# Thermal Diffusivity, Moisture Diffusivity, and Color Change ofCodonopsis javanica with the Support of the Ultrasound for Drying

Authors: Xuan-Quang Nguyen; Anh-Duc Le; Ngoc-Phuong Nguyen; Hay Nguyen
Journal: Journal of Food Quality
 (2019)

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## Abstract

In this study, drying kinetics, including thermal and moisture, ofCodonopsis javanica with the support of ultrasonic technology in the drying process were investigated. Experimental processes were carried out at drying air temperatures of 40°C, 45°C, and 50°C with and without ultrasound at a frequency of 20 kHz and three levels of intensity: 1.3 kW/m2, 1.8 kW/m2, and 2.2 kW/m2. Based on theoretical calculations, experimental data, and the particle swarm optimization (PSO) algorithm, the coefficient of thermal diffusion was determined in the range of 1.01–1.36 × 10−7 m2/s, and the coefficient of moisture diffusion is in the range of 3.2–6.7 × 10−10 m2/s. In addition, the color parameters (L∗, a∗, and b∗) of the drying materials were also considered. Results showed the overall color differences (∆E) of dried products change in the range of 8.0–12.9 compared with the fresh ones. In this work, the multiple boundary conditions were considered to determine the moisture and thermal diffusion coefficients; the results obtained prove that the quality of dried products in terms of color change is also improved.


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## Body

## 1. Introduction

Codonopsis javanica is an agricultural product of high economic value in Vietnam, used for food and medicine. So, its moisture content reduction is necessary for preservation. C. javanica is a heat-sensitive material; therefore, drying air temperature and drying time affect the nutrient composition, herbal medicine, and product color.Ultrasound-assisted drying is a high-energy mechanical wave (its frequencies range between 18 kHz and 100 kHz [1, 2]) and considered a hybrid drying technique to reduce drying time, save energy, and maintain the nutrient composition of the products [3]. The energy of ultrasound reduces the internal and external resistances to moisture transfer in the material. In addition, microstreaming at the surface of the drying material, caused by ultrasound, decreases the diffusion boundary layer and enhances drying kinetics and diffusivity [4]. This affects the drying kinetics of the material whose characteristic quantity is the effective diffusion coefficient (De). With no external resistance factors, the effective diffusion coefficient was calculated from the diffusion equation based on Fick’s second law [4]. Assuming that deformation of objects during drying is negligible, using the initial conditions and the reasonable constraints, Crank [5] determined the solutions of the diffusion equations in different geometrical conditions. Applying Fick’s second law and Crank’s results, the researchers determined the moisture diffusion coefficients of orange peel [6], salted cod [7], honeysuckle [8], and apple slabs [9]. Considering the external resistance factors, the conditions of moisture exchange at the surface of the drying materials and drying air are investigated, and the effective diffusion coefficient (De) and moisture transfer coefficients (hm) at the surface would be determined simultaneously. Previous studies [6, 10–12] accomplished this by combining theoretical calculations, experimental data, and the optimal algorithm with the appropriate objective function, to find parameters of orange, apple, strawberry, and passion fruit peel. Rodríguez et al. suggested a heat and mass transfer model and solved the optimization problem to obtain the thermal and mass transfer coefficients for thyme leaves [13]. The differences of the moisture content of materials between the experimental data and theoretical calculations when considering external resistance factors are less than the differences of those when no external resistance factors apply [6, 12]. It can be seen that in order to reduce the error of the calculated and experimental moisture content in drying processes, with the support of ultrasound, one should consider the external resistance factors. In this paper, therefore, the well-known PSO algorithm is employed to determine the appropriate parameters related to the drying kinetics.When Bantle et al. used ultrasound-assisted drying, the temperature in salt fish was higher than 5°C compared with drying without ultrasound [14, 15]. When drying was supported by ultrasound, the temperature at the surface of apples was higher from 1.0°C to 1.5°C [16]. Evidently, ultrasound-assisted drying affects not only moisture diffusion but also thermal diffusion in drying materials, heat, and moisture transfer between drying materials and drying air.Musielak et al. published a review of research on ultrasound-assisted drying [1] influencing many scholars to perform testing on a variety of agricultural products and foods. These studies focus mostly on moisture diffusivity analysis of drying kinetics. We realized that studying C. javanica has not yet been done, and the study of moisture and thermal diffusion using the multiple parameters considered at the boundary conditions is still limited. In the present study, therefore, moisture and thermal diffusion coefficients were determined, and a comparison of color change of the products with ultrasound was performed.


## 2. Materials and Methods

### 2.1. Materials

Fresh four-year-oldC. javanica samples were cultivated in Lam Dong province, Vietnam. Their average diameters were approximately 20–25 mm. After harvesting, they were stored in refrigerator conditions at 5°C. Before conducting the experiments, they were sliced to a thickness of 5 ± 0.5 mm. The initial temperature was 25–27°C, and the initial moisture content was 6.4–7.7 (kg W/kg DM (dry matter)). The moisture content of the samples was determined by using a moisture analyzer (DBS 60-3).


### 2.2. Drying Experiments and Procedures

The experiment system consisted of a heat pump dryer integrated with an ultrasonic transducer in the drying chamber (Figure1(a)). The drying air was created by the dryer system with a temperature range of 28–50°C, relative humidity between 12–58%, and air velocity from 0.1 to 2.5 m/s. High-energy ultrasound was emitted from a transmitter, which was developed previously [17]; its frequency and intensity were 20 ± 0.73 kHz and in the range of 0–3.0 kW/m2, respectively. The samples were weighed online with respect to time by an electronic weight scale (GX-200) with an accuracy of ±0.001 g. The temperature of the drying air and samples was measured by sensors (Fluke 5622-10-s) with an accuracy of ±0.09°C and 1 mm diameter. The system and material parameters were updated and stored automatically in a computer.Figure 1
The experiment setup of the drying process. (a) The drying system using a heat pump method and ultrasound. (b) The position of the material in the drying chamber and its two points of temperature measurement (A and B). (1) Drying fan; (2) evaporator; (3.1), (3.2) condenser; (4) compressor; (5) auxiliary air heater; (6) temperature and humidity sensors; (7) valve; (8) ultrasonic transducer; (9), (11) trays for samples; (10) drying chamber with ultrasound; (12) drying chamber without ultrasound; (13) high-power ultrasonic generator; (14), (15) electronic weight scale; (16) unit controller; (17) computer.
(a)
(b)


#### 2.2.1. Experimental Procedures to Determine the Ultrasonic Absorption Coefficient ofC. javanica

Material slices with 5 mm thickness and 25 mm diameter were placed on the tray at the distance of 8.5 mm to the emitter. Two temperature sensors were used for each slice (a sensor in the center (point B), the other was close to the surface inside the material (point A), Figure1(b)). To determine the temperature differences in the drying material with and without ultrasound, (i) the material was placed in the drying chamber (Figure 1(a)); the temperature in the chamber was maintained at 40°C and the ultrasound was emitted continuously. (ii) After approximately 60 minutes, when the system reached a steady state, the ultrasound was turned off for 10 minutes; thereafter, the ultrasound was emitted every 5 minutes, and the data were recorded at the beginning and the end of the cycle. The experiments were repeated three times per sample.


#### 2.2.2. Experimental Procedures to Determine Drying Kinetics ofC. javanica

Each batch of drying consisted of 100 g materials. The original colors of fresh materials were measured before putting them on the sample tray. Twelve cases (cases 1–12) were investigated corresponding to 12 different experimental conditions, where temperatures were 40°C, 45°C, and 50°C with an accuracy of ±0.5°C; the air velocity was fixed at 0.5 m/s, with and without the support of ultrasound at three levels of intensity:Iu1 = 1.3 kW/m2, Iu2 = 1.8 kW/m2, and Iu3 = 2.2 kW/m2. The experiment was terminated when the moisture content of the material reached an equilibrium state. Each test case was repeated three times; the final data were averaged of the three repetitions. The weight and temperature at points A and B of the dried samples were updated and stored automatically in a computer at a sampling time of 10 minutes. To minimize the error in each sample, the ultrasonic sound was stopped, and the air was not blown into the drying chamber for 10 seconds.


