Modeling quality changes of sesame oil during extraction process using intelligent and regression system

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.