- Email: [email protected]
Suitability of thin layer models for infrared–hot air-drying of onion slices
ARTICLE IN PRESS
LWT 39 (2006) 700–705 www.elsevier.com/locate/lwt
Suitability of thin layer models for infrared–hot air-drying of onion slices D.G. Praveen Kumar, H. Umesh Hebbar, M.N. Ramesh Department of Food Engineering, Central Food Technological Research Institute, Mysore—570 020, India Received 9 February 2004; accepted 17 March 2005
Abstract Onion slices were dried under different processing conditions applying infrared radiation assisted by hot air. Drying temperature, slice thickness, inlet air temperature and air velocity were varied to study the drying behavior. Thin layer models such as Page, modified Page, Fick’s and Exponential models, which are used to describe the drying kinetics of food materials, were tested for the combination mode drying. The linear plots for Page and modified Page models gave a better fit (R2 ¼ 0:98020:995) over the other two models (R2 ¼ 0:76720:933). A combined regression equation developed to predict the drying parameters (k and n) for all the four models gave a fairly good fit (R2 ¼ 0:85220:989). The modified Page model gave better predictions for drying characteristics over the other models. r 2005 Swiss Society of Food Science and Technology. Published by Elsevier Ltd. All rights reserved. Keywords: Combined drying; Hot air; Infrared; Onion; Thin layer
1. Introduction Onion ranks third highest in world vegetable production with an annual production of 47 million tonnes. India is the second largest producer of onion with 4.9 million tonnes production per annum (Anonymous, 2001). Onion finds widespread usage in both fresh and dried forms. Dried onions are a product of considerable importance in world trade and are made in several forms: flaked, minced, chopped and powdered. It is used as flavor additives in wide variety of food formulations such as comminuted meats, sauces, soups, salad dressings, pickle and pickle relishes. Dried onion export by India reached nearly 7000 tonnes during the year 2001–2002 (Anonymous, 2002). Drying of foods is mainly aimed at reducing the water activity to extend the shelf life. The major challenge during drying of food materials is to reduce the moisture content of the material to the desired level without Corresponding author. Tel.: +0821 2513910; fax: +0821 2517233.
E-mail address: [email protected] (H.U. Hebbar).
substantial loss of flavor, taste, color and nutrients. Dried onion is valued for its pungency and is normally measured in terms of pyruvic acid content (Singh & Kumar, 1984). Product color is the other quality parameter that needs to be maintained during onion drying. So, the drying conditions are to be optimized to retain maximum product quality, besides considering the process economics. Different varieties of onions with high pungency are grown to suit the drying requirements. Hot air-drying is the most commonly employed commercial technique for drying of biological products (Mazza & Lemaguer, 1980). The processing temperature mainly influences the quality changes during drying. The major limitation of hot air-drying is that it takes longer for drying, resulting in product quality degradation. New and innovative techniques that increase the drying rate and enhance product quality have achieved considerable attention in the recent past. Drying by infrared radiation is one among them, gaining popularity because of its inherent advantages over conventional heating (Mongpraneet, Abe, & Tsurusaki, 2002). The
0023-6438/$30.00 r 2005 Swiss Society of Food Science and Technology. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.lwt.2005.03.021
ARTICLE IN PRESS D.G. Praveen Kumar et al. / LWT 39 (2006) 700–705