### 2.3. Modeling

The thickness of the material was low compared with its diameter; thus, it is reasonable to assume that heat and moisture transfer is only considered in the thickness direction (z-axis). The mathematical model used to calculate the drying process was the one-dimensional Fourier’s law of heat, which is described by the following equation:(1)∂t∂τ=αt∂2t∂z2.Moisture transfer in the drying material is also described by the following diffusion equation [6, 7, 11, 12]:(2)∂M∂τ=De∂2M∂z2,where τ: time (s); z, coordinate (m); M: moisture content of materials (kg W/kg DM); t: temperature (°C); at: thermal diffusivity (m2/s); and De: effective moisture diffusivity (m2/s).The initial conditions of temperature and moisture (τ = 0) of the drying material were uniform (equation (3)); heat and moisture transfer at both sides of the material was identical (geometric symmetry) (equation (4)):(3)τ=0;tz,0=t0,Mz,0=M0,(4)z=0,τ≠0;∂t0,τ∂z=0,∂M0,τ∂z=0.Assuming that the heat flux absorbed into the material included the convective heat transfer from the drying air and the difference between the absorption heat by the ultrasound and the evaporation heat. The boundary conditions of the convective heat transfer at the surface of the drying material with the support of ultrasound were determined by the following equation:(5)z=δ,τ>0;−kp∂tδ,τ∂z=htta−tδ,τ+μuIu−Deρshfg∂Mδ,τ∂z.To consider the effect of ultrasound on convective moisture transfer at the surface of the drying material, the boundary conditions (equation (6)) were used to solve the equations of moisture transfer [6, 11, 12]:(6)z=δ,τ>0;−Deρs∂Mδ,τ∂z=hmφeδ,t−φa,where hm: convective moisture transfer coefficient (kg/m2s); hfg: latent heat of vaporization (J/kg); φe: water activity (aw) at the surface of the drying material (0–1); φa: moisture of the drying air (0–1); ht: convective heat transfer coefficient at the surface of the drying material (W/m2 K); ta: temperature of the drying air (°C); ρs and ρp: density of the dry solid and the material (kg/m3); kp: thermal conductivity of the material (W/m K); µu: ultrasonic absorption coefficient; and Iu: ultrasound intensity (kW/m2).


### 2.4. Determination of Moisture Content

The moisture content of the material (dry basis) was determined by the following equation:(7)M=mt−msms,where M: moisture content; mt: weight of the material; and ms: weight of the dry solid.


### 2.5. Determination of Ultrasonic Absorption Coefficient of Material

When the ultrasound propagates a material, part of its energy is absorbed by the material and converted into thermal energy, increasing the temperature of the material [16]. To evaluate the absorbing capability of the material for ultrasonic energy, the parameter μu is presented [16]. In this study, assuming that the energy absorbed by the material from the ultrasound is converted into thermal energy, we applied the law of energy conservation to derive the parameter μu in the following equation:(8)μu=2δρpcpΔtavΔτIu,where ∆tav is the average increased temperature in the period of time ∆τ which is calculated in the following equation:(9)Δtav=ΔtA+ΔtB2,where ∆tA and ∆tB are the temperature differences in the drying material with and without ultrasound at points A and B, respectively.It can be considered that the components ofC. javanica are similar to Korean Ginseng. Hence, heat capacity and density of this material can be determined from Korean Ginseng, which are cp = 2605.492 J/kg K and ρp = 1361.6 kg/m3, respectively [18].


### 2.6. Determination of Equilibrium Moisture Content of Material

At certain conditions of temperature and water activity (aw), after a period of time, the moisture in the material reaches an equilibrium state (Me) (a necessary parameter to calculate the diffusion coefficient). The static gravimetric method using a saturated salt solution was applied to determine the equilibrium moisture content in C. javanica samples. The experiments were carried out at three levels of temperature 30°C, 45°C, and 50°C and 21 levels of water activity from 0.111 to 0.923 created by seven types of salts: lithium chloride, potassium fluoride, magnesium chloride, sodium bromide, potassium chloride, sodium chloride, and potassium nitrate [11, 19]. The mathematical models proposed by Henderson, Chung–Pfost, Halsey [19], and Oswin [18] were adopted to predict the equilibrium moisture content of C. javanica. The nonlinear regression method was used to obtain the parameters of the regression equations. The suitable model was chosen based on some criteria of coefficient of determination (R2), root mean square error (RMSE), and mean relative percentage error (MRE).


### 2.7. Determination of Drying Kinetics

To study thermal and moisture diffusion of the materials, all parameters in equations (1) and (2) must be determined. The parameters with the physical thermal property, kp, ρp, and ρs can be obtained from experiments. However, ht and hm values are difficult to determine for the effects of ultrasound and oscillation of gas molecules around the drying material with the properties being in flux. In addition, when considering the external resistance effect and support of ultrasound, more parameters are needed. In this study, theoretical calculations together with the experimental results and the particle swarm optimization (PSO) algorithm were utilized to determine necessary parameters including thermal diffusivity (αt), effective moisture diffusivity (De), convective heat, and moisture transfer coefficients (ht and hm).Heat and moisture transfer equations (equations (1) and (2)) were solved by the explicit finite difference approximation method with a number of nodes, with respect to thickness direction (N = 15). Equation (10) shows the size of a step distance; equation (11) shows the size of a step time:(10)Δz=2δN−1,(11)Δτ=0.42δ/N−12αt.The average moisture and temperature of the volume at the time (m) were determined by the following equations:(12)Mavm=Δz/2M1m+∑i=2N−3/2ΔzMim+Δz/2MN−1/2mΔz/2N−1,(13)tavm=Δz/2t1m+∑i=2N−3/2Δztim+Δz/2tN−1/2mΔz/2N−1.The search algorithm would determine the values ofDe, αt, hm, and ht in the predefined constraints. Differential equations (1) and (2), which are subject to the initial and boundary conditions (equations (3)–(6)), were solved to determine the moisture and temperature profile in the material. The average moisture in the volume of the drying material (Mav) and the average temperature of the drying material (tav) were determined as well (equations (12) and (13)). The objective function, equation (16), is defined as the differences between theoretical calculations and experimental data of moisture and temperature of the drying material. The algorithm would determine the values of De, αt, hm, and ht and their constraints to minimize the objective function, equation (17). This algorithm is illustrated in Figure 2.Figure 2
Flowchart for determining the values ofDe, αt, hm, and ht.The PSO algorithm was used in this study as the search algorithm, its multi-objective optimization technique gives high-precision results for linear and nonlinear models [20]. Its concept is based on the behavior of looking for foods within a swarm [21]. Each particle has a position in the search space that represents a parameter value and a velocity vector used to update a new position. The particles start with random values in a predefined space and are modified to find the best variation. At each step, all particles are updated with the best two solution values: Pbest, personal best position so far and Gbest, global best position up to now. The position and velocity of each particle are accelerated toward the global best, and its own personal best based on the following equations [21]:(14)vim+1=ωvim+1+c1w1xPbest−xim+c2w2xGbest−xim,(15)xim+1=xim+vim+1.The objective equations (16) and (17) are defined as(16)MREi=αMREM,i+1−αMREt,i,(17)MinMREi,where MRE (mean relative percentage error) is the relative difference between the values of calculations and experimental data. The MREM,i is MRE of moisture content, MREt,i is MRE of temperature, and α is a weight (0-1), in this study, α = 0.5.A calculation program based on the PSO algorithm and other programs were performed by MATLAB 2015.


### 2.8. Color Change of the Drying Products

The product color is the quality evaluation criteria for dried ginseng roots [22], the CIE Lab color parameters (L∗, a∗, and b∗) were adopted to describe color change during our drying process. The values of L∗, a∗, and b∗ were measured by the color measurement machine from X-Rite Inc. Grand Rapids MI (RM200). The data validity was confirmed by taking the average of three repeated measurements. The color change index (∆E) was calculated by using the color parameters of the dried product as described by the following equation [23]:(18)ΔE=L∗−Lref∗2+a∗−aref∗2+b∗−bref∗2,where Lref∗, aref∗, and bref∗ are the standard values and in this study were values of the fresh material (before drying).


### 2.9. Statistical Analysis

To evaluate the fitness of the mathematical model, these following equations (19)–(21) including, R2 (coefficient of determination), RMSE (root mean square error), and MRE (mean relative percentage error) were considered [19]:(19)R2=1−∑i=1Nyexp,i−ypre,i2∑i=1Nyexp,i−yav2,(20)RMSE=1N∑i=1Nyexp,i−ypre,i2,(21)MRE=100∑i=1Nyexp,i−ypre,i/yexp,iN,where yexp,i, ypre,i, yav, and N are the measured data from the experiment, the predicted values, the average experimental values, and the number of experiments, respectively.Experiments were conducted in triplicate. All values were obtained in the average ± standard deviation (n = 3).Regression analysis was performed by statistical software SAS 9.1.


## 2.1. Materials

Fresh four-year-oldC. javanica samples were cultivated in Lam Dong province, Vietnam. Their average diameters were approximately 20–25 mm. After harvesting, they were stored in refrigerator conditions at 5°C. Before conducting the experiments, they were sliced to a thickness of 5 ± 0.5 mm. The initial temperature was 25–27°C, and the initial moisture content was 6.4–7.7 (kg W/kg DM (dry matter)). The moisture content of the samples was determined by using a moisture analyzer (DBS 60-3).


