Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling

Assessment of a LPG hybrid solar dryer assisted with smart air circulation system for drying basil leaves

The fluctuation of solar radiation throughout the day presents a significant obstacle to the widespread adoption of solar dryers for the dehydration of agricultural products, particularly those that are sensitive to high temperatures, such as basil leaf drying during the winter season. Consequently, this recent study sought to address the limitations of solar-powered dryers by implementing a hybrid drying system that harnesses both solar energy and liquid petroleum gas (LPG). Furthermore, an innovative automatic electronic unit was integrated to facilitate the circulation of air between the drying chamber and the ambient environment. Considering the solar radiation status in Egypt, an LPG hybrid solar dryer has been developed to be suitable for both sunny and cloudy weather conditions. This hybrid solar dryer (HSD) uses indirect forced convection and a controlled auxiliary heating system (LPG) to regulate both temperature and relative humidity, resulting in increased drying rates, reduced energy consumption, and the production of high-quality dried products. The HSD was tested and evaluated for drying basil leaves at three different temperatures of50, 55, and 60 °C and three air changing rates of 70, 80, and 90%, during both summer and winter sessions. The obtained results showed that drying basil at a temperature of 60 °C and an air changing rate of 90% led to a decrease in the drying time by about 35.71% and 35.56% in summer and winter, respectively, where summer drying took 135-210 min and winter drying took 145-225 min to reach equilibrium moisture content (MC). Additionally, the effective moisture diffusivity ranged from 5.25 to 9.06 × 10- 9 m2/s, where higher values of effective moisture diffusivity (EMD) were increased with increasing both drying temperatures and air change rates. Furthermore, the activation energy decreased from 16.557 to 25.182 kJ/mol to 1.945-15.366 kJ/mol for the winter and summer sessions, respectively. On the other hand, the analysis of thin-layer kinetic showed that the Modified Midilli II model has a higher coefficient of determination R2, the lowest χ2, and the lowest root mean square error (RMSE) compared to the other models of both winter and summer sessions. Finally, the LPG hybrid solar dryer can be used for drying a wide range of agricultural products, and it is more efficient for drying medicinal plants. This innovative dryer utilizes a combination of LPG and solar energy, making it efficient and environmentally friendly.

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.

Structural change kinetics, drying characteristics, antioxidant properties, and the correlation between quality parameters of dried duckweed (Wolffia arrhiza (L.) Wimm) affected by different levels of microwave power

BACKGROUND

Duckweed is considered as a future food material due to its fast growth, high yield, high nutritional value, and low impact on land use. However, in its fresh form, it has high moisture content (95% wet basis), resulting in a short shelf life. In this study, microwave drying (MWD) was conducted to produce a shelf-stable duckweed with minimal loss of quality. Drying characteristics and quality aspects of dried duckweed were assessed. Reaction order kinetics, including zero and first order, was applied to describe structural changes during drying process. Hierarchical cluster analysis (HCA) was used to select the appropriate drying conditions.

RESULTS

Of five drying models, the Midilli-Kucuk model was the one that best described the drying process. Drying at high microwave power could reduce energy consumption and increase energy efficiency. Increasing both microwave power and drying time increased the structural shrinkage rate as described by first-order reaction kinetics. High correlations among quality parameters were observed using Pearson's correlation. Drying treatments were differentiated into two main clusters by HCA and the results showed that MWD at 720 and 900 W provided samples that were closer in terms of quality to a freeze dried sample (the positive control) than samples that had been subjected to MWD at 450 W.

CONCLUSION

Drying behaviors of duckweed were well-described by the Midilli-Kucuk model. Microwave drying at 900 W gave the lowest energy consumption and displayed the most efficient use of energy. The first-order equation could be used effectively to describe the structural changes in the duckweed. Microwave drying at 720 and 900 W was the appropriate drying condition according to the HCA classification. © 2023 Society of Chemical Industry.