ability of infrared radiation to penetrate and heat the inner layers results in higher rate of heat and mass transfer (Ginzburg, 1969). Application of hot air assisted infrared drying for high moisture content materials is reported to be beneficial as it provides a synergistic effect (Ginzburg, 1969). 1.1. Mathematical model Numerous empirical and semi-empirical models have been proposed to describe the drying behavior of agricultural products (Li, Cao, & Liu, 1997; Sinicio & Muir, 1996). Mazza and Lemaguer (1980) have developed theoretical models for convective drying of onion. Rapusas and Driscoll (1995), and Kiranoudis, Maroulis, and Kouris (1992) proposed empirical relations for onions in the form of Arrhenius type and power models respectively. Diffusion-type models were proposed by several researchers to model the drying behavior of various agricultural products (Henderson & Pabis, 1961; Chu & Hustrulid, 1968; Whitaker & Young, 1972; Steffe & Singh, 1980; Walton, White, & Ross, 1988). Luikov (1966) proposed a mathematical model to describe the drying behavior of products based on the internal moisture transport mechanism. The two-term model developed by Shraf-Eldeen et al., required parameter drying temperature and an assumption of constant diffusivity (Panchariya, Popovic, & Sharma, 2002). Sharma, Prasad, and Datta (2003) reported the good fit (R2 ¼ 0:99) of Page Model during drying of garlic cloves under convective drying. Similar observations were made by the same authors during combined microwave-hot air-drying of garlic cloves (Sharma & Prasad, 2001). Studies by Madamba, Driscoll, and Buckle (1996) on thin layer drying of garlic slices also indicated the suitability of Page model over Exponential and Thompson model based on R2 , mean square error and mean relative deviation modulus. The report compiled by Sarasvadia, Sawhney, Pangavhane, and Singh (1999), indicated that the rate constants significantly varied depending on the experimental conditions, material characteristics during convective drying of brined onion slices. The solution of the Fick’s equation, with the assumptions of diffusion-based moisture migration, negligible shrinkage, constant diffusion co-efficients and temperature, is simplified to get the exponential equation (Lewis model) as Mt Me ¼ MR ¼ exp ðktÞ, M0 Me
(1)
where M t , M e and M 0 are the moisture content at time t, equilibrium and initial condition respectively. k is the drying parameter. Eq. (1) has been successfully used by some researchers to model the drying behavior of agricultural products (Parti, 1991; Diamante & Munro,
701
1991). A simplified solution of Fick’s diffusion equation valid for long drying time is MR ¼ k exp ðnt=d 2 Þ.
(2)
Another model which has been used to fit thin layer drying data is the Page equation (Eq. (3)). This is a simple empirical modification of the exponential law (Eq. (1)), involving another drying parameter n. MR ¼ exp ðktn Þ.
(3)
Eq. (3) has been used by many researchers to describe the rate of moisture loss during thin layer convective drying of agricultural materials under constant drying conditions (Li & Morey, 1984). Inclusion of another empirical coefficient ‘d’ (thickness) in Eq. (3), gives the modified Page equation (Eq. (4)): MR ¼ k expðt=d 2 Þn .
(4)
Though all the above models have been tried for hot air-drying of different thin layer food materials, no report is available on the suitability of these equations for combination drying. The objectives of the present investigation were (1) to study the suitability of a few thin layer models for different drying conditions of combination drying and to estimate the drying parameters, and (2) to develop a combined empirical equation to predict the drying characteristics during combination drying.
2. Materials and methods 2.1. Raw material The local (Bellary) variety of onion (Allium cepa L.) having initial moisture content around 85–90% was used for experimentation. The sample preparation involved trimming, peeling and slicing (25 mm diameter) of onion bulbs manually. 2.2. Experimental set-up The combined infrared and hot air heating system developed in CFTRI, Mysore (Hebbar & Ramesh, 2001) was used to carry out the experiments. The schematic diagram of the heating system used for experimentation is given in Fig. 1. The continuous dryer with overall dimensions of (5.5 (L) 0.9 (B) 1.4 (H) has a capacity of 16 kg/h of raw vegetables. The dryer consists of three numbers of insulated chambers, fitted with mid-infrared (MIR) heaters on either side of the wire mesh conveyor. A through flow hot air heating system was provided for convective heating.
ARTICLE IN PRESS D.G. Praveen Kumar et al. / LWT 39 (2006) 700–705
702
DRYING CHAMBER
BLOWER
IR HEATERS TEMPERATURE SENSOR
TEMPERATURE SENSOR
MATERIAL BED
WIRE MESH CONVEYOR
HEATER
Fig. 1. Schematic diagram of combined infrared and hot air-drying system.