## 2.2. Drying Experiments and Procedures

The experiment system consisted of a heat pump dryer integrated with an ultrasonic transducer in the drying chamber (Figure1(a)). The drying air was created by the dryer system with a temperature range of 28–50°C, relative humidity between 12–58%, and air velocity from 0.1 to 2.5 m/s. High-energy ultrasound was emitted from a transmitter, which was developed previously [17]; its frequency and intensity were 20 ± 0.73 kHz and in the range of 0–3.0 kW/m2, respectively. The samples were weighed online with respect to time by an electronic weight scale (GX-200) with an accuracy of ±0.001 g. The temperature of the drying air and samples was measured by sensors (Fluke 5622-10-s) with an accuracy of ±0.09°C and 1 mm diameter. The system and material parameters were updated and stored automatically in a computer.Figure 1
The experiment setup of the drying process. (a) The drying system using a heat pump method and ultrasound. (b) The position of the material in the drying chamber and its two points of temperature measurement (A and B). (1) Drying fan; (2) evaporator; (3.1), (3.2) condenser; (4) compressor; (5) auxiliary air heater; (6) temperature and humidity sensors; (7) valve; (8) ultrasonic transducer; (9), (11) trays for samples; (10) drying chamber with ultrasound; (12) drying chamber without ultrasound; (13) high-power ultrasonic generator; (14), (15) electronic weight scale; (16) unit controller; (17) computer.
(a)
(b)


### 2.2.1. Experimental Procedures to Determine the Ultrasonic Absorption Coefficient ofC. javanica

Material slices with 5 mm thickness and 25 mm diameter were placed on the tray at the distance of 8.5 mm to the emitter. Two temperature sensors were used for each slice (a sensor in the center (point B), the other was close to the surface inside the material (point A), Figure1(b)). To determine the temperature differences in the drying material with and without ultrasound, (i) the material was placed in the drying chamber (Figure 1(a)); the temperature in the chamber was maintained at 40°C and the ultrasound was emitted continuously. (ii) After approximately 60 minutes, when the system reached a steady state, the ultrasound was turned off for 10 minutes; thereafter, the ultrasound was emitted every 5 minutes, and the data were recorded at the beginning and the end of the cycle. The experiments were repeated three times per sample.


### 2.2.2. Experimental Procedures to Determine Drying Kinetics ofC. javanica

Each batch of drying consisted of 100 g materials. The original colors of fresh materials were measured before putting them on the sample tray. Twelve cases (cases 1–12) were investigated corresponding to 12 different experimental conditions, where temperatures were 40°C, 45°C, and 50°C with an accuracy of ±0.5°C; the air velocity was fixed at 0.5 m/s, with and without the support of ultrasound at three levels of intensity:Iu1 = 1.3 kW/m2, Iu2 = 1.8 kW/m2, and Iu3 = 2.2 kW/m2. The experiment was terminated when the moisture content of the material reached an equilibrium state. Each test case was repeated three times; the final data were averaged of the three repetitions. The weight and temperature at points A and B of the dried samples were updated and stored automatically in a computer at a sampling time of 10 minutes. To minimize the error in each sample, the ultrasonic sound was stopped, and the air was not blown into the drying chamber for 10 seconds.


## 2.2.1. Experimental Procedures to Determine the Ultrasonic Absorption Coefficient ofC. javanica

Material slices with 5 mm thickness and 25 mm diameter were placed on the tray at the distance of 8.5 mm to the emitter. Two temperature sensors were used for each slice (a sensor in the center (point B), the other was close to the surface inside the material (point A), Figure1(b)). To determine the temperature differences in the drying material with and without ultrasound, (i) the material was placed in the drying chamber (Figure 1(a)); the temperature in the chamber was maintained at 40°C and the ultrasound was emitted continuously. (ii) After approximately 60 minutes, when the system reached a steady state, the ultrasound was turned off for 10 minutes; thereafter, the ultrasound was emitted every 5 minutes, and the data were recorded at the beginning and the end of the cycle. The experiments were repeated three times per sample.


## 2.2.2. Experimental Procedures to Determine Drying Kinetics ofC. javanica

Each batch of drying consisted of 100 g materials. The original colors of fresh materials were measured before putting them on the sample tray. Twelve cases (cases 1–12) were investigated corresponding to 12 different experimental conditions, where temperatures were 40°C, 45°C, and 50°C with an accuracy of ±0.5°C; the air velocity was fixed at 0.5 m/s, with and without the support of ultrasound at three levels of intensity:Iu1 = 1.3 kW/m2, Iu2 = 1.8 kW/m2, and Iu3 = 2.2 kW/m2. The experiment was terminated when the moisture content of the material reached an equilibrium state. Each test case was repeated three times; the final data were averaged of the three repetitions. The weight and temperature at points A and B of the dried samples were updated and stored automatically in a computer at a sampling time of 10 minutes. To minimize the error in each sample, the ultrasonic sound was stopped, and the air was not blown into the drying chamber for 10 seconds.


## 2.3. Modeling

The thickness of the material was low compared with its diameter; thus, it is reasonable to assume that heat and moisture transfer is only considered in the thickness direction (z-axis). The mathematical model used to calculate the drying process was the one-dimensional Fourier’s law of heat, which is described by the following equation:(1)∂t∂τ=αt∂2t∂z2.Moisture transfer in the drying material is also described by the following diffusion equation [6, 7, 11, 12]:(2)∂M∂τ=De∂2M∂z2,where τ: time (s); z, coordinate (m); M: moisture content of materials (kg W/kg DM); t: temperature (°C); at: thermal diffusivity (m2/s); and De: effective moisture diffusivity (m2/s).The initial conditions of temperature and moisture (τ = 0) of the drying material were uniform (equation (3)); heat and moisture transfer at both sides of the material was identical (geometric symmetry) (equation (4)):(3)τ=0;tz,0=t0,Mz,0=M0,(4)z=0,τ≠0;∂t0,τ∂z=0,∂M0,τ∂z=0.Assuming that the heat flux absorbed into the material included the convective heat transfer from the drying air and the difference between the absorption heat by the ultrasound and the evaporation heat. The boundary conditions of the convective heat transfer at the surface of the drying material with the support of ultrasound were determined by the following equation:(5)z=δ,τ>0;−kp∂tδ,τ∂z=htta−tδ,τ+μuIu−Deρshfg∂Mδ,τ∂z.To consider the effect of ultrasound on convective moisture transfer at the surface of the drying material, the boundary conditions (equation (6)) were used to solve the equations of moisture transfer [6, 11, 12]:(6)z=δ,τ>0;−Deρs∂Mδ,τ∂z=hmφeδ,t−φa,where hm: convective moisture transfer coefficient (kg/m2s); hfg: latent heat of vaporization (J/kg); φe: water activity (aw) at the surface of the drying material (0–1); φa: moisture of the drying air (0–1); ht: convective heat transfer coefficient at the surface of the drying material (W/m2 K); ta: temperature of the drying air (°C); ρs and ρp: density of the dry solid and the material (kg/m3); kp: thermal conductivity of the material (W/m K); µu: ultrasonic absorption coefficient; and Iu: ultrasound intensity (kW/m2).


## 2.4. Determination of Moisture Content

The moisture content of the material (dry basis) was determined by the following equation:(7)M=mt−msms,where M: moisture content; mt: weight of the material; and ms: weight of the dry solid.


## 2.5. Determination of Ultrasonic Absorption Coefficient of Material

When the ultrasound propagates a material, part of its energy is absorbed by the material and converted into thermal energy, increasing the temperature of the material [16]. To evaluate the absorbing capability of the material for ultrasonic energy, the parameter μu is presented [16]. In this study, assuming that the energy absorbed by the material from the ultrasound is converted into thermal energy, we applied the law of energy conservation to derive the parameter μu in the following equation:(8)μu=2δρpcpΔtavΔτIu,where ∆tav is the average increased temperature in the period of time ∆τ which is calculated in the following equation:(9)Δtav=ΔtA+ΔtB2,where ∆tA and ∆tB are the temperature differences in the drying material with and without ultrasound at points A and B, respectively.It can be considered that the components ofC. javanica are similar to Korean Ginseng. Hence, heat capacity and density of this material can be determined from Korean Ginseng, which are cp = 2605.492 J/kg K and ρp = 1361.6 kg/m3, respectively [18].


## 2.6. Determination of Equilibrium Moisture Content of Material

At certain conditions of temperature and water activity (aw), after a period of time, the moisture in the material reaches an equilibrium state (Me) (a necessary parameter to calculate the diffusion coefficient). The static gravimetric method using a saturated salt solution was applied to determine the equilibrium moisture content in C. javanica samples. The experiments were carried out at three levels of temperature 30°C, 45°C, and 50°C and 21 levels of water activity from 0.111 to 0.923 created by seven types of salts: lithium chloride, potassium fluoride, magnesium chloride, sodium bromide, potassium chloride, sodium chloride, and potassium nitrate [11, 19]. The mathematical models proposed by Henderson, Chung–Pfost, Halsey [19], and Oswin [18] were adopted to predict the equilibrium moisture content of C. javanica. The nonlinear regression method was used to obtain the parameters of the regression equations. The suitable model was chosen based on some criteria of coefficient of determination (R2), root mean square error (RMSE), and mean relative percentage error (MRE).