Effects of ultrasound pretreatment assisted drying methods on drying characteristics, physical and bioactive properties of windfall apples

BACKGROUND

Drying is one of the most important post-harvest processes in order to ensure that the products harvested in certain periods are consumed throughout the year. In this study, Golden Delicious, Oregon Spur and Granny Smith apple cultivars (fallen apples) were dried in open-sun, a controlled glasshouse, microwave oven (200 W), hybrid system (100 W + 60 °C), convective greenhouse (70 °C) and freeze-dryer (-55 °C) with and without ultrasound pretreatments.

RESULTS

Present findings revealed that the longest drying duration was observed in freeze-drying and the shortest in microwave drying. The greatest color change (ΔE) was observed in ultrasound pretreated Oregon Spur apples dried in a controlled glasshouse. The greatest total phenolics (40.08 mg GAE g^-1 ), DPPH (2,2-diphenyl-1-picrylhydrazyl) inhibition activity (46.96%), FRAP (ferric reducing ability of plasma) antioxidant activity (68.01 μg Trolox g^-1 ) and ascorbic acid content (1.52 mg AAE g^-1 ) were observed in open sun-dried Granny Smith apples without ultrasound pretreatment.

CONCLUSIONS

Generally, microwave and hybrid drying yielded better outcomes for bioactive, HMF (5-hydroxymethyl-2-furfural) and drying characteristics of windfall apples. © 2022 Society of Chemical Industry.

The Effect of Nonthermal Pretreatment on the Drying Kinetics and Quality of Black Garlic

Black garlic is obtained from regular garlic (Allium sativum L.) through the aging process and consequently gains many health-promoting properties, including antidiabetic and antioxidant. However, the material is still prone to microbiological deterioration and requires a long time to dry due to its properties. Therefore, this study aimed to investigate the effect of various drying methods on the quality of black garlic as well as determine the influence of selected nonthermal pretreatments on the drying kinetics and quality of black garlic, which is especially important in the case of the materials that are difficult to dry. The Weibull model was chosen to describe drying kinetics. Additionally, color, water activity together with antioxidant activity, phenolic compounds, and antidiabetic potential were determined. This study found that the application of a pulsed electric field (PEF), a constant electric field (CEF) as well as a magnetic field (MF) significantly reduced the time of drying (by 32, 40, and 24 min for a PEF, a CEF, and a MF, respectively, compared to combined drying without the pretreatment), and resulted in high antidiabetic potential. However, the highest content of phenolic compounds (1123.54 and 1125.36 mg/100 g dm for VMD125 and CD3h-VMD, respectively) and antioxidant capacity (ABTS = 6.05 and 5.06 mmol Trolox/100 g dm for VMD500 and CD6h-VMD, respectively) were reported for black garlic treated by vacuum-microwave drying and combined convective pre-drying followed by vacuum-microwave drying. Overall, the nonthermal pretreatment decreased the time of drying and showed very good efficiency in maintaining the antidiabetic potential of black garlic, especially in the case of the materials pretreated by a constant electric field (IC50 = 99 and 56 mg/mL, for α-amylase and α-glucosidase, respectively).

Drying Kinetic of Jaboticaba Berries and Natural Fermentation for Anthocyanin-Rich Fruit Vinegar

This study aimed to determine the drying kinetic of jaboticaba berries that were then used for the fermentation of natural fruit vinegar. The drying behavior was fitted well to the thin-layer kinetic model of Midilli et al. in a vacuum oven at 40 °C. Moisture diffusion was the dominant mechanism because two falling rate periods were observed. The effective moisture diffusivity was decreased (2.52 × 10−10 m2/s) after being pretreated with 70% sugar (1.84 × 10−10 m2/s) and 10% salt (6.73 × 10−11 m2/s) solutions. Fresh berry vinegar was found to have higher flavonoids, including anthocyanins, to exhibit higher antiradical and anti-pathogenic microorganism activities. However, the phenolic content in dried berries vinegar was higher, mainly from the bond breaking of tannins and lignins from fruit peel. Some extent of oxidation occurred because of the change in the color index of vinegar samples. The acidity of both vinegars was 3% acetic acid. Headspace GC-MS also detected acetic acid as the major compound (>60%) in the vapor of vinegar samples. A wide range of non-volatile compounds composed of alkaloids, terpenoids, flavonoids, organic acids, and sugar derivatives was detected by UHPLC-TWIMS-QTOFMS. The peak intensity of anthocyanins was reduced by 28−77% in dried berry vinegar. Therefore, it is better to prepare natural fruit vinegar using fresh berries, preserving anthocyanins for high antioxidant capacity.

