Heat transfer analysis of convective and microwave drying of dragon fruit

Ultrasound assisted phytochemical extraction of persimmon fruit peel: Integrating ANN modeling and genetic algorithm optimization

In the present study, ultrasound assisted extraction (UAE) of phytochemicals from persimmon fruit peel (PFP) was modeled using an artificial neural network (ANN) and optimized by integrating with genetic algorithm (GA). The range of process parameters selected for conducting the experiments was ultrasonication power (XU) 150---350 W, extraction temperatures (XT) 30---70 °C, solid to solvent ratio (XS) 1:15---1:35 g/ml, and ethanol concentration (XC) 40---80 %. The range of responses total phenolic content (YP), antioxidant activity (YA), total beta carotenoid (YB) and total flavonoid content (YF) at various independent variables combinations were found to be 7.72---24.62 mg GAE/g d.w., 51.44---85.58 %DPPH inhibition, 24.78---56.56 µg/g d.w. and 0.29---1.97 mg QE/g d.w. respectively. The modelling utilised an ANN architecture with a configuration of 4-12-4. The training process employed the Levenberg-Marquardt method, whereas the activation function chosen for the layers was the log sigmoid. The optimum condition predicted by the hybrid ANN-GA model for the independent variables, XU, XT, XS and XC was found to be 230.18 W, 50.66 °C, 28.27 g/ml, and 62.75 % respectively. The extraction process was carried out for 25 min, with 5-minute intervals, at various temperatures between 30 and 60 °C, to investigate the kinetic and thermodynamic characteristics of the process, under the optimal conditions of XU, XS and XC. The UAE of phytochemicals from persimmon peel followed pseudo second order kinetic model and the extraction process was endothermic in nature.

Ultrasound assisted extraction of red cabbage and encapsulation by freeze-drying: moisture sorption isotherms and thermodynamic characteristics of encapsulate

In the present study encapsulation of ultrasound assisted red cabbage extract was carried out using four different carrier agents such as maltodextrin, gum arbic, xanthan gum, and gellan gum. Among the four hydrocolloids investigated, maltodextrin was found to have the least destructive effect on anthocyanin content (14.87 mg C3G/g dw), TPC (54.51 ± 0.09 mg GAE/g dw), TFC (19.82 Mg RE/g dw) and antioxidant activity (74.15%) upon freeze-drying. Subsequently a storage study was conducted using maltodextrin as carrier agent at 25-50 °C. The Clausius-Clapeyron equation was used to evaluate the net isosteric heat (qst) of water adsorption. The differential entropy (ΔS) and qst decreased from 82.298 to 38.628 J/mol, and 27.518 kJ/mol to 12.505 kJ/mol, respectively as the moisture content increased from 2 to 14%. The value of isokinetic energy and Gibb's free energy were found to be 364.88 and - 1.596 kJ/mol for freeze dried red cabbage.

Graphical abstract

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 (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 (R²) 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 R² value of 0.998. On comparing the R² 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%.