Classification of edible vegetable oil using digital image and pattern recognition techniques

This work presents a simple and non-expensive method based on digital image and pattern recognition techniques for the classification of edible vegetable oils with respect to the type (soybean, canola, sunflower and corn) and the conservation state (expired and non-expired shelf life). For this purpose, vegetable oil sample images were obtained from a webcam and the frequency distribution of color indexes in the red-green-blue (RGB), hue (H), saturation (S), intensity (I), and grayscale channels were obtained. Linear discriminant analysis (LDA) was employed in order to build classification models on the basis of a reduced subset of variables. For the purpose of variable selection, two techniques were utilized, namely the successive projection algorithm (SPA) and stepwise (SW) formulation. For the study evolving the classification with respect to oil type, LDA/SPA and LDA/SW models achieved a correct classification rate (CCR) of 95% and 90% respectively. For the identification of expired and non-expired samples, LDA/SPA models were found to be the best method for classifying sunflower, soybean and canola oils, achieving a CCR in the overall data set of 97%, 94% and 93%, respectively, while the LDA/SW correctly classified at 100% for corn oil data. These results suggest that the proposed method is a promising alternative for the inspection of authenticity and the conservation state of edible vegetable oils. As advantages, the method does not use reagents to carry out the analysis and laborious procedures for chemical characterization of the samples are not required.