Microwave-vacuum drying of sour cherry: comparison of mathematical models and artificial neural networks

Recent application of artificial neural network in microwave drying of foods: a mini-review

The microwave-assisted thermal process is a high-efficiency drying method and is promising to be applied in the food industry. However, the prediction of the thermal treatment results from such a dynamic and complicated process can be difficult. Additionally, the determination of the optimal drying parameters, such as drying temperature, microwave power, and drying time for optimized performance can also be hard. Recently, extensive research has been focusing on the use of artificial neural network (ANN) models in the laboratory-scale microwave drying processes and has shown the feasibility of such application. As a regression tool, the ANN models have been widely used in predicting drying performance; when integrated with additional optimizing algorithms, the ANN models could be used for drying parameter optimization; and when combined with real-time measuring techniques (e.g. nuclear magnetic resonance), the ANN models could be used for monitoring and controlling the drying process in a dynamic sense. Future research could focus on testing the developed ANN models in industrial-scale microwave drying processes and applying the ANN models in microwave drying kinetics research for optimizing the dynamic drying processes. © 2022 Society of Chemical Industry.

Modern Methods of Pre-Treatment of Plant Material for the Extraction of Bioactive Compounds

In this review, recent advances in the methods of pre-treatment of plant material for the extraction of secondary metabolites with high biological activity are presented. The correct preparation of the material for extraction is as important as the selection of the extraction method. This step should prevent the degradation of bioactive compounds as well as the development of fungi and bacteria. Currently, the methods of preparation are expected to modify the particles of the plant material in such a way that will contribute to the release of bioactive compounds loosely bonded to cell wall polymers. This review presents a wide range of methods of preparing plant material, including drying, freeze-drying, convection drying, microwave vacuum drying, enzymatic processes, and fermentation. The influence of the particular methods on the structure of plant material particles, the level of preserved bioactive compounds, and the possibility of their release during the extraction were highlighted. The plant material pre-treatment techniques used were discussed with respect to the amount of compounds released during extraction as well their application in various industries interested in products with a high content of biologically active compounds, such as the pharmaceutical, cosmetics, and food industries.

Effects of Hot-Air Coupled Microwave on Characteristics and Kinetics Drying of Lotus Root Slices

Hot-air coupled microwave was employed to dry lotus root slices. The effects of lotus root slice thickness (5, 8, 11, 14, and 17 mm), hot-air velocity (1.5, 2.0, 2.5, 3.0, and 3.5 m/s), hot-air temperature (50, 55, 60, 65, and 70 °C), and microwave power density (2, 4, 6, 8, and 10 W/g) on drying characteristics and kinetics were studied. Results indicated that the drying process involved both the accelerating and decelerating periods but no constant rate period. The drying rate reached the maximum of 1.52 kg/kg when microwave power density was 8 W/g and reached the minimum of 0.02 kg/kg at the last stage of drying. In addition, the drying kinetics of lotus root slices were also investigated using eleven previously reported models. Among the models, the Verma et al. model was the most suitable for description of the drying behaviors of lotus root slices based on R ², root-mean-square error, and chi-square. The moisture transfer from lotus root slices can be effectively described by Fick's diffusion model. Regardless of drying conditions, the effective diffusivity coefficients ranged from 8.23 × 10^-7 to 7.08 × 10^-6 m²/s, and their variations were mostly in agreement with those of moisture ratios. The activation energy of moisture diffusion related to lotus root slices was determined to be 13.754 kJ/mol.

Optimization of Microwave Coupled Hot Air Drying for Chinese Yam Using Response Surface Methodology

The effect of microwave coupled hot air drying on rehydration ratio (RR) and total sugar content (TSC) of Chinese yam was investigated. Single factor test and response surface methodology were used for process parameter optimization with hot air temperature, hot air velocity, slice thickness, and microwave power density as variables and RR and TSC of dried products as responses. The effect of variables on RR followed the order: slice thickness > hot air temperature > microwave power density > hot air velocity. The effect of variables on TSC followed the order: slice thickness > microwave power density > hot air velocity > hot air temperature. The optimized process parameters were hot air velocity of 2.5 m/s, hot air temperature of 61.7 °C, slice thickness of 8.5 mm, and microwave power density of 5.9 W/g. Under the optimal conditions, the predicted values of RR and TSC were 1.90 g/g and 5.74 g/100 g, respectively, which is very close to corresponding actual values (1.83 g/g and 5.72 g/100 g). The desirability of 0.913 further validated the effectiveness of the model. The findings from this work may apply to other agricultural products.

