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

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AbstractThis 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°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.Practical ApplicationsThe developed lab‐scale infrared drying system can be used for drying other food commodities. The optimum drying performance plays a very prominent role based on the drying parameters and the quality attributes of the osmo‐sonicated assisted infrared drying. Enhancement of the drying performance depends on various factors like pretreatment, and actual drying condition prior to optimization. This research showed that increased drying performance in terms of mass transfer and quality attributes and optimum process conditions will be applied to the intelligent process design for any drying optimization for further development of large‐scale drying systems.