2.3. Experimentation The experiments were carried out in batch mode with a loading density of 15 kg/m2. The experiments were carried out at different drying temperatures (60, 70 and 80 1C), slice thickness (2, 4 and 6 mm), inlet air temperatures (30, 40 and 50 1C) and air velocities (0.8, 1.4, and 2.0 m/s). The temperatures were controlled by means of thermostats while the air velocity was regulated by flow control valves. During experimentation, one of the above processing conditions was varied, while maintaining the other conditions constant. The samples were drawn at regular intervals for moisture analysis. The final moisture content in the product was 8–9% (w.b).
Standard deviation of difference (S D ) sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P ðMRExp MRPrd Þ2 ½ðMRExp MRPrd Þ2 =N SD ¼ . N1 (6) The average percentage error (%E) 100 X jMRExp MRPrd j E¼ , N MRExp
(7)
where MRExp and MRPrd are experimental and predicted values respectively, and N denotes the number of data points. The lower values of RMSD, S D and E (%) are chosen as the criteria for goodness of fit (Ramesh, 2000) and same was followed in the present study.
2.4. Moisture analysis
3. Results and discussion
The moisture content of the samples was measured as per the procedure detailed by Ranganna (1986). The moisture content values for all the samples were transformed to dimensionless moisture ratios assuming the final moisture content to be the equilibrium moisture.
The details of the processing conditions applied and the drying time for different conditions of combination mode drying are provided in Table 1.
2.5. Statistical analysis
The temperature effect on drying of onion was studied at 60, 70 and 80 1C, keeping in view the product quality. The parameters k and n obtained from the linear plot of each model for different drying conditions are provided in Table 2. Page and modified Page models gave higher co-efficient of regression (R2 ) values (0.990–0.995) over Fick’s and Exponential models (0.767–0.933). An increase in temperature resulted in higher values of k in almost all the cases, as it largely depends on the temperature of processing. The n values did not show any particular trend as it depends on the other processing conditions also. A small difference in the slice thickness affects significantly the drying time, especially at low drying temperatures. Although, the infrared radiation has the ability to penetrate and heat the material from inside, it is essential to optimize the thickness based on the
The goodness-of-fit of different models at varied drying conditions was evaluated based on values of R2 . The regression technique was used to develop a combined equation for predicting the moisture ratios. Several criteria are available to evaluate the fitting of a model to experimental data. A model is considered to be good when the correlation coefficient value (R2 ) is higher and the mean square error (MSE) is lower (Noomhorm & Verma, 1986). Ramesh (2000) used the following statistical parameters to evaluate the comparative results. Root mean square difference (RMSD) sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P ðMRExp MRPrd Þ2 RMSD ¼ . (5) N
3.1. Drying parameters at varied processing conditions
ARTICLE IN PRESS D.G. Praveen Kumar et al. / LWT 39 (2006) 700–705
703
Table 1 Processing conditions adopted during drying of onion slices
Table 3 Equation parameters at different slice thickness of onion
S. Drying Slice No. temperature thickness (1C) (mm)
Inlet air Air temperature velocity (1C) (m/s)
Drying time (min)
Models
Slice thickness (mm)
k
n
R2
1 2 3
60 70 80
2 2 2
40 40 40
2 2 2
220 160 140
Page
2 4 6
0.000068 0.00060 0.00027
1.983 1.467 1.593
0.995 0.980 0.991
4 5 6
60 60 60
2 4 6
40 40 40
2 2 2
220 280 320
Modified Page
2 4 6
0.0011 0.0045 0.0025
1.983 1.467 1.593
0.995 0.980 0.991
7 8 9
60 60 60
2 2 2
30 40 50
2 2 2
220 220 340
Fick’s
2 4 6
1.91 1.63 1.70
0.060 0.042 0.040
0.874 0.882 0.909
10 11 12
60 60 60
2 2 2
40 40 40
2 1.4 0.8
220 260 320
Exponential
2 4 6
0.010 0.0078 0.0074
Table 2 Equation parameters at different drying temperatures of onion drying
— — —
0.789 0.808 0.831
Table 4 Equation parameters at different inlet air temperatures of onion drying
R2
Models
Inlet air temperature (1C)
k
1.984 1.613 1.589
0.995 0.990 0.989
Page
