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The quality of virgin olive oil obtained in the milling process is directly bound to the characteristics of the olives. Hence, the correct classification of the different incoming olive batches is crucial to reach the maximum quality of the oil. The aim of this work is to provide an automatic inspection system, based on computer vision, and to classify automatically different batches of olives entering the milling process. The classification is based on the differentiation between ground and tree olives. For this purpose, three different species have been studied (Picudo, Picual and Hojiblanco). The samples have been obtained by picking the olives directly from the tree or from the ground. The feature vector of the samples has been obtained on the basis of the olive image histograms. Moreover, different image preprocessing has been employed, and two classification techniques have been used: these are discriminant analysis and neural networks. The proposed methodology has been validated successfully, obtaining good classification results.

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The quality of virgin olive oil is related to the agronomic conditions of the olive fruits and the process variables of the production process. Nowadays, food markets demand better products in terms of safety, health and organoleptic properties with competitive prices. Innovative techniques for process control, inspection and classification have been developed in order to to achieve these requirements. This paper presents a review of the most significant sensing technologies which are increasingly used in the olive oil industry to supervise and control the virgin olive oil production process. Throughout the present work, the main research studies in the literature that employ non-invasive technologies such as infrared spectroscopy, computer vision, machine olfaction technology, electronic tongues and dielectric spectroscopy are analysed and their main results and conclusions are presented. These technologies are used on olive fruit, olive slurry and olive oil to determine parameters such as acidity, peroxide indexes, ripening indexes, organoleptic properties and minor components, among others. © 2016 Society of Chemical Industry.