the artefacts interfering with the relevant information was achieved by applying Independent Component Analysis to the row-wise unfolded array. A PARAFAC model was then built on the refolded array, reconstructed without the independent component(s) related to the “artefact signals”. The PARAFAC components obtained were interpreted by relating them to some compounds present in the analysed samples. The results of multivariate analyses show that the chemical parameters of olive oils contain enough information to distinguish the production areas of studied Algerian oil.