Thermal Engineering Division, CSIR-Central Mechanical Engineering Research Institute, India
Received date: May 30, 2012; Accepted date: June 28, 2012; Published date: June 30, 2012
Citation: Loha C, Das R, Choudhury B, Chatterjee PK (2012) Evaluation of Air Drying Characteristics of Sliced Ginger (Zingiber officinale) in a Forced Convective Cabinet Dryer and Thermal Conductivity Measurement. J Food Process Technol 3:160. doi:10.4172/2157-7110.1000160
Copyright: © 2012 Loha C, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Ginger; Drying; Forced convection; Mathematical model; Thermal conductivity
Ginger is the rhizome of the plant Zingiber officinale. It is a tropical spice and cultivated in India, China, Japan, Indonesia, Australia, Nigeria and West Indies islands. India is the largest producer and consumer of ginger in the world. Ginger is one of the major cash crops in Mizoram, Arunachal Pradesh and some other North-East (NE) states of India. It is generally harvested in the month of October- November. The quality of ginger produced in NE India is very good in terms of its aroma content and pungency and it is also purely organic. But till now there is no proper post harvest processing of such good quality ginger. Open sun drying is a general practice. Availability of sun being very much uncertain, sun drying is not effective in NE India. Besides, there are other problems in sun drying like due to the slowness of the process and the exposure to the environment, the product gets contaminated from dust, insects etc. The weather being moist round the year, spoilage takes place very soon. Therefore, more rapid, safe and controllable drying methods are required. The forced convection hot air drying is an effective method to produce a uniform, hygienic and attractive colored product rapidly. Therefore, a forced convective cabinet dryer has been developed to address such problem [1].
The drying behavior of different materials are studied by many authors and several mathematical models have been proposed such as for green chilli [2], garlic slices [3], apricot [4,5], plum [6], eggplant [7] grape [8], carrot [9], sweet cherry [10], sweet potato [11,12], mulberry [13,14], hazelnuts [15], red pepper [16,17], apple [18,19], litchi [20] , pumpkin [21,22], figs [23], gundelia tournefortii [24] etc. However, a unanimously accepted correlation for drying process has not been proposed till date because of the variation of drying material and their biological differences, the way in which heat is supplied to the wet material in different types of dryer. All these factors make mathematical modeling of drying more complicated. Even different correlations have been proposed for the same material. To the best of present authors’ knowledge, no significant work on the drying kinetics of ginger and its mathematical modeling has been reported in the literature.
Therefore, the objectives of the present work are
I. To study the moisture removal rate of sliced ginger at different drying air temperatures by keeping the air velocity constant in a forced convective cabinet dryer.
II. To fit the experimentally obtained moisture ratio with ten selected theoretical models from the literature. To compare the models with respect to the statistical parameters and to evaluate the best suitable model.
III. To determine the thermal conductivity of ginger experimentally at different moisture contents of the sliced ginger and correlate the thermal conductivity as a function of moisture content.
Ginger
Freshly harvested gingers are properly washed in fresh running water and then they are cut into slices of 1.5 - 2 mm thickness by a slicing machine. The initial moisture content of the sliced ginger is determined by using infrared type moisture meter (Testing Instrument Pvt. Ltd., India).
Drying equipment
The dryer used for the present experiment is a batch type cabinet dryer, designed and manufactured in “Thermal Engineering Division” of CSIR-Central Mechanical Engineering Research Institute, Durgapur, India. It consists of a heating chamber, a drying chamber, an air filter, an exhaust fan, a Proportional Integral Derivative (PID) controller and an electronic balance as shown schematically in Figure 1. The drying chamber is made of stainless steel sheet. The chamber is of double walled construction with insulation between inner and outer wall. The door is provided with a gasket to prevent the heat loss. The chamber is housed with six stainless steel trays, which are arranged in such a fashion that one side of each tray is kept open in alternate direction (Figure 1). Air heated by electric coil follows a zigzag path inside the drying chamber to achieve faster drying by increasing the convective heat transfer rate. Temperatures are measured at different places by T-type thermocouple, controlled by a PID controller. The air velocity is measured with a thermal anemometer (Make-Dantec Dynamica, Denmark). The existing dryer is slightly modified for the present experiment as shown in Figure 1. Top four trays are removed and a perforated sample tray is placed in between two trays with the help of holding wire. The wire is attached with the electronic balance placed at the top of the drying chamber.
