Practical modeling and optimization of ultrasound-assisted bleaching of olive oil using hybrid artificial neural network-genetic algorithm technique

Abstract Multi-objective modeling and optimization of ultrasound-assisted bleaching of olive oil were accomplished by a hybrid artificial neural network (ANN) and genetic algorithm (GA) method using an ultrasonic bath with a frequency of 25 kHz. The influence of process parameters including ultrasonic power, bleaching clay dosage, process temperature and time (inputs) on final Lovibond red (Lr) and peroxide value (PV) (outputs) was modeled by a multilayer feed-forward back propagation ANN. The accurate 2-hidden layer model with 20 neurons in each, high R 2 (up to 90%) and minimum mean square error (MSE) obtained by ANN was introduced to GA to find the best operation conditions to achieve minimum Lr and PV. The optimum treatment was found with ultrasonic power of 30%, bleaching clay of 1.2%, bleaching time of 13 min and temperature of 65 °C. Under optimal conditions, Lr and PV were 2.47 and 6.49 (meqO 2 /kg), respectively, that were consistent with predicted values. Optimally ultrasonic bleached olive oil and an industrially bleached olive oil were compared. In most cases, the results indicated no detrimental effects of ultrasound on oil structure. Thus, 40% reduction in bleaching clay dosage, 35% reduction in process temperature and 57% reduction in time over ultrasound-assisted bleaching which not only provided economic and environmental benefits, but also retained edible oil nutritional value in comparison to common bleaching procedure. The results of this study confirm the applicability of ultrasound-assisted bleaching by ultrasonic bath as an economic and feasible approach for bleaching of olive oil to reduce high bleaching costs.