Evaluation of extra virgin olive oil stability by artificial neural network

Abstract

The stability of extra virgin olive oil in polyethylene terephthalate bottles and tinplate cans stored for 6months under dark and light conditions was evaluated. The following analyses were carried out: free fatty acids, peroxide value, specific extinction at 232 and 270nm, chlorophyll, L∗C∗h color, total phenolic compounds, tocopherols and squalene. The physicochemical changes were evaluated by artificial neural network (ANN) modeling with respect to light exposure conditions and packaging material. The optimized ANN structure consists of 11 input neurons, 18 hidden neurons and 5 output neurons using hyperbolic tangent and softmax activation functions in hidden and output layers, respectively. The five output neurons correspond to five possible classifications according to packaging material (PET amber, PET transparent and tinplate can) and light exposure (dark and light storage). The predicted physicochemical changes agreed very well with the experimental data showing high classification accuracy for test (>90%) and training set (>85). Sensitivity analysis showed that free fatty acid content, peroxide value, L∗Cab∗hab∗ color parameters, tocopherol and chlorophyll contents were the physicochemical attributes with the most discriminative power.