Iranian Research Organization for Science and Technology (IROST) Innovative Food Technologies 2783-350X 5 4 2018 07 23 Modeling quality changes of sesame oil during extraction process using intelligent and regression system Modeling quality changes of sesame oil during extraction process using intelligent and regression system 627 636 665 10.22104/jift.2018.2725.1651 FA Mansour Assafi MSc student of Mechanics of Biosystems Engineering Department, Faculty of Agricultural Engineering and Rural Development, Khuzestan Agricultural Sciences and Natural Resources university (KAU), Mollasani, Iran. Rasoul Meamar-Dastjerdi Assistant professor of Biosystems Engineering Department, Faculty of Agricultural Engineering and Rural Development, , Khuzestan Agricultural Sciences and Natural Resources university (KAU), Mollasani, Iran. Mohammad Noshad Assistant professor of Food Science & Technology, Faculty of Animal Science and Food Technology, Khuzestan Agricultural Sciences and Natural Resources university (KAU), Mollasani, Iran 0000-0002-4060-9254 Journal Article 2018 01 14 Sesame is one of the most important oil seeds with high nutritional value and high functional properties in the world. Therefore, it is important to model and investigate the relationship between the factors that can affect the quality of sesame oil. . In this research, artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to predict the quality of sesame oil extracted by the press method. The model obtained from ANN had a higher correlation coefficient and less RMSE in predicting the quality of sesame oil extracted. Results showed the highest of acid values is related to the speed of 20 (rpm) and the lowest value is related to the speed of 80 (rpm), and increase temperature has increased it. Also, the highest of ions values was related to 80 (rpm) and extraction temperature 90 °C (1.13) and the lowest value was 80 (rpm) and 20 °C, (0.312). Increased temperature caused an increase acid value. Sesame is one of the most important oil seeds with high nutritional value and high functional properties in the world. Therefore, it is important to model and investigate the relationship between the factors that can affect the quality of sesame oil. . In this research, artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to predict the quality of sesame oil extracted by the press method. The model obtained from ANN had a higher correlation coefficient and less RMSE in predicting the quality of sesame oil extracted. Results showed the highest of acid values is related to the speed of 20 (rpm) and the lowest value is related to the speed of 80 (rpm), and increase temperature has increased it. Also, the highest of ions values was related to 80 (rpm) and extraction temperature 90 °C (1.13) and the lowest value was 80 (rpm) and 20 °C, (0.312). Increased temperature caused an increase acid value. https://jift.irost.ir/article_665_84117275be999ff55a987b9381e01f96.pdf