Intelligent Estimation of the Canola Oil Stability Using Artificial Neural Networks

In the present study, a multi-layer perceptron neural network and radial basis function (RBF) network were used to estimate the oxidative stability of canola oil during storage. Artificial neural networks (ANNs) were used to model oxidative stability of canola oil during storage, and comparison was also made with the results obtained from a regression analysis. The oxidative stability of canola oils was considered as dependent variable, and independent variables were selected as time (in week), variety, C14:0, C16:0, C18:0, C20:0, C18:1, C18:2, C18:3, and C22:1 fatty acid content. The results were compared with experimental data and it was found that the estimated oxidative stability by RBF neural network is more accurate than multi-layer perceptron network and regression model. It was also found that the oxidative stability of canola oil decreased with increase in storage time and C18:3 fatty acid content.