A neural network based model to analyze rice parboiling process with small dataset

In this study, milling recovery, head rice yield, degree of milling and whiteness were utilized to characterize the milling quality of Tarom parboiled rice variety. The parboiled rice was prepared with three soaking temperatures and steaming times. Then the samples were dried to three levels of final moisture contents [8, 10 and 12% (w.b)]. Modeling of process and validating of the results with small dataset are always challenging. So, the aim of this study was to develop models based on the milling quality data in parboiling process by means of multivariate regression and artificial neural network. In order to validate the neural network model with a little dataset, K-fold cross validation method was applied. The ANN structure with one hidden layer and Tansig transfer function by 18 neurons in the hidden layer was selected as the best model in this study. The results indicated that the neural network could model the parboiling process with higher degree of accuracy. This method was a promising procedure to create accuracy and can be used as a reliable model to select the best parameters for the parboiling process with little experiment dataset.

Mathematical modelling of thin layer hot air drying of apricot with combined heat and power dryer

In this study thermal energy of an engine was used to dry apricot. For this purpose, experiments were conducted on thin layer drying apricot with combined heat and power dryer, in a laboratory dryer. The drying experiments were carried out for four levels of engine output power (25 %, 50 %, 75 % and full load), producing temperatures of 50, 60, 70, and 80 ° C in drying chamber respectively. The air velocity in drying chamber was about 0.5 ± 0.05 m/s. Different mathematical models were evaluated to predict the behavior of apricot drying in a combined heat and power dryer. Conventional statistical equations namely modeling efficiency (EF), Root mean square error (RMSE) and chi-square (χ2) were also used to determine the most suitable model. Assessments indicated that the Logarithmic model considering the values of EF = 0.998746, χ 2 = 0.000120 and RMSE = 0.004772, shows the best treatment of drying apricot with combined heat and power dryer among eleven models were used in this study. The average values of effective diffusivity ranged 1.6260 × 10(-9) to 4.3612 × 10(-9) m2/s for drying apricot at air temperatures between 50 and 80 °C and at the air flow rate of 0.5 ± 0.05 m/s; the values of Deff increased with the increase of drying temperature the effective diffusivities in the second falling rate period were about eight times greater than that in the first falling rate period.

Drying characteristics and quality of shiitake mushroom undergoing microwave-vacuum drying and microwave-vacuum combined with infrared drying

Shiitake mushrooms were dehydrated by two different drying methods, i.e., microwave-vacuum drying (MVD) and microwave-vacuum combined with infrared drying (MVD + IR). MVD was operated at microwave powers of 56, 143, 209 and 267 W under absolute pressures of 18.66, 29.32, 39.99 and 50.65 kPa, whereas infrared radiation was added in MVD + IR at 100 and 200 W. The effects of microwave power, absolute pressure and infrared power on drying characteristics, qualities and specific energy consumption were investigated. It was found that drying rate increased with lower absolute pressure, higher microwave power and higher infrared power. In particular, the results also indicated that drying undergoing MVD + IR could provide better qualities in terms of color of dried shiitake mushroom, rehydration ratio and texture of rehydrated ones. Furthermore, the drying characteristics were described by fitting data to six different drying models. Based on their coefficient of determination, root mean square error, residual of sum square and chi-square, Modified Page model could accurately predict moisture ratio for all drying conditions. Within the range of this study, the suitable drying condition with respect to the product qualities and energy consumption was MVD + IR drying at 267 W of microwave power, 18.66 kPa of absolute pressure and 200 W of infrared power.