Ultrasound-Enhanced Hot Air Drying of Germinated Highland Barley Seeds: Drying Characteristics, Microstructure, and Bioactive Profile

Drying kinetics and quality dynamics of ultrasound-assisted dried selenium-enriched germinated black rice

Black rice is a functional food due to its higher protein, fiber, iron, antioxidant compounds, and other health benefits than traditional rice. The ultrasonic (US) pretreatments (10, 20, and 50 min) followed by hot-air drying (50, 60, and 70 °C) were applied to study the drying kinetics, mathematical modeling, thermodynamics, microstructure, bioactive profile, volatile compounds and to lock the nutritional composition of selenium-enriched germinated black rice (SeGBR). Ultrasonic-treated samples exhibited a 20.5% reduced drying time than control ones. The Hii model accurately describes the drying kinetics of SeGBR with the highest R2 (>0.997 to 1.00) among the fifteen studied models. The activation energy values in US-SeGBR varied from 3.97 to 13.90 kJ/mol, while the specific energy consumption ranged from 6.45 to 12.32 kWh/kg, which was lower than untreated. The obtained thermodynamic attributes of dried black rice revealed that the process was endothermic and non-spontaneous. Gallic acid, kaempferol, and cyanidin 3-glucoside were present in high concentrations in phenolics, flavonoids, and anthocyanins, respectively. The HS-SPME-GC-MS investigation detected and quantified 55 volatile compounds. The US-treated SeGBR had more volatile compounds, which may stimulate the release of more flavorful substances. The scanning electronic micrograph shows that the US-treated samples absorbed high water through several micro-cavities. Selenium concentration was significantly higher in US-treated samples at 50 °C than in control samples. In conclusion, ultrasound-assisted hot-air drying accelerated drying and improved SeGBR quality, which is crucial for the food industry and global promotion of this healthiest rice variety.

Optimization and Prediction of the Drying and Quality of Turnip Slices by Convective-Infrared Dryer under Various Pretreatments by RSM and ANFIS Methods

Drying can prolong the shelf life of a product by reducing microbial activities while facilitating its transportation and storage by decreasing the product weight and volume. The quality factors of the drying process are among the important issues in the drying of food and agricultural products. In this study, the effects of several independent variables such as the temperature of the drying air (50, 60, and 70 °C) and the thickness of the samples (2, 4, and 6 mm) were studied on the response variables including the quality indices (color difference and shrinkage) and drying factors (drying time, effective moisture diffusivity coefficient, specific energy consumption (SEC), energy efficiency and dryer efficiency) of the turnip slices dried by a hybrid convective-infrared (HCIR) dryer. Before drying, the samples were treated by three pretreatments: microwave (360 W for 2.5 min), ultrasonic (at 30 °C for 10 min) and blanching (at 90 °C for 2 min). The statistical analyses of the data and optimization of the drying process were achieved by the response surface method (RSM) and the response variables were predicted by the adaptive neuro-fuzzy inference system (ANFIS) model. The results indicated that an increase in the dryer temperature and a decline in the thickness of the sample can enhance the evaporation rate of the samples which will decrease the drying time (40–20 min), SEC (from 168.98 to 21.57 MJ/kg), color difference (from 50.59 to 15.38) and shrinkage (from 67.84% to 24.28%) while increasing the effective moisture diffusivity coefficient (from 1.007 × 10⁻⁹ to 8.11 × 10⁻⁹ m²/s), energy efficiency (from 0.89% to 15.23%) and dryer efficiency (from 2.11% to 21.2%). Compared to ultrasonic and blanching, microwave pretreatment increased the energy and drying efficiency; while the variations in the color and shrinkage were the lowest in the ultrasonic pretreatment. The optimal condition involved the temperature of 70 °C and sample thickness of 2 mm with the desirability above 0.89. The ANFIS model also managed to predict the response variables with R² > 0.96.