Introduction
Drying of a variety of food product is carried out to reduce their moisture levels in order to increase the shelf life, reduce the probability of developing fungi and to facilitate further processing to obtain the final product. Drying of almonds exhibit many characteristic features such as non-spherical shape, swelling/ shrinkage as a function of moisture content, uneven drying because of their peculiar shape and proximity to other almond kernels and so on. In recent years, different drying methods have been developed for different types of food materials, including hot air (HA) drying, infrared drying, vacuum drying, etc. For tree nuts, microwave drying, radiofrequency drying, and ultrasound assisted HA drying have also been studied in bench scale. However, HA drying in the deep-bed bin dryers is the most commonly applied technology in the industry, considering its large drying throughputs and relatively low cost.
Material and Methods
The main objectiveof this research is to dry almond nuts optimally using 3 combined microwave-vacuum, continuous and infrared dryers and finding the best method by artificial neural networks. 3 microwave power levels of 250, 450 , 650 watts, 3 levels of vacuum pressure of 25, 45, 65 (kPa), 3 levels of linear speed of 15, 39, 63 〖cm m〗^(-1) of belt, and 3 levels of infrared radiation power of 500, 1000, 1500 watts were performed. In during the process of drying almond kernels, its initial moisture content was reduced from about 46% to about 6% based on the weight of dry matter. In order to predict the moisture content of almond kernels, Mathematical Model was selected based on the lowest value of root mean square error (RMSE) and χ2 and the highest value of coefficient of determination (R^2).
Results and Discussion
Based on the Midili model, in the combined microwave-vacuum dryer, the percentage of shrinkage is from 8.5 to 15%. General color changes from 2.6 to 14, activation energy from 21.2to 39.7 (kJ mol-1), effective moisture diffusion coefficient from1.2×10-8 to 6.16×10-8 (m2 s-1), and specific energy consumption from 0.06 to 0.17 (GJ kg-1) was variable. Combined microwave-continuous dryer, shrinkage percentage from 7.1 to 12.1%, color changes from 8.5 to 19, activation energy from 11.75to 24.7 (kJ mol-1), effective moisture diffusion coefficient from 1.1×10-8 to 9.9×10-8 (m2 s-1) and specific energy consumption values were calculated from 0.9 to 10.5 (GJ kg-1). Combined microwave-infrared dryer, fading percentage from 8.7 to 15%, color changes from 7.1 to 22.17, activation energy from 10.95 to 21.05 (kJ mol-1), effective diffusion coefficient humidity from1.4×10-8 to4.5×10-8 (m2 s-1) and the specific energy consumption was calculated from 0.2 t 0.6 (GJ kg-1).
The optimal point of drying almond kernels of combined microwave-infrared dryer at air temperature of 45 (0C) and power of 446 (W) of microwave and power of 1500(W) of infrared radiation was obtained by forward backpropagation network and Bayesian adjustment training function. Combined microwave-vacuum dryer, The most optimal point at air temperature of 45(0C), microwave power of 650(W) and vacuum pressure of 25 (kPa) was obtained by the pre-propagation network and Lunberg-Marquat training function. Combined microwave-continuous dryer, the most optimal point at air temperature of 45(0C) and microwave power of 450(W) and belt linear speed of 59(cm/min) was obtained by feed back propagation network with Lunberg-Marquat training function. Optimum brightening of almond kernels in the response surface method of combined microwave-infrared dryer, the most optimal point in shrinkage is 7% and color change is 10.5, and the amount of specific energy consumption is 0.2 (GJ kg-1) and the amount of the effective diffusion coefficient of moisture is 2.5×10-8 (m2/s) at a temperature of 45(0C) and 417(W) of microwave power and 1490(W) of infrared power were obtained. Combined microwave-vacuum dryer has the most optimal point in shrinkage of9%, color changes of 8.5the amount of specific energy consumption of 0.13(GJ kg-1) and the amount of effective diffusion coefficient of 2.3×10-8(m2/s) at the air temperature of 45 (0C) and the microwave power of 650 watts, the vacuum pressure of 26.5 kilopascals was achieved.
Conclusions
This study demonstrated that the application of combined drying
technologies significantly improves the drying performance and quality attributes of almond kernels compared to conventional approaches. Among the evaluated methods, combined microwave–infrared, microwave–vacuum, and microwave–continuous drying systems effectively reduced the moisture content from 46% to approximately 6% (dry basis) while influencing shrinkage, color change, energy consumption, and moisture diffusion behavior. The Midilli model provided the most accurate prediction of drying kinetics for all systems, confirming its suitability for modeling almond drying processes. Artificial neural network analysis successfully identified optimal operating conditions for each dryer configuration, highlighting the critical role of air temperature and microwave power in process efficiency. Overall, the combined microwave–infrared dryer showed superior performance in terms of lower specific energy consumption and favorable quality characteristics, while the microwave–vacuum dryer exhibited competitive results under optimized conditions. These findings indicate that hybrid drying technologies, coupled with intelligent modeling and optimization tools, offer a promising and energy-efficient approach for industrial-scale drying of almond kernels and potentially other tree nuts.