30 40 50
0.00016 0.000068 0.00072
1.639 1.983 1.423
0.991 0.995 0.986
0.0011 0.0066 0.0093
1.983 1.613 1.587
0.995 0.990 0.989
Modified Page
30 40 50
0.0015 0.0011 0.0052
1.639 1.983 1.432
0.991 0.995 0.986
60 70 80
1.91 1.68 1.68
0.060 0.075 0.087
0.874 0.912 0.933
Fick’s
30 40 50
1.58 1.93 1.39
0.032 0.061 0.036
0.889 0.873 0.906
60 70 80
0.010 0.014 0.016
0.767 0.825 0.845
Exponential
30 40 50
0.0059 0.010 0.0069
Models
Drying temperature (1C)
k
Page
60 70 80
0.000068 0.00071 0.0010
Modified Page
60 70 80
Fick’s
Exponential
n
— — —
product quality and process economics. Ginzburg (1969) reported that the depth of penetration of infrared depends on the wavelength of radiation and physicochemical nature of the material exposed. The R2 values and drying parameters for different slice thickness (2, 4 and 6 mm) are provided in Table 3. Page and modified Page models gave higher values (R2 ¼ 0:98020:995) over the other two models. Although, the drying time increased with the slice thickness, the k values did not show any clear trend (Table 3). This may be attributed to the variation in depth of penetration of infrared with the change in slice thickness, affecting heat and mass transfer. During the combination drying, the airflow helps in rapidly removing the moisture that has diffused to the
n
— — —
R2
0.809 0.767 0.840
material surface due to internal heating by infrared. The moisture removal from the surface creates the gradient within the material, further enhancing the rate of drying. However, the air temperature needs to be optimized as the cold air blast leads to drastic reduction in surface temperature while the higher temperature would cause the case hardening. In the present study, the inlet air temperature was varied, while maintaining the same drying temperature (60 1C) in the chamber. In the present case also Page and modified Page models gave higher R2 (0.987–0.995) values (Table 4). At lower air temperature (30 1C) the drying temperature was controlled by the infrared alone and on increasing the inlet air temperature, the convective heating also played a role, affecting the drying parameters.
ARTICLE IN PRESS D.G. Praveen Kumar et al. / LWT 39 (2006) 700–705
704
Table 5 Equation parameters at different air velocity of onion drying R2
Models
Air velocity (m/s)
k
n
Page
2.0 1.4 0.8
0.000068 0.000084 0.00036
1.983 1.879 1.512
0.995 0.990 0.983
Modified Page
2.0 1.4 0.8
0.0011 0.0012 0.0029
1.983 1.879 1.512
0.995 0.990 0.983
Fick’s
2.0 1.4 0.8
1.91 1.98 1.59
0.060 0.053 0.035
0.874 0.901 0.914
Exponential
2.0 1.4 0.8
0.010 0.0092 0.0065
— — —
0.767 0.791 0.836
All four models were tested for three air velocities (0.8, 1.4 and 2.0 m/s). The R2 values, and parameters k and n for different air velocities are provided in Table 5. The R2 values were at a maximum for Page model and modified Page model (R2 ¼ 0:98320:995). A decrease in air velocity from 2.0 to 1.4 m/s did not affect the rate appreciably (Table 5). However, further reduction in air velocity reduced the rate of drying.
four models gave a fairly good fit for both the parameters k (0.852–0.989) and n (0.860–0.900) with modified Page (k ¼ 0:914, n ¼ 0:900) and Exponential (k ¼ 0:989) models giving higher values. The moisture ratios were estimated (Eqs. (1)–(4)) using the predicted parameters and compared with the experimental values. The best-fit model was chosen based on the least values of root mean square deviation (RMSD), standard deviation (SD) and percent error (%E) (Table 6). Modified Page model gave the lowest values of RMSD and E (%) over the other models. Though the percent error for the modified Page model was slightly higher (10.4%), it was close to the acceptable limit of 10% and considered as the best fit for the operated conditions. Earlier studies by the authors (Hebbar & Ramesh, 2003) indicated that combined drying of 2 mm onion slices at a drying temperature of 60 1C, assisted by hot air (40 1C) blown at 2 m/s velocity was optimum for retention of pungency and color. The pungency of the onion samples were measured in terms of pyruvic acid content, while the browning index was used as the measure of color. For the modified Page model a plot of experimental versus predicted moisture ratios for the above condition was drawn (Fig. 2), which showed a good fit (R2 ¼ 0:983).
