Thermodynamic and Quality Performance Studies for Drying Kiwi in Hybrid Hot Air-Infrared Drying with Ultrasound Pretreatment

Physicochemical Properties of Dried and Powdered Pear Pomace

Pear pomace, a byproduct of juice production, represents a valuable reservoir of bioactive compounds with potential health benefits for humans. This study aimed to evaluate the influence of drying method and temperature on pear pomace, specifically focusing on the drying kinetics, grinding characteristics, color, phenolic profile (LC-MS/MS), and antioxidant activities of the powder. Drying using the contact method at 40 °C with microwave assistance demonstrated the shortest duration, whereas freeze-drying was briefer compared to contact-drying without microwave assistance. Freeze-drying resulted in brighter and more easily comminuted pomace. Lyophilized samples also exhibited higher total phenolic compound levels compared to contact-dried ones, correlating with enhanced antioxidant activity. Twenty-one phenolic compounds were identified, with dominant acids being quinic, chlorogenic, and protocatechuic. Flavonoids, primarily isoquercitrin, and rutin, were also presented. Pear pomace dried via contact at 60 °C contained more quinic and protocatechuic acids, while freeze-dried pomace at the same temperature exhibited higher levels of chlorogenic acid, epicatechin, and catechin. The content of certain phenolic components, such as gallic acid and epicatechin, also varied depending on the applied drying temperature.

Qualitative, energy and environmental aspects of microwave drying of pre-treated apple slices

In the present research, response parameters such as specific energy consumption (SEC), thermal efficiency (TE), energy efficiency (EF), drying time (DT), greenhouse gas (GHG) emission (such as CO2 and NOx), and quality features (color variation and shrinkage) were modeled by response surface methodology (RSM) for apple slices dried in a microwave dryer under ultrasonication (30 ℃-10 min) and blanching (80 °C-2 min) pretreatments. Also, RSM was applied to optimize two independent parameters including microwave power and sample thickness in the levels 100, 200, and 300 W and 2, 4, and 6 mm, respectively. The results indicated the significant influence (P < 0.01) of the independent parameters on the response parameters. The vales of SEC, DT, GHG emission, shrinkage, and color difference were linearly decreased with the declining sample thickness and increasing microwave power, while the energy and thermal efficiencies were increased by a quadratic equation. The use of ultrasonication and blanching pretreatments decreased the SEC, GHG emissions, and DT; while improving the quality of the samples as compared to the non-treated slices. The optimization results showed the optimal drying times (31.55, 82.19, and 50.55 min), SEC (3.42, 10.07, and 4.37 MJ/kg), CO2 with natural gas (1539.75, 1518.75, and 4585 g), CO2 with gas oil (3662.53, 2099.25, 2721.25 g), NOx with natural gas (10.094, 9.956, and 12.906 g), and NOx with gas oil (12.934, 12.758, and 16.538 g) at a microwave power of 300 W and sample thickness of 2 mm with desirability of 0.921, 0.935, and 0.916 for control samples, ultrasonicated, and blanched, respectively.

Novel sequential and simultaneous infrared-accelerated drying technologies for the food industry: Principles, applications and challenges

Infrared drying (IRD) is considered an innovative drying solution for the food industry with advantages of energy-saving potentials, reduced drying time and production cost-effectiveness. However, IRD also suffers from drawbacks such as weak penetrative ability, and product overheating and burning. Therefore, over the years, significant progress has been made to overcome these shortcomings by developing infrared-accelerated drying (IRAD) technology based on the combination of IRD with other drying technologies. Although several reviews have been published on IRD, no review focusing on IRAD is yet available. The current review presents up-to-date knowledge and findings on the applications of IRAD technologies for enhancing the quality and safety of food. The fundamental principles and characteristics of IRAD, energy-saving potentials, simulation and optimization approaches for enhancing efficiency, and developments in various acceleration approaches by combining with other drying techniques for achieving better end-products are discussed, and challenges and future work for developing the novel accelerated drying technology are also presented. Due to the synergistic effects of sequential or simultaneous combined drying methods, the total drying time and energy required are drastically lowered with most IRAD technologies, and consequently there are significant improvements in the sensory, nutritional, and safety attributes of dried food products with better appearance and quality. The development of multi-wavelength IRAD systems based on infrared absorption bands, and the incorporation of novel sensing techniques for real-time monitoring during drying will further enhance process efficiency and food quality and safety.