## 2.7. Determination of Drying Kinetics

To study thermal and moisture diffusion of the materials, all parameters in equations (1) and (2) must be determined. The parameters with the physical thermal property, kp, ρp, and ρs can be obtained from experiments. However, ht and hm values are difficult to determine for the effects of ultrasound and oscillation of gas molecules around the drying material with the properties being in flux. In addition, when considering the external resistance effect and support of ultrasound, more parameters are needed. In this study, theoretical calculations together with the experimental results and the particle swarm optimization (PSO) algorithm were utilized to determine necessary parameters including thermal diffusivity (αt), effective moisture diffusivity (De), convective heat, and moisture transfer coefficients (ht and hm).Heat and moisture transfer equations (equations (1) and (2)) were solved by the explicit finite difference approximation method with a number of nodes, with respect to thickness direction (N = 15). Equation (10) shows the size of a step distance; equation (11) shows the size of a step time:(10)Δz=2δN−1,(11)Δτ=0.42δ/N−12αt.The average moisture and temperature of the volume at the time (m) were determined by the following equations:(12)Mavm=Δz/2M1m+∑i=2N−3/2ΔzMim+Δz/2MN−1/2mΔz/2N−1,(13)tavm=Δz/2t1m+∑i=2N−3/2Δztim+Δz/2tN−1/2mΔz/2N−1.The search algorithm would determine the values ofDe, αt, hm, and ht in the predefined constraints. Differential equations (1) and (2), which are subject to the initial and boundary conditions (equations (3)–(6)), were solved to determine the moisture and temperature profile in the material. The average moisture in the volume of the drying material (Mav) and the average temperature of the drying material (tav) were determined as well (equations (12) and (13)). The objective function, equation (16), is defined as the differences between theoretical calculations and experimental data of moisture and temperature of the drying material. The algorithm would determine the values of De, αt, hm, and ht and their constraints to minimize the objective function, equation (17). This algorithm is illustrated in Figure 2.Figure 2
Flowchart for determining the values ofDe, αt, hm, and ht.The PSO algorithm was used in this study as the search algorithm, its multi-objective optimization technique gives high-precision results for linear and nonlinear models [20]. Its concept is based on the behavior of looking for foods within a swarm [21]. Each particle has a position in the search space that represents a parameter value and a velocity vector used to update a new position. The particles start with random values in a predefined space and are modified to find the best variation. At each step, all particles are updated with the best two solution values: Pbest, personal best position so far and Gbest, global best position up to now. The position and velocity of each particle are accelerated toward the global best, and its own personal best based on the following equations [21]:(14)vim+1=ωvim+1+c1w1xPbest−xim+c2w2xGbest−xim,(15)xim+1=xim+vim+1.The objective equations (16) and (17) are defined as(16)MREi=αMREM,i+1−αMREt,i,(17)MinMREi,where MRE (mean relative percentage error) is the relative difference between the values of calculations and experimental data. The MREM,i is MRE of moisture content, MREt,i is MRE of temperature, and α is a weight (0-1), in this study, α = 0.5.A calculation program based on the PSO algorithm and other programs were performed by MATLAB 2015.


## 2.8. Color Change of the Drying Products

The product color is the quality evaluation criteria for dried ginseng roots [22], the CIE Lab color parameters (L∗, a∗, and b∗) were adopted to describe color change during our drying process. The values of L∗, a∗, and b∗ were measured by the color measurement machine from X-Rite Inc. Grand Rapids MI (RM200). The data validity was confirmed by taking the average of three repeated measurements. The color change index (∆E) was calculated by using the color parameters of the dried product as described by the following equation [23]:(18)ΔE=L∗−Lref∗2+a∗−aref∗2+b∗−bref∗2,where Lref∗, aref∗, and bref∗ are the standard values and in this study were values of the fresh material (before drying).


## 2.9. Statistical Analysis

To evaluate the fitness of the mathematical model, these following equations (19)–(21) including, R2 (coefficient of determination), RMSE (root mean square error), and MRE (mean relative percentage error) were considered [19]:(19)R2=1−∑i=1Nyexp,i−ypre,i2∑i=1Nyexp,i−yav2,(20)RMSE=1N∑i=1Nyexp,i−ypre,i2,(21)MRE=100∑i=1Nyexp,i−ypre,i/yexp,iN,where yexp,i, ypre,i, yav, and N are the measured data from the experiment, the predicted values, the average experimental values, and the number of experiments, respectively.Experiments were conducted in triplicate. All values were obtained in the average ± standard deviation (n = 3).Regression analysis was performed by statistical software SAS 9.1.


## 3. Results and Discussion

### 3.1. The Ultrasonic Absorption Coefficient ofC. javanica

Figure3 shows the average temperature of C. javanica with and without ultrasound. The temperature difference at the point A (∆tA), point B (∆tB), and the average temperature difference of the material (∆tav) with and without ultrasound are the average values of three repetitions. The standard errors in Figure 3 are relatively small, indicating good repeatability of experiments. The experimental results are illustrated in Table 1.Figure 3
The average temperature (average ± SD,n = 3) of the drying material at the conditions: ta = 40°C; va = 0.5 m/s; φa = 22–23%; ultrasonic intensity Iu = 1.8 kW/m2.Table 1
The difference temperatures (average ± SD,n = 3) with and without ultrasound for 5 minutes.
Cases
Ultrasound intensity (kW/m2)
Difference temperature (°C)
∆tA
∆tB
∆tav
1
1.3
0.8 ± 0.014
0.4 ± 0.014
0.6 ± 0.030
2
1.8
1.4 ± 0.066
0.9 ± 0.008
1.2 ± 0.022
3
2.2
1.9 ± 0.113
1.1 ± 0.084
1.5 ± 0.080Figure3 shows that the temperature inside the drying material when using ultrasound was higher than the temperature without ultrasound. The increased temperatures at the alternate positions of the drying material are different. The increased temperature (∆t) in the presence of ultrasound, calculated by equation (9), is the average value measured at points A and B. Here, the ultrasound intensity is in the range of 1.3–2.2 kW/m2, and the values of ∆t are higher without ultrasound, which are from 0.6 to 1.5°C. Linear regression analysis was performed by using the software SAS 9.1 with the data from Table 1, giving an ultrasonic absorption coefficient (μu) of C. javanica of 119.2 (R2 = 0.98).


### 3.2. The Equilibrium Moisture Content ofC. javanica

From the experimental data at three levels of temperature 30°C, 40°C, and 50°C, the nonlinear regression analysis determined the relationship between the equilibrium moisture content ofC. javanica, the temperature, and water activity. Among the four chosen mathematical models, the modified Chung–Pfost model showed the worst indices: the least coefficient of determination (R2 = 0.95), the largest values of RMSE (=0.13), and MRE (=14.71%). In contrast, the Oswin model gave the best values of R2 = 0.99, RMSE = 0.001, and MRE = 1.22%. The experimental data and predictions from the Oswin model (equation (22)) are shown in Figure 4:(22)Me=0.120438−0.0005taw1−aw0.4+0.001958t.Figure 4
Experimental (exp) (average ± SD,n = 3) and predicted (pre) moisture content of C. javanica at 30°C, 40°C, and 50°C.Equation (22) was chosen to calculate the water activity of C. javanica and was applied at the boundary conditions (equation (6)) when solving the equations (1) and (2) of heat and moisture transfer.


### 3.3. Drying Kinetics ofC. javanica

#### 3.3.1. Experimental Drying Data

Experiments were carried out at three levels of temperature, with and without ultrasound at three different values of intensity. The weight and temperature at points A and B are updated and stored on a computer with respect to drying time. The moisture content ofC. javanica was calculated using equation (7). The average temperature at a certain time is the average temperature measured at points A and B at that time. The drying curve (moisture of the drying material) and temperature inside the sample of 12 different experiments along with the standard error bars are illustrated in Figures 5–7 and show that the experiment had good repeatability as indicated by the low values of the standard error.Figure 5
Drying kinetics at drying air parameters: 40°C, 0.5 m/s, and 20–23% air relative humidity. (a) Drying curves; (b) increasing temperatures (average ± SD,n = 3).
(a)
(b)Figure 6
Drying kinetics at drying air parameters: 45°C, 0.5 m/s, and 18–20% air relative humidity. (a) Drying curves; (b) increasing temperatures (average ± SD,n = 3).
(a)
(b)Figure 7
Drying kinetics at drying air parameters: 50°C, 0.5 m/s, and 15–17% air relative humidity. (a) Drying curves; (b) increasing temperatures (average ± SD,n = 3).
(a)
(b)To prove the effectiveness of the ultrasound during the drying process, the drying time to reduce the moisture (∆τ) of C. javanica from 7.0 to 0.44 (kg W/kg DM) and the increased temperature for the first 30 minutes (∆t), with and without ultrasound are considered. The experimental data are shown in Table 2.Table 2
The drying time ofCodonopsis javanica with and without the ultrasound.
Cases
Ultrasound intensity,Iu (kW/m2)
Air temperature,ta (°C)
Drying time,τ (s)
The reduced drying time, ∆τ (%)
1
0
40
23900
—
2
1.3
19000
20
3
1.7
16700
30
4
2.2
14000
41
5
0
45
21900
—
6
1.3
16500
25
7
1.8
13800
37
8
2.2
12100
45
9
0
50
16700
—
10
1.3
12900
23
11
1.8
12600
24
12
2.2
12000
28The experimental data show that with the support of ultrasound, the drying time decreases, especially at the higher ultrasound intensity. When air temperature was 45°C, and the highest intensity was used (2.2 kW/m2), the drying time reduces up to 45% compared with no ultrasound. However, higher air temperature is less effective with ultrasound on the drying time, such as at 50°C, the drying time is reduced to 28% at the highest intensity compared with without ultrasound use.In addition, based on the experimental data, we concluded that when using ultrasound, the temperature in the drying material increased faster. Considering the air temperature at 40°C, in the first 30 minutes, the temperature in the drying material increased by 0.7°C without ultrasound and by 1.4°C, 1.6°C, and 2.8°C with ultrasound at intensities of 1.3 kW/m2, 1.8 kW/m2, and 2.2 kW/m2, respectively. Moreover, part of the energy was absorbed and converted into thermal energy, making the temperature in the drying material greater than the temperature of the air, 0.6–2.1°C.