Table 6 Statistical analysis for the comparison of experimental and predicted moisture values
3.2. Model development The partial least square (PLS) regression method was used to estimate the drying parameters for all the four models. The equations obtained for k and n with the modified Page and Exponential model are provided below: Modified Page model
Model
RMSD
SD
E (%)
Page Modified Page Fick’s Exponential
0.1496 0.0260 0.2992 0.1500
0.7285 0.2904 0.2985 0.1262
26.7 10.4 32.6 34.0
k ¼ 0:04127 þ 0:00055x1 0:00027x2
1
þ 1:6704 1006 x3 þ 0:00158x4 þ 3:086 1005 x5 , ð8Þ
x1 þ 0:000819x2
0:0004226x3 0:00218x4 6:211 10
05
Moisture Ratio
ð9Þ
Exponential model n ¼ 0:04243 þ 1:7625 10
Experimental Predicted
0.8 0.7
n ¼ 7:6758 0:0483x1 þ 0:0509x2 0:0279x3 0:2107x4 0:0063x5 .
05
0.9
0.6 0.6 0.4 0.3 0.2
x5 , ð10Þ
where x1 is the drying temperature (1C), x2 the slice thickness (mm), x3 the inlet air temperature (1C), x4 the air velocities (m/s), x5 the drying time (min). The above models were validated by comparing the experimental and predicted values of parameters. All the
0.1 0 0
40
80
120
160
200
240
Time (min)
Fig. 2. Comparison of experimental and predicted moisture ratios for modified Page model: drying temperature—60 1C; slice thickness— 2 mm; air temperature—40 1C and air velocity—2 m/s.
ARTICLE IN PRESS D.G. Praveen Kumar et al. / LWT 39 (2006) 700–705
4. Conclusions Thin layer drying models such as Page, modified Page, Fick’s and Exponential models, which are commonly used to describe the drying kinetics of food materials, were tested for combination mode drying. Page and Modified Page models gave a better fit (R2 ¼ 0:98020:995) over the other two models (R2 ¼ 0:76720:933). The combined regression equation developed to predict the drying parameters, gave a fairly good fit (R2 ¼ 0:85220:989) for all the four models tested. Moisture ratios estimated using predicted drying parameters gave a better fit for modified Page model.
Acknowledgements The authors wish to thank Dr. V. Prakash, Director, CFTRI, Mysore and Dr. KSMS Raghavarao, Head, Food Engineering Department, CFTRI for their kind support and encouragement. References Anonymous. (2001). Production Yearbook. Food and Agricultural Organization (FAO) p. 55. Anonymous. (2002). Export Statistics for Agro and Food Products. India: Agricultural and Processed Food Products Export Development Authority (APEDA) pp. 295–297. Chu, S. T., & Hustrulid, A. (1968). General characteristics of variable effective diffusivity process and the thermodynamic equilibrium moisture content. Transaction of the ASAE, 11(5) pp. 709–710, 715. Diamante, L. M., & Munro, P. A. (1991). Mathematical modeling of hot air drying of sweet potato slices. International Journal of Food Science and Technology, 26, 99–109. Ginzburg, A. S. (1969). Application of infrared radiation in food processing. London: Leonard Hill Books. Hebbar, U. H., & Ramesh, M. N. (2001). A combined infrared and hot air heating system for food processing, Indian patent application, 366/DEL/02. Hebbar, U. H., & Ramesh, M. N. (2003). An improved process for the retention of pungency during dehydration of onion rings, Indian patent application, 316/NF/03. Henderson, S. M., & Pabis, S. (1961). Grain drying theory. I. Temperature effect on drying coefficient. Journal of Agriculture Engineering Research, 6, 169–174. Kiranoudis, C. T., Maroulis, Z. B., & Kouris, M. D. (1992). Drying kinetics of onion and green pepper. Drying Technology, 10(4), 995–1011.
