Ultrasonic modulated osmotic dehydration of Aonla ( Phyllanthus emblica L.) slices: An integrated modeling through ANN, GPR, and RSM

Abstract

This study was aimed to explore the effect of ultrasonic treatment on osmotic dehydration of aonla slices. The Box–Behnken design (BBD) was utilized to design the experiment to study the effect of three process variables: ultrasonication time of 10–30 min, ultrasonication temperature of 30–50°C, and sugar concentration of 40–60°Bx. Artificial neural network (ANN), Gaussian process regression (GPR), and response surface methodology (RSM) (in terms of a second-order polynomial) were used to predict the mass transfer parameters (water loss, solid gain, and weight reduction), shrinkage ratio and color changes during osmotic dehydration of aonla slices. The predictive capabilities of the ANN, GPR, and RSM were compared by means of mean square error (RMSE), mean absolute error (MAE), average absolute deviation (AAD), standard error of prediction (SEP), chi-square statistic and coefficient of determination (R2). Results showed that the ANN model was found to be a more accurate predictor than the other models for all responses except shrinkage ratio where the GPR model was found to be better in predicting model. RSM also suggested the optimum processing conditions leading to maximizing water loss and weight reduction and minimizing solid gain to be at ultrasonication time of 10 min, ultrasonication temperature of 30°C, and sugar concentration of 60°Bx. Novelty impact statement Ultrasonic pretreated Aonla slices were found to have improved mass transfer dynamics during osmotic dehydration. Integrated Process modeling was explored and ANN was found to be a better process model compared to GPR and RSM. RSM predicted the optimum process parameters such as pretreatment time: 10 min, temperature: 30°C and sugar concentration: 60°Bx.