#### 3.3.2. Parameters of Drying Kinetics

Based on the theoretical calculations, the experimental data, and the PSO algorithm, the four parameters (De, at, hm, and ht) related to the drying kinetics of C. javanica were obtained. Figure 8 shows a typical result of the average moisture content and the average temperature of the drying material at the air temperature of 45°C and intensity of 1.8 kW/m2. The results of 12 cases including moisture and thermal diffusivity, convective heat, and moisture transfer coefficient at the surface of the material and the value of the objective function (MRE index) are shown in Table 3.Figure 8
Calculation and experiment drying curves and temperature curves ofCodonopsis javanica at 45°C air temperature and 1.8 kW/m2 intensity: (a) change of moisture content; (b) temperature inside the sample.
(a)
(b)Table 3
Parameters (De, at, hm, and ht) of Codonopsis javanica at different conditions.
Cases
I
u (kW/m2)
t
a (°C)
D
e × 10−10 (m2/s)
α
t × 10−7 (m2/s)
h
m × 10−3 (kg/m2s)
h
t (W/m2K)
MRE (%)
1
0
40
3.2
1.01
2.3
29.2
3.3
2
1.3
4
1.13
2.8
39.8
3.1
3
1.8
4.6
1.27
3.2
48.8
3.4
4
2.2
5.6
1.32
3.8
60.1
6.8
5
0
45
3.9
1.16
2.9
30.7
3.0
6
1.3
4.9
1.30
3.2
40.7
2.7
7
1.8
5.7
1.35
3.6
51.2
2.9
8
2.2
6.4
1.37
4.0
62.4
4.4
9
0
50
4.9
1.27
3.4
33.2
2.7
10
1.3
5.9
1.35
3.6
41.6
2.2
11
1.8
6.4
1.37
3.9
53.6
5.2
12
2.2
6.7
1.38
4.1
63.6
2.6Table3 shows that the average differences (MRE) between the calculations of equations (12) and (13) and the experiments are from 2.2% to 6.8%. Therefore, the parameters of the drying kinetics can be accepted as stated in the literature [19] (the MRE value must be less than 10%).(1) The Effects of the Ultrasound on the Effective Moisture Diffusivity and Convective Moisture Transfer at the Surface of the Material. Table 3 shows that De and hm values depend on the air temperature and the intensity of ultrasound; higher values of temperature and intensity results in higher values of De and hm. In the air temperature range 40–50°C and the ultrasound intensity of 0 to 2.2 kW/m2 (0–100 W), the De and hm values are from 3.2 × 10−10 to 6.7 × 10−10 m2/s and 2.3 × 10−3 to 4.1 × 10−3 kg/m2s, respectively. The values of De, in this study, are in the range of the effective moisture diffusivity of food, between 10−11 and 10−9 m2/s as stated in [11]. Also, similar to previously stated orange peel [6] values, De is between 0.88 × 10−9 and 1.72 × 10−9 m2/s, and hm is between 1.17 × 10−3 and 2.43 × 10−3 kg/m2s at an air temperature and moisture of 40°C and 26.5%, respectively, the power of the ultrasound was in the range 0–90 W. Other like examples include, values of De between 0.14 × 10−10 and 0.74 × 10−10 m2/s for salted codfish [7] and between 0.763 × 10−10 and 2.293 × 10−10 m2/s for strawberries [11]. The values of hm for strawberries are also between 1.380 × 10−5 and 3.387 × 10−5 kg/m2s at the air temperature of 40–70°C and with an ultrasound power between 0 and 60 W [11].In this study, with use of ultrasound, the increase inDe is inversely proportional to the air temperature (25–75% at 40°C, 26–64% at 45°C, and 18–28% at 50°C). This is similar to hm (22–56% at 40°C, 12–37% at 45°C, and 11–26% at 50°C). Therefore, we concluded that the ultrasound has a profound effect on the effective moisture diffusivity in the drying material and convective moisture transfer at surface of the material at lower air temperature. These effects decrease as the air temperature increases.A nonlinear model was proposed by Rodríguez et al. [4] as in equations (23) and (24) for effective moisture diffusivity and external mass transfer coefficients, respectively:(23)De=d1e−d2/273.15+ta−d3P−d4P/273.15+ta2+d5/273.15+ta,(24)hm=h1−h2273.15+ta−h3P−h4P273.15+ta2+h5273.15+ta.In this study, a nonlinear model was developed based on equations (23) and (24) and nonlinear regression analysis in the form of a second-order function. The relationships between the identified parameters (De, hm, the air temperature, and the ultrasound intensity) are shown in Figure 9 and in the following equations:(25)De=3.05×10−4e−4286.96/273.15+ta+2.202967Iu−212275Iu/273.15+ta2+24418.8Iu2/273.15+ta−3710120Iu2/273.15+ta2−40.04Iu2,where R2 = 0.99, RMSE = 0.010, and MRE = 0.96%.(26)hm=0.010032−0.41659ta−0.0031Iu−5.3856Iuta2+0.256727Iuta+0.515849Iu2ta2+4.285627ta2,where R2 = 0.99, RMSE = 0.008, and MRE = 0.63%.Figure 9
Influence of temperature on effective moisture diffusivity and convective moisture transfer coefficient at different ultrasound intensities: (a) the moisture diffusion coefficients and (b) the external moisture transfer coefficients ofCodonopsis javanica.
(a)
(b)(2) The Effects of the Ultrasound on the Thermal Diffusivity (αt) and Convective Heat Transfer Coefficients (ht) at the Surface of the Drying Material. The ultrasound propagating through the air affects the temperature kinetics of the drying material via the parameters αt and ht. According to the experimental results shown in Table 3, the values of αt and ht are in the range of 1.01 × 10−7 and 1.38 × 10−7 m2/s and 29.2 and 63.6 W/m2 K, respectively. In another research with other drying materials, the ht of salted codfish is between 21.0 and 27.5 W/m2 K [14]; αt and ht of potato are 1.31 × 10−7 m2/s and 25–250 W/m2 K, respectively [24].Based on Table3 data, the relationships between αt, ht, the air temperature, and ultrasound intensity are illustrated in Figure 10 and in the following equations:(27)αt=1.014×10−7+6.553×10−6ta−6.53×10−8Iu−1.3×10−4Iuta2+6.242×10−6Iuta+1.7×10−5Iu2ta2−2.6×10−4ta2−7.16×10−9Iu2,where R2 = 0.98, RMSE = 0.013, and MRE = 0.97%.(28)ht=56.64+2.20ta−34.54Iu+4taIu2−5.63taIu;withU=13.03+0.4ta;with NU,where, with ultrasound-assisted (with U), R2 = 0.99, RMSE = 0.010, and MRE = 0.83%. Without ultrasound-assisted (with NU) Iu = 0 kW/m2, R2 = 0.97, RMSE = 0.020, and MRE = 0.91%.Figure 10
Influence of temperature on the thermal diffusivity and convective heat transfer coefficients: (a) the thermal diffusion coefficients and (b) the heat transfer coefficients ofCodonopsis javanica.
(a)
(b)


### 3.4. Color Change ofC. javanica

The color parameters of the products at the different drying conditions were measured and shown in Table4.Table 4
Color of the driedCodonopsis javanica (average ± SD, n = 3).
Cases
I
u (kW/m2)
t
a (°C)
L
∗
a
∗
b
∗
ΔE
Material
71.8 ± 2.26
2 ± 0.05
31.6 ± 1.31
1
0
40
79.2 ± 1.08
2.7 ± 0.13
21.4 ± 0.69
12.6 ± 0.42
2
1.3
78.6 ± 1.07
3.3 ± 0.06
23.9 ± 0.57
10.4 ± 0.20
3
1.8
78.5 ± 1.42
3.4 ± 0.04
25.5 ± 0.39
9.2 ± 0.11
4
2.2
76.8 ± 1.12
4.2 ± 0.11
22.5 ± 0.36
10.6 ± 0.32
5
0
45
75.1 ± 1.46
5.8 ± 0.10
21.6 ± 0.50
11.2 ± 0.23
6
1.3
78.4 ± 1.31
4.5 ± 0.10
26.2 ± 0.43
8.8 ± 0.18
7
1.8
78.2 ± 6.66
5.4 ± 0.19
27.3 ± 2.45
8.4 ± 0.82
8
2.2
72.6 ± 1.93
5.9 ± 0.19
23.7 ± 1.31
8.8 ± 0.56
9
0
50
59.6 ± 1.74
6.1 ± 0.18
30.8 ± 0.74
12.9 ± 0.19
10
1.3
61.6 ± 0.97
6.1 ± 0.13
30.8 ± 0.75
11.0 ± 0.18
11
1.8
61.6 ± 1.13
6.8 ± 0.62
33.1 ± 0.76
11.4 ± 0.24
12
2.2
60.1 ± 2.2
7.1 ± 0.30
32.8 ± 0.96
12.8 ± 0.51Table4 shows that when air temperature increases, the value of L∗ (lightness) decreases, but the values of a∗ (redness/greenness) and b∗ (yellowness/blueness) increase proportionally along with the ultrasound intensity. That is similar to the results reported by Wei et al. for American Ginseng [22]. The value of ∆E changes of C. javanica is between 11.2 and 12.9 without ultrasound and between 8.4 and 12.8 with ultrasound at the intensity of 1.3–2.2 kW/m2. We concluded that the color of the dried samples changes slightly compared with the fresh samples when using the ultrasound-assisted drying process. This can be explained as using ultrasound makes drying times shorter, causing less influence on the color of the product.