705
Li, H., & Morey, R. V. (1984). Thin-layer drying of yellow dent corn. Transaction of the ASAE, 27(2), 581–585. Li, Y., Cao, C. W., & Liu, D. Y. (1997). Simulation of recirculating circular grain dryer with temperature stage. Drying Technology, 15(1), 201–214. Luikov, A. V. (1966). Heat and mass transfer in capillary porous bodies. Oxford: Pergamon Press. Madamba, S. P., Driscoll, H. R., & Buckle, K. A. (1996). The thin layer drying characteristics of garlic slices. Journal of Food Engineering, 29, 75–97. Mongpraneet, S., Abe, T., & Tsurusaki, T. (2002). Accelerated drying of welsh onion by far infrared radiation under vacuum conditions. Journal of Food Engineering, 55, 147–156. Mazza, G., & Lemaguer, M. (1980). Dehydration of onion: Some theoretical and practical considerations. Journal of Food Technology, 15, 181–194. Noomhorm, A., & Verma, L. R. (1986). Generalized single layer rice drying models. Transaction of ASAE, 29(2), 587–591. Parti, M. (1991). Evaluation of selected mathematical models for grain drying. Drying 91. Amsterdam: Elsevier Science Publishers. Panchariya, P. C., Popovic, D., & Sharma, A. L. (2002). Thin layer modeling of black tea drying process. Journal of Food Engineering, 52, 349–357. Ramesh, M. N. (2000). Effect of cooking and drying on the thermal conductivity of rice. International Journal of Food Properties, 3(1), 77–92. Ranganna, S. (1986). Handbook of analysis and quality control for fruit and vegetable products. New Delhi: Tata McGraw-Hill Publishing Company Limited. Rapusas, R. S., & Driscoll, R. H. (1995). The thin layer drying characteristics of white onion slices. Drying Technology, 13(8&9), 1905–1931. Sarasvadia, P. N., Sawhney, R. L., Pangavhane, D. R., & Singh, S. P. (1999). Drying behavior of brined onion slices. Journal of Food Engineering, 40, 219–226. Sharma, G. P., & Prasad, S. (2001). Drying of garlic cloves by microwave-hot air combination. Journal of Food Engineering, 50, 99–105. Sharma, G. P., Prasad, S., & Datta, A. K. (2003). Drying kinetics of garlic cloves under convective drying conditions. Journal of Food Science and Technology, 40(1), 45–51. Singh, B., & Kumar, B. L. M. (1984). Dehydration of onion varieties. Indian Food Packer, 67–74. Sinicio, R., & Muir, W. (1996). Comparison of mathematical models to simulate aeration of wheat stored in Brazil. Journal of Agricultural Engineering Research, 64(2), 119–130. Steffe, J. F., & Singh, R. P. (1980). Liquid diffusivity of rough rice components. Transaction of ASAE, 23(3) 767–774, 782. Walton, C. R., White, G. M., & Ross, I. J. (1988). A cellular diffusion based drying model for corn. Transaction of ASAE, 31(1), 279–283. Whitaker, T. B., & Young, J. H. (1972). Simulation of moisture movement in peanut kernels; evaluation of diffusion equation. Transaction of ASAE, 16(1), 118–120.