Drying experiment
Before starting the experiment the dryer is preheated for half an hour with set temperature and velocity. The sliced ginger of 100 g weight is spread uniformly in a thin layer on to the sample tray. The temperature and the velocity of the hot air are measured just before the sample tray. The weight loss is recorded from the electronic balance (Testing Instrument Pvt. Ltd., India) at 30 min interval. The balance is having weighing capacity of 5000 g and readability 0.01 g. The experiments are carried out for four different temperatures of 45, 50, 55 and 60°C by keeping the air velocity fixed at 1.3 m/s. The drying process is stopped when the moisture content of the sample reached 6-7 % (w.b). The drying experiments are replicated five times for each temperature and the average values are taken. The quality of the dried product is ensured based on the criteria of color through visualization, taste and smell.
The thermal conductivity of ginger is determined by using QUICKLINE–30TM (Anter Corporation, Pittsburg, USA) thermal property analyzer with a needle probe. The principle of measurement is based on the analysis of temperature response of the analyzed material to heat flow impulses. The heat flow is produced by electrical heating of a resistor heater inserted into the probe which is in direct thermal contact with the tested specimen. Evaluation of thermal conductivity and volume heat capacity is based on periodically sampled temperature recorder as function of time, provided that the heat propagation occurs in infinite medium. The thermal conductivity is measured at room temperature for the samples with different moisture contents. The measurements are replicated for ten samples to get the average thermal conductivity value at particular moisture content.
Mathematical modeling of drying curves
Different mathematical models are proposed in the literature to study the drying curves; out of them ten such mathematical models are selected for the present study and shown in Table 1. Here a, b, c and n are the constants; k, k_{1} and k_{2} are the drying rate constants and t is the drying time. MR represents the moisture ratio and is calculated according to the equation given below:-
Model No. | Model Name | Model Equation | References |
---|---|---|---|
1 | Lewis | MR = exp(- kt) | [36,37] |
2 | Page | MR = exp (- kt^{n}) | [38,39] |
3 | Modified Page | MR = exp[ - (kt)^{n}] | [40] |
4 | Henderson and Pabis | MR = a exp(- kt) | [41,42] |
5 | Logarithmic | MR = a exp(- kt) + b | [43,8] |
6 | Two term | MR = a exp(- k1t) + b exp(- k2t) | [41,29] |
7 | Two term exponential | MR = a exp(- kt) + (1-a) exp(- kat) | [44,29] |
8 | Approximation of diffusion | MR = a exp(- kt) + (1-a) exp(- kbt) | [45,29] |
9 | Wang & Singh | MR = a + bt + ct2 | [46] |
10 | Midilli et al. | MR = a exp(-kt^{n}) + bt | [32] |
Table 1: Selected drying models applied for drying curves of sliced ginger.
(1)
Where M is the moisture content at any time, M0 is the initial moisture content and Me is the equilibrium moisture content. For the present study MR is simplified to M / M_{0} instead of (M –Me) / (M_{0} – M_{e}) because Me is relatively small compared to M and M_{0} [7,11,14,25-27]. Hence, the error involved in the simplification can be neglected. The drying curves experimentally obtained for four different temperatures are fitted with different MR models. The coefficient of determination (R^{2}) is selected as one of the primary criteria to select the best equation to account for variation in the drying curves of dried samples [28]. In addition to R^{2}, various statistical parameters such as mean relative percentage error (P) and root mean square error (RMSE) are used to determine the quality of the fit [5,14,28]. Higher values of R^{2} and lower the values of P and RMSE are chosen as the criteria for goodness of fit. These parameters can be calculated as follows:
(2)
(3)
(4)
where MR_{exp,i}, MR_{cal,i} stands for the experimental and the calculated dimensionless moisture content respectively, for the i^{th} measurement. N is the number of measurements.
Effect of drying air temperature on the drying curves
The variation of moisture ratio (MR) is plotted against the drying time for four different drying air temperatures of 45, 50, 55 and 60°C as shown in Figure 2. It is evident from the figure that the MR decreases continuously with drying time and it decreases exponentially. With increase in drying air temperature the drying time decreases considerably. The drying time required to reach from an initial moisture content of around 88-87% (w.b.) to a final moisture content of around 6-7% (w.b.) are 8.5, 7.5, 6 and 4.5 h at drying air temperatures of 45, 50, 55 and 60°C respectively. Similar observations are also reported in the literature for the drying of apple slices [29], eggplant [7], onions [30], garlic [31], and lettuce and cauliflower leaves [32].
