Modeling and optimization of osmo-sonicated dehydration of garlic slices in a novel infrared dryer using artificial neural network and response surface methodology

This investigation aims to develop a novel infrared drying system and optimization of ultrasound-assisted osmotic dehydration of garlic slices. RSM and ANN approaches are used for modeling and optimization of the process parameters. The factors sonication time, sonication temperature, and osmotic concentration, were considered to optimize responses, that is, solid gain (SG), weight loss (WL), rehydration ratio (RR), drying rate (DR), and allicin content (AC). The ultrasonic time and osmotic concentration had significant effects on mass transfer parameters and the quality of dried garlic slices. The optimum drying conditions were found at an ultrasonic temperature: 20 & DEG;C, ultrasound time: 28.54 min, and osmotic concentration: 55.58% while the optimum responses were WL: 25.839%, SG: 3.557%, RR: 7.512, DR: 0.163 (g H2O/g.dm/min), and AC: 15.229 (mg/g). The prediction of the responses revealed that both RSM and ANN approaches can predict the model precisely but the ANN models showed higher accuracy.