Application of computer vision and Support Vector Machines to estimate the content of impurities in olive oil samples

The determination of the content of impurities is a very frequent analysis performed on virgin olive oil samples, but the official method established in the European norm CE 2568/91 is quite work-intensive, and it would be convenient to have an alternative approximate method to evaluate the performance of the impurity removal process. In this work we develop a system based on computer vision and pattern recognition to classify the content of impurities of the olive oil samples in three sets, indicative of the goodness of the separation process of olive oil after its extraction from the paste. Starting from the histograms of the channels of the RGB, CIELAB and HSV color spaces, we construct an initial input parameter vector and perform a feature extraction previous to the classification. Several linear and non-linear feature extraction techniques were evaluated, and the classifiers used were Support Vector Machines. The best classification rate achieved was 87.66%, obtained using KPCA and a grade-3-polynomial kernel SVM.