Application of Artificial Neural Networks, Support Vector, Adaptive Neuro-Fuzzy Inference Systems for the Moisture Ratio of Parboiled Hulls

Drying as an effective method for preservation of crop products is affected by various conditions and to obtain optimum drying conditions it is needed to be evaluated using modeling techniques. In this study, an adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and support vector regression (SVR) was used for modeling the infrared-hot air (IR-HA) drying kinetics of parboiled hull. The ANFIS, ANN, and SVR were fed with 3 inputs of drying time (0–80 min), drying temperature (40, 50, and 60 °C), and two levels of IR power (0.32 and 0.49 W/cm2) for the prediction of moisture ratio (MR). After applying different models, several performance prediction indices, i.e., correlation coefficient (R2), mean square error index (MSE), and mean absolute error (MAE) were examined to select the best prediction and evaluation model. The results disclosed that higher inlet air temperature and IR power reduced the drying time. MSE values for the ANN, ANFIS tests, and SVR training were 0.0059, 0.0036, and 0.0004, respectively. These results indicate the high-performance capacity of machine learning methods and artificial intelligence to predict the MR in the drying process. According to the results obtained from the comparison of the three models, the SVR method showed better performance than the ANN and ANFIS methods due to its higher R2 and lower MSE.

Selected Quality Parameters of Air-Dried Apples Pretreated by High Pressure, Ultrasounds and Pulsed Electric Field-A Comparison Study

The aim of this work was to compare selected physicochemical properties of air dried ‘Golden Delicious’ apples, pretreated either by high-pressure processing (HPP), ultrasound (US) or pulsed electric field (PEF). Following parameters of pretreatment were used: HPP–400 MPa for 15 min, US–21 kHz, 180 W for 45 min, PEF–1 kV/cm, 3.5 kJ/kg. The quality of materials was evaluated by their rehydration properties, hygroscopicity, color and total phenolic content. To compare the effectiveness of the utilized methods, determined properties were expressed as relative comparison values against the reference sample obtained without any pretreatment in the same conditions. The performed research demonstrated that properties can be shaped by the application of proper pretreatment methods. For instance, PEF was shown to be the best method for improving water uptake during rehydration, whereas HPP was the most effective in decreasing hygroscopic properties in comparison with untreated dried apples. Among the investigated methods, HPP resulted in the deepest browning and thus total color difference, while the effects of US and PEF were comparable. For all pretreated dried apples, the total phenolic content was lower when compared with reference material, though the smallest drop was found in sonicated samples.

Exergy and Energy Analyses of Microwave Dryer for Cantaloupe Slice and Prediction of Thermodynamic Parameters Using ANN and ANFIS Algorithms

The study targeted towards drying of cantaloupe slices with various thicknesses in a microwave dryer. The experiments were carried out at three microwave powers of 180, 360, and 540 W and three thicknesses of 2, 4, and 6 mm for cantaloupe drying, and the weight variations were determined. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were exploited to investigate energy and exergy indices of cantaloupe drying using various afore-mentioned input parameters. The results indicated that a rise in microwave power and a decline in sample thickness can significantly decrease the specific energy consumption (SEC), energy loss, exergy loss, and improvement potential (probability level of 5%). The mean SEC, energy efficiency, energy loss, thermal efficiency, dryer efficiency, exergy efficiency, exergy loss, improvement potential, and sustainability index ranged in 10.48–25.92 MJ/kg water, 16.11–47.24%, 2.65–11.24 MJ/kg water, 7.02–36.46%, 12.36–42.70%, 11.25–38.89%, 3–12.2 MJ/kg water, 1.88–10.83 MJ/kg water, and 1.12–1.63, respectively. Based on the results, the use of higher microwave powers for drying thinner samples can improve the thermodynamic performance of the process. The ANFIS model offers a more accurate forecast of energy and exergy indices of cantaloupe drying compare to ANN model.