## 3.1. The Ultrasonic Absorption Coefficient ofC. javanica

Figure3 shows the average temperature of C. javanica with and without ultrasound. The temperature difference at the point A (∆tA), point B (∆tB), and the average temperature difference of the material (∆tav) with and without ultrasound are the average values of three repetitions. The standard errors in Figure 3 are relatively small, indicating good repeatability of experiments. The experimental results are illustrated in Table 1.Figure 3
The average temperature (average ± SD,n = 3) of the drying material at the conditions: ta = 40°C; va = 0.5 m/s; φa = 22–23%; ultrasonic intensity Iu = 1.8 kW/m2.Table 1
The difference temperatures (average ± SD,n = 3) with and without ultrasound for 5 minutes.
Cases
Ultrasound intensity (kW/m2)
Difference temperature (°C)
∆tA
∆tB
∆tav
1
1.3
0.8 ± 0.014
0.4 ± 0.014
0.6 ± 0.030
2
1.8
1.4 ± 0.066
0.9 ± 0.008
1.2 ± 0.022
3
2.2
1.9 ± 0.113
1.1 ± 0.084
1.5 ± 0.080Figure3 shows that the temperature inside the drying material when using ultrasound was higher than the temperature without ultrasound. The increased temperatures at the alternate positions of the drying material are different. The increased temperature (∆t) in the presence of ultrasound, calculated by equation (9), is the average value measured at points A and B. Here, the ultrasound intensity is in the range of 1.3–2.2 kW/m2, and the values of ∆t are higher without ultrasound, which are from 0.6 to 1.5°C. Linear regression analysis was performed by using the software SAS 9.1 with the data from Table 1, giving an ultrasonic absorption coefficient (μu) of C. javanica of 119.2 (R2 = 0.98).


## 3.2. The Equilibrium Moisture Content ofC. javanica

From the experimental data at three levels of temperature 30°C, 40°C, and 50°C, the nonlinear regression analysis determined the relationship between the equilibrium moisture content ofC. javanica, the temperature, and water activity. Among the four chosen mathematical models, the modified Chung–Pfost model showed the worst indices: the least coefficient of determination (R2 = 0.95), the largest values of RMSE (=0.13), and MRE (=14.71%). In contrast, the Oswin model gave the best values of R2 = 0.99, RMSE = 0.001, and MRE = 1.22%. The experimental data and predictions from the Oswin model (equation (22)) are shown in Figure 4:(22)Me=0.120438−0.0005taw1−aw0.4+0.001958t.Figure 4
Experimental (exp) (average ± SD,n = 3) and predicted (pre) moisture content of C. javanica at 30°C, 40°C, and 50°C.Equation (22) was chosen to calculate the water activity of C. javanica and was applied at the boundary conditions (equation (6)) when solving the equations (1) and (2) of heat and moisture transfer.


## 3.3. Drying Kinetics ofC. javanica

### 3.3.1. Experimental Drying Data

Experiments were carried out at three levels of temperature, with and without ultrasound at three different values of intensity. The weight and temperature at points A and B are updated and stored on a computer with respect to drying time. The moisture content ofC. javanica was calculated using equation (7). The average temperature at a certain time is the average temperature measured at points A and B at that time. The drying curve (moisture of the drying material) and temperature inside the sample of 12 different experiments along with the standard error bars are illustrated in Figures 5–7 and show that the experiment had good repeatability as indicated by the low values of the standard error.Figure 5
Drying kinetics at drying air parameters: 40°C, 0.5 m/s, and 20–23% air relative humidity. (a) Drying curves; (b) increasing temperatures (average ± SD,n = 3).
(a)
(b)Figure 6
Drying kinetics at drying air parameters: 45°C, 0.5 m/s, and 18–20% air relative humidity. (a) Drying curves; (b) increasing temperatures (average ± SD,n = 3).
(a)
(b)Figure 7
Drying kinetics at drying air parameters: 50°C, 0.5 m/s, and 15–17% air relative humidity. (a) Drying curves; (b) increasing temperatures (average ± SD,n = 3).
(a)
(b)To prove the effectiveness of the ultrasound during the drying process, the drying time to reduce the moisture (∆τ) of C. javanica from 7.0 to 0.44 (kg W/kg DM) and the increased temperature for the first 30 minutes (∆t), with and without ultrasound are considered. The experimental data are shown in Table 2.Table 2
The drying time ofCodonopsis javanica with and without the ultrasound.
Cases
Ultrasound intensity,Iu (kW/m2)
Air temperature,ta (°C)
Drying time,τ (s)
The reduced drying time, ∆τ (%)
1
0
40
23900
—
2
1.3
19000
20
3
1.7
16700
30
4
2.2
14000
41
5
0
45
21900
—
6
1.3
16500
25
7
1.8
13800
37
8
2.2
12100
45
9
0
50
16700
—
10
1.3
12900
23
11
1.8
12600
24
12
2.2
12000
28The experimental data show that with the support of ultrasound, the drying time decreases, especially at the higher ultrasound intensity. When air temperature was 45°C, and the highest intensity was used (2.2 kW/m2), the drying time reduces up to 45% compared with no ultrasound. However, higher air temperature is less effective with ultrasound on the drying time, such as at 50°C, the drying time is reduced to 28% at the highest intensity compared with without ultrasound use.In addition, based on the experimental data, we concluded that when using ultrasound, the temperature in the drying material increased faster. Considering the air temperature at 40°C, in the first 30 minutes, the temperature in the drying material increased by 0.7°C without ultrasound and by 1.4°C, 1.6°C, and 2.8°C with ultrasound at intensities of 1.3 kW/m2, 1.8 kW/m2, and 2.2 kW/m2, respectively. Moreover, part of the energy was absorbed and converted into thermal energy, making the temperature in the drying material greater than the temperature of the air, 0.6–2.1°C.


