Abstract
In the present investigation, the cape gooseberry (Physalis peruviana L.) was preserved by the application of osmotic dehydration (sugar solution) with ultrasonication. The experiments were planned based on central composite circumscribed design with four independent variables and four dependent variables, which yielded 30 experimental runs. The four independent variables used were ultrasonication power (X(P)) with a range of 100-500 W, immersion time (X(T)) in the range of 30-55 min, solvent concentration (X(C)) of 45-65 % and solid to solvent ratio (X(S)) with range 1:6-1:14 w/w. The effect of these process parameters on the responses weight loss (Y(W)), solid gain (Y(S)), change in color (Y(C)) and water activity (Y(A)) of ultrasound assisted osmotic dehydration (UOD) cape gooseberry was studied by using response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS). The second order polynomial equation successfully modeled the data with an average coefficient of determination (R(2)) was found to be 0.964 for RSM. While for the ANFIS modeling, Gaussian type membership function (MF) and linear type MF was used for the input and output, respectively. The ANFIS model formed after 500 epochs and trained by hybrid model was found to have average R(2) value of 0.998. On comparing the R(2) value the ANFIS model found to be superior over RSM in predicting the responses of the UOD cape gooseberry process. So, the ANFIS was integrated with a genetic algorithm (GA) for optimization with the aim of maximum Y(W) and minimum Y(S), Y(C) and Y(A). Depending on the higher fitness value of 3.4, the integrated ANFIS-GA picked the ideal combination of independent variables and was found to be X(P) of 282.434 W, X(T) of 50.280 min, X(C) of 55.836 % and X(S) of 9.250 w/w. The predicted and experimental values of response at optimum condition predicted by integrated ANN-GA were in close agreement, which was evident by the relative deviation less than 7%.