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.

Semi-Empirical Mathematical Modeling, Energy and Exergy Analysis, and Textural Characteristics of Convectively Dried Plantain Banana Slices

Thin-layer convective drying of plantain banana was performed at four different temperatures from 50 to 80 °C, with slice thicknesses from 2 to 8 mm. The drying curves, fitted to seven different semi-empirical mathematical models, were successfully used to fit experimental data (R2 0.72−0.99). The diffusion approach had better applicability in envisaging the moisture ratio at any time during the drying process, with the maximum correlation value (R2 0.99) and minimum value of x2 (2.5×10−5 to 1.5×10−4) and RMSE (5.0 ×10−3 to 1.2×10−2). The Deff, hm, and Ea values were calculated on the basis of the experimental data, and overall ranged from 1.11×10−10 to 1.79×10−9 m2 s−1, 3.17×10−8 to 2.20 ×10−7 m s−1 and 13.70 to 18.23 kJ mol−1, respectively. The process energy consumption varied from 23.3 to 121.4 kWh kg−1. The correlation study showed that the drying temperature had a close correlation with hm value and sample hardness. A significant (p < 0.05) increase in hardness of dried plantain banana was observed at 80 °C compared to the other temperatures. Additionally, the sample hardness and process energy consumption were more positively correlated with the thickness of the samples.

Mathematical Description of Changes of Dried Apple Characteristics during Their Rehydration

The mathematical description of changes of dried apples characteristics (mass gain, volume increase, dry matter loss, rehydration indices, and colour) during their rehydration was performed. The effect of conditions of both processes on model parameters were also considered. Apple slices (3 and 10 mm) and cubes (10 mm) were dried in natural convection (drying air velocity 0.01 m/s), forced convection (0.5 and 2 m/s), and fluidisation (6 m/s). Drying air temperatures (Td) were equal to 50, 60, and 70 °C. The rehydration process was carried out in distilled water at the temperatures (Tr) of 20, 45, 70, and 95 °C. Mass gain, volume increase, and dry matter loss were modelled using the following empirical models: Peleg, Pilosof–Boquet–Batholomai, Singh and Kulshrestha, Lewis (Newton), Henderson–Pabis, Page, and modified Page. Colour changes were described through applying the first-order model. Artificial neural networks (feedforward multilayer perceptron) were applied to make the rehydration indices and colour variations (ΔE) dependent on characteristic dimension, Td, drying air velocity, and Tr. The Page and the modified Page models can be considered to be the most appropriate in order to characterise the mass gain (RMSE = 0.0143–0.0619) and the volume increase (RMSE = 0.0142–0.1130), whereas the Peleg, Pilosof–Bouquet–Batholomai, and Singh and Kulshrestha models were found to be the most appropriate to characterise dry matter loss (RMSE = 0.0116–0.0454). The ANNs described rehydration indices and ΔE satisfactorily (RMSE = 0.0567–0.0802). Both considered process conditions influenced (although in different degree) the changes of the considered dried apple characteristics during their rehydration.

Analysis of Material-Characterization Properties of Post-Production Waste-The Case of Apple Pomace

The paper presents the material-characterization properties of apple pomace-the post-production waste of juice pressing. Tests were carried out on the basic physical properties of apple pomace: color, specific-density, and energy properties. Extensive material-composition analyses based on DSC (differential scanning calorimetry) and TGA (thermogravimetry) methods were also performed. It has been shown that pomace, due to its energy value, can be a good fuel. The obtained thermal data confirm the presence of cellulose, hemicelluloses, lignins and pectins in the analyzed pomace. The results confirm that dried apple pomace is microbiologically stable with good health-promoting properties.

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.

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.