Drying rate is also computed by numerical differentiation of drying data (MR vs. Time) and plotted against the moisture ratio at different drying air temperature as shown in Figure 3. It is observed that the drying of ginger slices take place in a falling rate period, however, a constant rate period is observed only at the initial stage for 45°C. The drying rate is faster at the beginning and it decreased continuously with the removal of moisture and it becomes more prominent as the temperature increases. Initially the drying rates are significantly different at different temperatures (high at elevated temperature) and they merge together before reaching to the final moisture content. Similar results are also observed for the drying of pumpkin slices [22], mango slices [33], onions [30], garlic [31] etc.
Fitting of drying curves with mathematical models
The values of coefficients and statistical parameters obtained for four different temperatures are summarized in Table 2-5. For all the models, the R^{2} values varied from 0.9833 to 0.9996, indicating a good fit. Model developed by Midilli et al. [34] gives the highest C value and lowest RMSE value for all the temperatures. R^{2} values and RMSE values for Midilli et al. [34] model vary from 0.9993 to 0.9996 and 0.007072 to 0.009637, respectively. The P-value is also lowest for Midilli et al. [34] model at 45, 50 and 55°C. The P-value (6.645%) calculated at 60°C for Midilli et al. [34] is slightly higher (<1%) than the Henderson and Pabis [35] model, which gives the lowest P-value (5.725%) at 60°C. Therefore, it can be said that the Midilli et al. [34] model is the best suited model for representing the drying kinetic of sliced ginger. Usub et al. [36] have also reported in their paper that the Midilli et al. [34] model represents the best drying kinetics of silkworm pupae. Figure 4 indicates the comparison between experimental moisture ratio and those predicted from the model of Midilli et al. [34] at different temperatures. It is found from the figure that good agreement exists between predicted and experimental moisture ratio values for all the temperatures, which is round about 45° straight line.
Model No. | Model constants and Coefficients | R2 | RSME | P (%) |
---|---|---|---|---|
1 | k = 0.2774 | 0.9862 | 0.03586 | 13.502 |
2 | k = 0.2028, n = 1.23 | 0.9992 | 0.008914 | 7.655 |
3 | k = 0.2731, n = 1.23 | 0.9992 | 0.008914 | 7.661 |
4 | a = 1.061, k = 0.2949 | 0.9914 | 0.02912 | 10.434 |
5 | a = 1.139, k = 0.2378, b = -0.1028 | 0.9957 | 0.02124 | 11.369 |
6 | a = -6.951, b = 8.01 k1 = 0.2407, k2 = 0.2473 | 0.9927 | 0.02877 | 9.164 |
7 | a = 1.792, k = 0.3919 | 0.9994 | 0.00766 | 7.057 |
8 | a = 7.616, b = 0.9073, k = 0.1503 | 0.9949 | 0.02332 | 12.004 |
9 | a = 1.017, b = -0.2262, c = 0.01368 | 0.9989 | 0.01062 | 5.301 |
10 | a = 0.9916, b = 0.003353 k = 0.1915, n = 1.303 | 0.9996 | 0.007072 | 5.256 |
Table 2: Results of statistical analysis at T = 45°C.
Model No. | Model constants and Coefficients | R2 | RSME | P (%) |
---|---|---|---|---|
1 | k = 0.3424 | 0.9904 | 0.03016 | 8.830 |
2 | k = 0.2751, n = 1.185 | 0.9988 | 0.01117 | 8.334 |
3 | k = 0.3364, n = 1.185 | 0.9988 | 0.01117 | 8.328 |
4 | a = 1.046, k = 0.3586 | 0.9933 | 0.02594 | 7.657 |
5 | a = 1.094, k = 0.3095, b = -0.06459 | 0.9958 | 0.02143 | 11.085 |
6 | a = 11.38, b = -10.34 k1 = 0.3013, k2 = 0.2961 | 0.9940 | 0.02593 | 7.865 |
7 | a = 1.734, k = 0.4657 | 0.9990 | 0.01018 | 8.000 |
8 | a = 41.17, b = 0.9879, k = 0.2104 | 0.9954 | 0.0224 | 10.731 |
9 | a = 1.004, b = -0.2705, c = 0.01972 | 0.9993 | 0.008829 | 5.960 |
10 | a = 0.989, b = 0.004242 k = 0.2598, n = 1.271 | 0.9993 | 0.008801 | 5.856 |
Table 3: Results of statistical analysis at T = 50°C.
