Modelling of ultrasonic assisted osmotic dehydration of cape gooseberry using adaptive neuro-fuzzy inference system (ANFIS)

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 ([Formula: see text]) with a range of 100–500 W, immersion time ([Formula: see text]) in the range of 30–55 min, solvent concentration ([Formula: see text]) of 45–65 % and solid to solvent ratio ([Formula: see text]) with range 1:6–1:14 w/w. The effect of these process parameters on the responses weight loss ([Formula: see text]), solid gain ([Formula: see text]), change in color ([Formula: see text]) and water activity ([Formula: see text]) 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 ([Formula: see text]) 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 [Formula: see text] value of 0.998. On comparing the [Formula: see text] 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 [Formula: see text] and minimum [Formula: see text] , [Formula: see text] and [Formula: see text]. 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 [Formula: see text] of 282.434 W, [Formula: see text] of 50.280 min, [Formula: see text] of 55.836 % and [Formula: see text] 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%.