### 3.3.2. Parameters of Drying Kinetics

Based on the theoretical calculations, the experimental data, and the PSO algorithm, the four parameters (De, at, hm, and ht) related to the drying kinetics of C. javanica were obtained. Figure 8 shows a typical result of the average moisture content and the average temperature of the drying material at the air temperature of 45°C and intensity of 1.8 kW/m2. The results of 12 cases including moisture and thermal diffusivity, convective heat, and moisture transfer coefficient at the surface of the material and the value of the objective function (MRE index) are shown in Table 3.Figure 8
Calculation and experiment drying curves and temperature curves ofCodonopsis javanica at 45°C air temperature and 1.8 kW/m2 intensity: (a) change of moisture content; (b) temperature inside the sample.
(a)
(b)Table 3
Parameters (De, at, hm, and ht) of Codonopsis javanica at different conditions.
Cases
I
u (kW/m2)
t
a (°C)
D
e × 10−10 (m2/s)
α
t × 10−7 (m2/s)
h
m × 10−3 (kg/m2s)
h
t (W/m2K)
MRE (%)
1
0
40
3.2
1.01
2.3
29.2
3.3
2
1.3
4
1.13
2.8
39.8
3.1
3
1.8
4.6
1.27
3.2
48.8
3.4
4
2.2
5.6
1.32
3.8
60.1
6.8
5
0
45
3.9
1.16
2.9
30.7
3.0
6
1.3
4.9
1.30
3.2
40.7
2.7
7
1.8
5.7
1.35
3.6
51.2
2.9
8
2.2
6.4
1.37
4.0
62.4
4.4
9
0
50
4.9
1.27
3.4
33.2
2.7
10
1.3
5.9
1.35
3.6
41.6
2.2
11
1.8
6.4
1.37
3.9
53.6
5.2
12
2.2
6.7
1.38
4.1
63.6
2.6Table3 shows that the average differences (MRE) between the calculations of equations (12) and (13) and the experiments are from 2.2% to 6.8%. Therefore, the parameters of the drying kinetics can be accepted as stated in the literature [19] (the MRE value must be less than 10%).(1) The Effects of the Ultrasound on the Effective Moisture Diffusivity and Convective Moisture Transfer at the Surface of the Material. Table 3 shows that De and hm values depend on the air temperature and the intensity of ultrasound; higher values of temperature and intensity results in higher values of De and hm. In the air temperature range 40–50°C and the ultrasound intensity of 0 to 2.2 kW/m2 (0–100 W), the De and hm values are from 3.2 × 10−10 to 6.7 × 10−10 m2/s and 2.3 × 10−3 to 4.1 × 10−3 kg/m2s, respectively. The values of De, in this study, are in the range of the effective moisture diffusivity of food, between 10−11 and 10−9 m2/s as stated in [11]. Also, similar to previously stated orange peel [6] values, De is between 0.88 × 10−9 and 1.72 × 10−9 m2/s, and hm is between 1.17 × 10−3 and 2.43 × 10−3 kg/m2s at an air temperature and moisture of 40°C and 26.5%, respectively, the power of the ultrasound was in the range 0–90 W. Other like examples include, values of De between 0.14 × 10−10 and 0.74 × 10−10 m2/s for salted codfish [7] and between 0.763 × 10−10 and 2.293 × 10−10 m2/s for strawberries [11]. The values of hm for strawberries are also between 1.380 × 10−5 and 3.387 × 10−5 kg/m2s at the air temperature of 40–70°C and with an ultrasound power between 0 and 60 W [11].In this study, with use of ultrasound, the increase inDe is inversely proportional to the air temperature (25–75% at 40°C, 26–64% at 45°C, and 18–28% at 50°C). This is similar to hm (22–56% at 40°C, 12–37% at 45°C, and 11–26% at 50°C). Therefore, we concluded that the ultrasound has a profound effect on the effective moisture diffusivity in the drying material and convective moisture transfer at surface of the material at lower air temperature. These effects decrease as the air temperature increases.A nonlinear model was proposed by Rodríguez et al. [4] as in equations (23) and (24) for effective moisture diffusivity and external mass transfer coefficients, respectively:(23)De=d1e−d2/273.15+ta−d3P−d4P/273.15+ta2+d5/273.15+ta,(24)hm=h1−h2273.15+ta−h3P−h4P273.15+ta2+h5273.15+ta.In this study, a nonlinear model was developed based on equations (23) and (24) and nonlinear regression analysis in the form of a second-order function. The relationships between the identified parameters (De, hm, the air temperature, and the ultrasound intensity) are shown in Figure 9 and in the following equations:(25)De=3.05×10−4e−4286.96/273.15+ta+2.202967Iu−212275Iu/273.15+ta2+24418.8Iu2/273.15+ta−3710120Iu2/273.15+ta2−40.04Iu2,where R2 = 0.99, RMSE = 0.010, and MRE = 0.96%.(26)hm=0.010032−0.41659ta−0.0031Iu−5.3856Iuta2+0.256727Iuta+0.515849Iu2ta2+4.285627ta2,where R2 = 0.99, RMSE = 0.008, and MRE = 0.63%.Figure 9
Influence of temperature on effective moisture diffusivity and convective moisture transfer coefficient at different ultrasound intensities: (a) the moisture diffusion coefficients and (b) the external moisture transfer coefficients ofCodonopsis javanica.
(a)
(b)(2) The Effects of the Ultrasound on the Thermal Diffusivity (αt) and Convective Heat Transfer Coefficients (ht) at the Surface of the Drying Material. The ultrasound propagating through the air affects the temperature kinetics of the drying material via the parameters αt and ht. According to the experimental results shown in Table 3, the values of αt and ht are in the range of 1.01 × 10−7 and 1.38 × 10−7 m2/s and 29.2 and 63.6 W/m2 K, respectively. In another research with other drying materials, the ht of salted codfish is between 21.0 and 27.5 W/m2 K [14]; αt and ht of potato are 1.31 × 10−7 m2/s and 25–250 W/m2 K, respectively [24].Based on Table3 data, the relationships between αt, ht, the air temperature, and ultrasound intensity are illustrated in Figure 10 and in the following equations:(27)αt=1.014×10−7+6.553×10−6ta−6.53×10−8Iu−1.3×10−4Iuta2+6.242×10−6Iuta+1.7×10−5Iu2ta2−2.6×10−4ta2−7.16×10−9Iu2,where R2 = 0.98, RMSE = 0.013, and MRE = 0.97%.(28)ht=56.64+2.20ta−34.54Iu+4taIu2−5.63taIu;withU=13.03+0.4ta;with NU,where, with ultrasound-assisted (with U), R2 = 0.99, RMSE = 0.010, and MRE = 0.83%. Without ultrasound-assisted (with NU) Iu = 0 kW/m2, R2 = 0.97, RMSE = 0.020, and MRE = 0.91%.Figure 10
Influence of temperature on the thermal diffusivity and convective heat transfer coefficients: (a) the thermal diffusion coefficients and (b) the heat transfer coefficients ofCodonopsis javanica.
(a)
(b)


## 3.3.1. Experimental Drying Data

Experiments were carried out at three levels of temperature, with and without ultrasound at three different values of intensity. The weight and temperature at points A and B are updated and stored on a computer with respect to drying time. The moisture content ofC. javanica was calculated using equation (7). The average temperature at a certain time is the average temperature measured at points A and B at that time. The drying curve (moisture of the drying material) and temperature inside the sample of 12 different experiments along with the standard error bars are illustrated in Figures 5–7 and show that the experiment had good repeatability as indicated by the low values of the standard error.Figure 5
Drying kinetics at drying air parameters: 40°C, 0.5 m/s, and 20–23% air relative humidity. (a) Drying curves; (b) increasing temperatures (average ± SD,n = 3).
(a)
(b)Figure 6
Drying kinetics at drying air parameters: 45°C, 0.5 m/s, and 18–20% air relative humidity. (a) Drying curves; (b) increasing temperatures (average ± SD,n = 3).
(a)
(b)Figure 7
Drying kinetics at drying air parameters: 50°C, 0.5 m/s, and 15–17% air relative humidity. (a) Drying curves; (b) increasing temperatures (average ± SD,n = 3).
(a)
(b)To prove the effectiveness of the ultrasound during the drying process, the drying time to reduce the moisture (∆τ) of C. javanica from 7.0 to 0.44 (kg W/kg DM) and the increased temperature for the first 30 minutes (∆t), with and without ultrasound are considered. The experimental data are shown in Table 2.Table 2
The drying time ofCodonopsis javanica with and without the ultrasound.
Cases
Ultrasound intensity,Iu (kW/m2)
Air temperature,ta (°C)
Drying time,τ (s)
The reduced drying time, ∆τ (%)
1
0
40
23900
—
2
1.3
19000
20
3
1.7
16700
30
4
2.2
14000
41
5
0
45
21900
—
6
1.3
16500
25
7
1.8
13800
37
8
2.2
12100
45
9
0
50
16700
—
10
1.3
12900
23
11
1.8
12600
24
12
2.2
12000
28The experimental data show that with the support of ultrasound, the drying time decreases, especially at the higher ultrasound intensity. When air temperature was 45°C, and the highest intensity was used (2.2 kW/m2), the drying time reduces up to 45% compared with no ultrasound. However, higher air temperature is less effective with ultrasound on the drying time, such as at 50°C, the drying time is reduced to 28% at the highest intensity compared with without ultrasound use.In addition, based on the experimental data, we concluded that when using ultrasound, the temperature in the drying material increased faster. Considering the air temperature at 40°C, in the first 30 minutes, the temperature in the drying material increased by 0.7°C without ultrasound and by 1.4°C, 1.6°C, and 2.8°C with ultrasound at intensities of 1.3 kW/m2, 1.8 kW/m2, and 2.2 kW/m2, respectively. Moreover, part of the energy was absorbed and converted into thermal energy, making the temperature in the drying material greater than the temperature of the air, 0.6–2.1°C.


