Iranian Research Organization for Science and Technology (IROST) Innovative Food Technologies 2783-350X 4 3 2017 04 21 Monitoring of olive oil oxidation during accelerated storage using machine vision system Monitoring of olive oil oxidation during accelerated storage using machine vision system 77 88 444 10.22104/jift.2017.444 FA Alireza Sanaeifar Ph.D. student, Department of Biosystems Engineering, School of Agriculture, Shiraz University Abdolabbas Jafari Associate Professor, Department of Biosystems Engineering, School of Agriculture, Shiraz University S. Mehdi Nassiri Associate Professor, Department of Biosystems Engineering, School of Agriculture, Shiraz University M. Taghi Golmakani Associate Professor, Department of Food Science and Technology, School of Agriculture, Shiraz University Mohammad Eghtesad Professor, Department of Solid Mechanics Engineering, School of Mechanical Engineering, Shiraz University Journal Article 2016 11 14 In this research, the ability of a machine vision system was evaluated to monitor the oxidation period of olive oil. For this purpose, the olive oil samples studied during accelerated storage for 24 days in an oven and then image processing processes was carried out to extract parameters related to color spaces RGB, HSV and L*a*b*. The performance of artificial neural network and decision tree techniques compared to determine the olive oil oxidation rate. In each of the models color parameters were used as inputs and different stages in the olive oil oxidation period were considered as output. According to the results, the highest classification accuracy (94.44%) and the lowest RMSE (0.0696) are related to the decision tree technique. The proposed machine vision system combined with artificial intelligence techniques as non-destructive and efficient tool will offer for monitoring and quality control during oil storage. In this research, the ability of a machine vision system was evaluated to monitor the oxidation period of olive oil. For this purpose, the olive oil samples studied during accelerated storage for 24 days in an oven and then image processing processes was carried out to extract parameters related to color spaces RGB, HSV and L*a*b*. The performance of artificial neural network and decision tree techniques compared to determine the olive oil oxidation rate. In each of the models color parameters were used as inputs and different stages in the olive oil oxidation period were considered as output. According to the results, the highest classification accuracy (94.44%) and the lowest RMSE (0.0696) are related to the decision tree technique. The proposed machine vision system combined with artificial intelligence techniques as non-destructive and efficient tool will offer for monitoring and quality control during oil storage. https://jift.irost.ir/article_444_550a141f12de6341fba65b0ad0433500.pdf