Modeling the encapsulation efficiency and stability of curcumin in cellulose Pickering emulsions using artificial neural network (ANN) and classification and regression tree (CART) algorithms

The stability of curcumin and its encapsulation efficiency in emulsion are among the most important factors determining its bioavailability and absorption in the body. For this purpose, parameters affecting these two factors, including time, pH, and cellulose concentration, were used as input variables in the present study. Curcumin stability and encapsulation efficiency were used as response variables in artificial neural networks and decision tree algorithms. In this regard, cellulose nanocrystal obtained from acid hydrolysis was used to prepare curcumin Pickering emulsion with oil:water ratio of 5:95 and cellulose concentration 1, 1.5, 2, 2.5, and 3% (w/v) and the encapsulation efficiency and stability of curcumin were measured during 8 days. The results showed that the encapsulation efficiency significantly increased as cellulose concentration increased. Encapsulation efficiency at pH 7 was higher than at pH 2 (p≤0.05). The curcumin content in the emulsions prepared at pH 2 drastically decreased during storage, while it was less degraded in emulsions at pH 7 after 8 days of storage. The modelling results for curcumin stability and encapsulation efficiency based on R2 and RMSE% statistics showed that MLP 3-6-1 (R2=0.99; RMSE%=10.00) and RBF 2-6-1 (R2=0.99; RMSE %=9.99) were had more accuracy than other models. Finally, the results showed that the artificial neural network algorithm performed better than the decision tree in predicting cellulose Pickering emulsions' encapsulation efficiency and curcumin stability.