## 3.3.2. Parameters of Drying Kinetics

Based on the theoretical calculations, the experimental data, and the PSO algorithm, the four parameters (De, at, hm, and ht) related to the drying kinetics of C. javanica were obtained. Figure 8 shows a typical result of the average moisture content and the average temperature of the drying material at the air temperature of 45°C and intensity of 1.8 kW/m2. The results of 12 cases including moisture and thermal diffusivity, convective heat, and moisture transfer coefficient at the surface of the material and the value of the objective function (MRE index) are shown in Table 3.Figure 8
Calculation and experiment drying curves and temperature curves ofCodonopsis javanica at 45°C air temperature and 1.8 kW/m2 intensity: (a) change of moisture content; (b) temperature inside the sample.
(a)
(b)Table 3
Parameters (De, at, hm, and ht) of Codonopsis javanica at different conditions.
Cases
I
u (kW/m2)
t
a (°C)
D
e × 10−10 (m2/s)
α
t × 10−7 (m2/s)
h
m × 10−3 (kg/m2s)
h
t (W/m2K)
MRE (%)
1
0
40
3.2
1.01
2.3
29.2
3.3
2
1.3
4
1.13
2.8
39.8
3.1
3
1.8
4.6
1.27
3.2
48.8
3.4
4
2.2
5.6
1.32
3.8
60.1
6.8
5
0
45
3.9
1.16
2.9
30.7
3.0
6
1.3
4.9
1.30
3.2
40.7
2.7
7
1.8
5.7
1.35
3.6
51.2
2.9
8
2.2
6.4
1.37
4.0
62.4
4.4
9
0
50
4.9
1.27
3.4
33.2
2.7
10
1.3
5.9
1.35
3.6
41.6
2.2
11
1.8
6.4
1.37
3.9
53.6
5.2
12
2.2
6.7
1.38
4.1
63.6
2.6Table3 shows that the average differences (MRE) between the calculations of equations (12) and (13) and the experiments are from 2.2% to 6.8%. Therefore, the parameters of the drying kinetics can be accepted as stated in the literature [19] (the MRE value must be less than 10%).(1) The Effects of the Ultrasound on the Effective Moisture Diffusivity and Convective Moisture Transfer at the Surface of the Material. Table 3 shows that De and hm values depend on the air temperature and the intensity of ultrasound; higher values of temperature and intensity results in higher values of De and hm. In the air temperature range 40–50°C and the ultrasound intensity of 0 to 2.2 kW/m2 (0–100 W), the De and hm values are from 3.2 × 10−10 to 6.7 × 10−10 m2/s and 2.3 × 10−3 to 4.1 × 10−3 kg/m2s, respectively. The values of De, in this study, are in the range of the effective moisture diffusivity of food, between 10−11 and 10−9 m2/s as stated in [11]. Also, similar to previously stated orange peel [6] values, De is between 0.88 × 10−9 and 1.72 × 10−9 m2/s, and hm is between 1.17 × 10−3 and 2.43 × 10−3 kg/m2s at an air temperature and moisture of 40°C and 26.5%, respectively, the power of the ultrasound was in the range 0–90 W. Other like examples include, values of De between 0.14 × 10−10 and 0.74 × 10−10 m2/s for salted codfish [7] and between 0.763 × 10−10 and 2.293 × 10−10 m2/s for strawberries [11]. The values of hm for strawberries are also between 1.380 × 10−5 and 3.387 × 10−5 kg/m2s at the air temperature of 40–70°C and with an ultrasound power between 0 and 60 W [11].In this study, with use of ultrasound, the increase inDe is inversely proportional to the air temperature (25–75% at 40°C, 26–64% at 45°C, and 18–28% at 50°C). This is similar to hm (22–56% at 40°C, 12–37% at 45°C, and 11–26% at 50°C). Therefore, we concluded that the ultrasound has a profound effect on the effective moisture diffusivity in the drying material and convective moisture transfer at surface of the material at lower air temperature. These effects decrease as the air temperature increases.A nonlinear model was proposed by Rodríguez et al. [4] as in equations (23) and (24) for effective moisture diffusivity and external mass transfer coefficients, respectively:(23)De=d1e−d2/273.15+ta−d3P−d4P/273.15+ta2+d5/273.15+ta,(24)hm=h1−h2273.15+ta−h3P−h4P273.15+ta2+h5273.15+ta.In this study, a nonlinear model was developed based on equations (23) and (24) and nonlinear regression analysis in the form of a second-order function. The relationships between the identified parameters (De, hm, the air temperature, and the ultrasound intensity) are shown in Figure 9 and in the following equations:(25)De=3.05×10−4e−4286.96/273.15+ta+2.202967Iu−212275Iu/273.15+ta2+24418.8Iu2/273.15+ta−3710120Iu2/273.15+ta2−40.04Iu2,where R2 = 0.99, RMSE = 0.010, and MRE = 0.96%.(26)hm=0.010032−0.41659ta−0.0031Iu−5.3856Iuta2+0.256727Iuta+0.515849Iu2ta2+4.285627ta2,where R2 = 0.99, RMSE = 0.008, and MRE = 0.63%.Figure 9
Influence of temperature on effective moisture diffusivity and convective moisture transfer coefficient at different ultrasound intensities: (a) the moisture diffusion coefficients and (b) the external moisture transfer coefficients ofCodonopsis javanica.
(a)
(b)(2) The Effects of the Ultrasound on the Thermal Diffusivity (αt) and Convective Heat Transfer Coefficients (ht) at the Surface of the Drying Material. The ultrasound propagating through the air affects the temperature kinetics of the drying material via the parameters αt and ht. According to the experimental results shown in Table 3, the values of αt and ht are in the range of 1.01 × 10−7 and 1.38 × 10−7 m2/s and 29.2 and 63.6 W/m2 K, respectively. In another research with other drying materials, the ht of salted codfish is between 21.0 and 27.5 W/m2 K [14]; αt and ht of potato are 1.31 × 10−7 m2/s and 25–250 W/m2 K, respectively [24].Based on Table3 data, the relationships between αt, ht, the air temperature, and ultrasound intensity are illustrated in Figure 10 and in the following equations:(27)αt=1.014×10−7+6.553×10−6ta−6.53×10−8Iu−1.3×10−4Iuta2+6.242×10−6Iuta+1.7×10−5Iu2ta2−2.6×10−4ta2−7.16×10−9Iu2,where R2 = 0.98, RMSE = 0.013, and MRE = 0.97%.(28)ht=56.64+2.20ta−34.54Iu+4taIu2−5.63taIu;withU=13.03+0.4ta;with NU,where, with ultrasound-assisted (with U), R2 = 0.99, RMSE = 0.010, and MRE = 0.83%. Without ultrasound-assisted (with NU) Iu = 0 kW/m2, R2 = 0.97, RMSE = 0.020, and MRE = 0.91%.Figure 10
Influence of temperature on the thermal diffusivity and convective heat transfer coefficients: (a) the thermal diffusion coefficients and (b) the heat transfer coefficients ofCodonopsis javanica.
(a)
(b)


## 3.4. Color Change ofC. javanica

The color parameters of the products at the different drying conditions were measured and shown in Table4.Table 4
Color of the driedCodonopsis javanica (average ± SD, n = 3).
Cases
I
u (kW/m2)
t
a (°C)
L
∗
a
∗
b
∗
ΔE
Material
71.8 ± 2.26
2 ± 0.05
31.6 ± 1.31
1
0
40
79.2 ± 1.08
2.7 ± 0.13
21.4 ± 0.69
12.6 ± 0.42
2
1.3
78.6 ± 1.07
3.3 ± 0.06
23.9 ± 0.57
10.4 ± 0.20
3
1.8
78.5 ± 1.42
3.4 ± 0.04
25.5 ± 0.39
9.2 ± 0.11
4
2.2
76.8 ± 1.12
4.2 ± 0.11
22.5 ± 0.36
10.6 ± 0.32
5
0
45
75.1 ± 1.46
5.8 ± 0.10
21.6 ± 0.50
11.2 ± 0.23
6
1.3
78.4 ± 1.31
4.5 ± 0.10
26.2 ± 0.43
8.8 ± 0.18
7
1.8
78.2 ± 6.66
5.4 ± 0.19
27.3 ± 2.45
8.4 ± 0.82
8
2.2
72.6 ± 1.93
5.9 ± 0.19
23.7 ± 1.31
8.8 ± 0.56
9
0
50
59.6 ± 1.74
6.1 ± 0.18
30.8 ± 0.74
12.9 ± 0.19
10
1.3
61.6 ± 0.97
6.1 ± 0.13
30.8 ± 0.75
11.0 ± 0.18
11
1.8
61.6 ± 1.13
6.8 ± 0.62
33.1 ± 0.76
11.4 ± 0.24
12
2.2
60.1 ± 2.2
7.1 ± 0.30
32.8 ± 0.96
12.8 ± 0.51Table4 shows that when air temperature increases, the value of L∗ (lightness) decreases, but the values of a∗ (redness/greenness) and b∗ (yellowness/blueness) increase proportionally along with the ultrasound intensity. That is similar to the results reported by Wei et al. for American Ginseng [22]. The value of ∆E changes of C. javanica is between 11.2 and 12.9 without ultrasound and between 8.4 and 12.8 with ultrasound at the intensity of 1.3–2.2 kW/m2. We concluded that the color of the dried samples changes slightly compared with the fresh samples when using the ultrasound-assisted drying process. This can be explained as using ultrasound makes drying times shorter, causing less influence on the color of the product.


## 4. Conclusions

The thermal diffusion and moisture diffusion coefficients ofC. javanica at air temperature 40–50°C, with and without ultrasound at the intensity of 1.3–2.2 kW/m2 were investigated in this study. The results show that the values of thermal and moisture diffusivity are in the range of 1.01–1.38 × 10−7 m2/s and 3.2–6.7 × 10−10 m2/s, respectively. The convective heat and moisture transfer coefficients at the surface of the drying material were between 2.3 × 10−3–4.1 × 10−3 kg/m2s and 29.2–63.6 W/m2 K, respectively. In this study, the overall color differences considered the effectiveness of ultrasound as a way to preserve the quality of the product.



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*Source: 2623404-2019-11-03.xml*