Edible oils are an important constituent of human diet because they provide desirable nutritional properties, flavor and texture of food. Olive oil is one of the most frequently used edible oils. Therefore knowledge of its physicochemical properties is indispensable to assess its quality. In fact, there are many factors having an influence on the chemical and physical characteristics of olive oil, such as the climatic conditions, the agronomic and genetic factors.
In Morocco, different varieties have been developed in order to improve the yield and oil quality. But few comparative studies were made between the different product varieties. The aim of this study was to characterize the olive oils from five olive varieties most cultivated in Morocco. Then compare them by using the physic-chemical parameters with storage conditions (darkness and sunlight). Several parameters were studied, namely, quality indices defined by the International Olive Oil Council (IOOC): The acid value "Av", the peroxide value "Pv" and the specific extinctions "K232 / K270". The results of different analyses show significant differences between these five varieties, and demonstrate that the Picholine de Languedoc variety is the most efficient in term of quality for the consumer.

The aim of this work was to characterize and classify three close regions of olives by direct analysis on the olive without any preliminary treatment. This study was focused on the olive samples picked in the three zones: named Bazaza, oled ayad and oled hamdan, in the Moroccan region of Beni Mellal. All samples were also analysed by FT-IR spectroscopy, the spectral data were subjected to a preliminary derivative transform based on the gap segment algorithm to reduce the noise and extract a largest number of analytical information from spectra. A multivariate statistical procedure based on cluster analysis (CA) coupled to support vector machines (SVM), was elaborated, providing an effective classification method. On the basis of a hierarchical agglomerative CA and principal component analysis (PCA), three distinctive clusters were recognized. The SVM procedure was then applied to classify samples from the same regions. The model resulted able to separate the three classes and classify new objects into the appropriate defined classes with a percentage prediction of 93%. The results showed that FTIR spectroscopy coupled with chemiometric methods are an interesting technique for classifying olive samples according to their geographical origins.