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In this study, cinnamon essential oil (CEO) nanocapsules were stabilized by means of bacterial cellulose nanocrystals (BCNCs) and encapsulated using fish gelatin as the main polymer phase. Emulsions were prepared at pH 5 using different CEO concentrations (0.03, 0.06, 0.12, 0.24, 0.36, and 0.48% v/w) and a fixed amount of fish gelatin (3% w/w) and BCNCs (0.06% w/w). The controlled release of the essential oil was assessed by release studies, which revealed that the higher the CEO concentration, the lower the release rate of CEO. In addition, modelling of experimental data using five different equations showed that the best fitting was obtained for the Korsmeyer-Peppas model, according to which the CEO release obeyed a non-Fickian behavior. Films obtained from the same formulations were characterized in terms of optical properties (light transmittance and haze), surface wettability, barrier (oxygen, carbon dioxide, and water vapor transmission rates) and mechanical properties. It was observed that an increased amount of CEO in the films did not significantly affect both transparency and haze, while it yielded an increase in surface hydrophobicity (~ 120% increase in water contact angle over the control) and elongation. Finally, the barrier performances of films against O2, CO2, and water vapor suggest a potential application of CEO/GelA-BCNC matrices as antimicrobial layers (in the form of coatings deposited on plastic films or directly on food) in living foods that have a respiratory metabolism, such as modified atmosphere-packaged crustaceans and mollusks as well as fruits and vegetables.

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In order to discover the most accurate prediction of yield stress, UTS and elongation percentage, the effects of various training algorithms on learning performance of the neural networks were investigated. Different primary and secondary dendrite arm spacings were used as inputs, and yield stress, UTS and elongation percentage were used as outputs in the training and test modules of the neural network. After the preparation of the training set, the neural network was trained using different training algorithms, hidden layers and neuron numbers in hidden layers. The test set was used to check the system accuracy of each training algorithm at the end of learning. The results show that Levenberg-Marquardt learning algorithm gave the best prediction for yield stress, UTS and elongation percentage of A356 alloy.