عنوان مقاله [English]
The prediction of olive oil quality parameters is of great importance in Modern Approaches to Quality Control. One of the main problems when predicting oil quality during storage is the complexity of the physicochemical properties of raw material and the difference of data to various causes. Modeling oxidative stability of olive oil with fuzzy inference neural adaptation can help to improve the quality control process this product. One of the important parameters controlling of olive oil is oxidative stability. Parameters of the model design were used Acidity, peroxide value (PV) specific extinction coefficient K232, phenolic compounds as input variables and the extinction coefficient k270 as the output. In order to develop ANFIS model were used various membership functions types, number of membership functions, learning algorithms, learning cycles, Different membership function types by trial and error. The best model using trapezoidal membership functions, and 3 3 3 3 3 numbers of memberships and 50 training cycle were obtained that it was determined least mean square error 0.0012 and the best regression coefficient 0.997.Analysis of the model revealed that the Adaptive neuro fuzzy inference system a powerful tool to predict the oxidative stability of olive oil.