Ascorbic acid nanoencapsulation using polyelectrolyte complex formation and optimization using hybrid artificial neural network-genetic algorithm

Backgrounds: The polyelectrolyte complex (PEC) formation using chitosan (Cs) and alginate (Alg) has been employed to carry out Vitamin C (VC) nanoencapsulation because of its instability in adverse conditions. This method includes VC nanoencapsulation using physical cross-linking. The effect of the mixing order as well as the different mass ratios of both the polymer and the VC content, were investigated on the size, pdi, zeta potential, encapsulation efficiency (EE%), and yield%. Hence, considering different mass ratios of both polymer (4:1-1:4) and VC content (10%-30% w/w of Cs) as independent variables, PECs were formed using 0.1% (w/v) of Cs and Alg solution. Result: The result showed that, for equal mass ratio (1:1) of Cs and Alg, the addition of Alg solution to Cs solution led to lower particle size, while higher particle size was observed with reverse order of addition. However, no significant effect of polymer mass ratios was observed on particle size and NPs stability, while EE varied with VC concentration. Artificial neural network (ANN) was applied to train the experimental parameters Cs: Alg ratios (X1:X2) and VC content (X3) for the output variables. Moreover, process optimization was carried out using multi-objective genetic algorithm (MOGA) with the goal of lowering particle size and increasing EE while increasing nanoparticle yield. Conclusion: The PEC method effectively encapsulated VC as obtained from higher EE. The Cs: Alg ratio of 4:2.1 and 18% VC content (w/w of Cs) was found optimum for nanoencapsulation. The final ANN-GA model showed an acceptable agreement with experimental data.