Model No. | Model constants and Coefficients | R2 | RSME | P (%) |
---|---|---|---|---|
1 | k = 0.4584 | 0.9905 | 0.03083 | 10.663 |
2 | k = 0.3885, n = 1.172 | 0.9973 | 0.01715 | 16.2995 |
3 | k = 0.4465, n = 1.172 | 0.9973 | 0.01715 | 16.350 |
4 | a = 1.042, k = 0.4739 | 0.9929 | 0.02775 | 10.911 |
5 | a = 1.066, b = -0.03317, k = 0.4375 | 0.9938 | 0.02726 | 13.876 |
6 | a = 1.049, b = 2.246e-12, k1 = 0.4845, k2 = -3.838 | 0.9931 | 0.03035 | 10.337 |
7 | a = 1.729, k = 0.6162 | 0.9975 | 0.01642 | 16.770 |
8 | a = 7.38, b = 0.9386, k = 0.3021 | 0.9833 | 0.02832 | 14.542 |
9 | a = 1.005, b = -0.3594, c = 0.03466 | 0.9988 | 0.01175 | 13.351 |
10 | a = 0.9924, b = 0.009741 k = 0.3755, n = 1.307 | 0.9994 | 0.008739 | 10.294 |
Table 4: Results of statistical analysis at T = 55°C.
Model No. | Model constants and Coefficients | R2 | RSME | P (%) |
---|---|---|---|---|
1 | k = 0.5533 | 0.9904 | 0.3189 | 6.813 |
2 | k = 0.484, n = 1.191 | 0.9984 | 0.01386 | 9.758 |
3 | k = 0.5437, n = 1.191 | 0.9984 | 0.01386 | 9.749 |
4 | a = 1.038, k = 0.5742 | 0.9926 | 0.0297 | 5.725 |
5 | a = 1.089, b = -0.06519, k = 0.4973 | 0.9949 | 0.02647 | 8.762 |
6 | a = -9.51, b =10.54 k1 = 0.3653, k2 = 0.3813 | 0.9954 | 0.02719 | 7.634 |
7 | a = 1.013, k = 0.5534 | 0.9904 | 0.03383 | 6.712 |
8 | a = 3.716, b = 0.8473, k = 0.03544 | 0.9947 | 0.02677 | 8.607 |
9 | a = 1.008, b =-0.4434, c = 0.05314 | 0.9991 | 0.01092 | 8.517 |
10 | a = 0.9946, b = 0.009395 k = 0.4849, n = 1.293 | 0.9994 | 0.009637 | 6.645 |
Table 5: Results of statistical analysis at T = 60°C.
Effect of moisture content on thermal conductivity
The experimental thermal conductivity variation with moisture content (% w.b.) is shown in Figure 5. The thermal conductivity reduces with the reduction of moisture content. It varies from 0.571 to 0.358 W/m.K with the reduction of moisture content from 80% to 40% (w.b.) at the room temperature of 24°C. Ali et al. [37] have also studied the thermal conductivity of ginger by varying both the temperature and moisture content and they got the variation of thermal conductivity from 0.552 to 0.477 W/m°C for a change in moisture content from 91% to 62% (w.b.) and temperature from 6 to 30°C. They also reported that the moisture content is having the most significant effect on the thermal conductivity whereas the effect of temperature is negligible.
Since the moisture content is having the most significant effect on the thermal conductivity, a correlation has been developed to calculate the thermal conductivity of sliced ginger with moisture content in the following form
(5)
The correlation predicts the value within 1.5% accuracy for a moisture range of 80% to 40% (w.b.) and a temperature 24°C.
Thin layer drying experiments are conducted under controlled conditions of drying air temperature and velocity. The drying time required to reach from initial moisture content of around 88-87% (w.b.) to final moisture content of around 6-7% (w.b.) are 8.5, 7.5, 6 and 4.5 h at drying air temperature of 45, 50, 55 and 60°C respectively. Therefore, it is evident that the drying time decreased with increase in drying air temperature. From the plot of the drying rate, it is observed that in general the drying of sliced ginger occurs at falling rate period, though, an initial constant rate period is found at 45°C. This indicates that the moisture removal from the sample is controlled by the mechanism of diffusion. The experimental moisture removal rate is fitted with ten different mathematical models available in the literature and the model coefficients are obtained. The models are compared by calculating R^{2}, RMSE and P values. The results show that the model developed by Midilli et al. [34] is the best suited model to represent the drying kinetics of sliced ginger. The thermal conductivity of the material is also determined experimentally and the variation with moisture content is shown. The thermal conductivity decreased with the reduction of moisture content and a cubic polynomial expression has been established for this relationship.