LWT 39 (2006) 700–705 www.elsevier.com/locate/lwt
Suitability of thin layer models for infrared–hot air-drying of onion slices D.G. Praveen Kumar, H. Umesh Hebbar, M.N. Ramesh Department of Food Engineering, Central Food Technological Research Institute, Mysore—570 020, India Received 9 February 2004; accepted 17 March 2005
Abstract Onion slices were dried under different processing conditions applying infrared radiation assisted by hot air. Drying temperature, slice thickness, inlet air temperature and air velocity were varied to study the drying behavior. Thin layer models such as Page, modified Page, Fick’s and Exponential models, which are used to describe the drying kinetics of food materials, were tested for the combination mode drying. The linear plots for Page and modified Page models gave a better fit (R2 ¼ 0:98020:995) over the other two models (R2 ¼ 0:76720:933). A combined regression equation developed to predict the drying parameters (k and n) for all the four models gave a fairly good fit (R2 ¼ 0:85220:989). The modified Page model gave better predictions for drying characteristics over the other models. r 2005 Swiss Society of Food Science and Technology. Published by Elsevier Ltd. All rights reserved. Keywords: Combined drying; Hot air; Infrared; Onion; Thin layer
1. Introduction Onion ranks third highest in world vegetable production with an annual production of 47 million tonnes. India is the second largest producer of onion with 4.9 million tonnes production per annum (Anonymous, 2001). Onion finds widespread usage in both fresh and dried forms. Dried onions are a product of considerable importance in world trade and are made in several forms: flaked, minced, chopped and powdered. It is used as flavor additives in wide variety of food formulations such as comminuted meats, sauces, soups, salad dressings, pickle and pickle relishes. Dried onion export by India reached nearly 7000 tonnes during the year 2001–2002 (Anonymous, 2002). Drying of foods is mainly aimed at reducing the water activity to extend the shelf life. The major challenge during drying of food materials is to reduce the moisture content of the material to the desired level without Corresponding author. Tel.: +0821 2513910; fax: +0821 2517233.
E-mail address: [email protected] (H.U. Hebbar).
substantial loss of flavor, taste, color and nutrients. Dried onion is valued for its pungency and is normally measured in terms of pyruvic acid content (Singh & Kumar, 1984). Product color is the other quality parameter that needs to be maintained during onion drying. So, the drying conditions are to be optimized to retain maximum product quality, besides considering the process economics. Different varieties of onions with high pungency are grown to suit the drying requirements. Hot air-drying is the most commonly employed commercial technique for drying of biological products (Mazza & Lemaguer, 1980). The processing temperature mainly influences the quality changes during drying. The major limitation of hot air-drying is that it takes longer for drying, resulting in product quality degradation. New and innovative techniques that increase the drying rate and enhance product quality have achieved considerable attention in the recent past. Drying by infrared radiation is one among them, gaining popularity because of its inherent advantages over conventional heating (Mongpraneet, Abe, & Tsurusaki, 2002). The
0023-6438/$30.00 r 2005 Swiss Society of Food Science and Technology. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.lwt.2005.03.021
ARTICLE IN PRESS D.G. Praveen Kumar et al. / LWT 39 (2006) 700–705
ability of infrared radiation to penetrate and heat the inner layers results in higher rate of heat and mass transfer (Ginzburg, 1969). Application of hot air assisted infrared drying for high moisture content materials is reported to be beneficial as it provides a synergistic effect (Ginzburg, 1969). 1.1. Mathematical model Numerous empirical and semi-empirical models have been proposed to describe the drying behavior of agricultural products (Li, Cao, & Liu, 1997; Sinicio & Muir, 1996). Mazza and Lemaguer (1980) have developed theoretical models for convective drying of onion. Rapusas and Driscoll (1995), and Kiranoudis, Maroulis, and Kouris (1992) proposed empirical relations for onions in the form of Arrhenius type and power models respectively. Diffusion-type models were proposed by several researchers to model the drying behavior of various agricultural products (Henderson & Pabis, 1961; Chu & Hustrulid, 1968; Whitaker & Young, 1972; Steffe & Singh, 1980; Walton, White, & Ross, 1988). Luikov (1966) proposed a mathematical model to describe the drying behavior of products based on the internal moisture transport mechanism. The two-term model developed by Shraf-Eldeen et al., required parameter drying temperature and an assumption of constant diffusivity (Panchariya, Popovic, & Sharma, 2002). Sharma, Prasad, and Datta (2003) reported the good fit (R2 ¼ 0:99) of Page Model during drying of garlic cloves under convective drying. Similar observations were made by the same authors during combined microwave-hot air-drying of garlic cloves (Sharma & Prasad, 2001). Studies by Madamba, Driscoll, and Buckle (1996) on thin layer drying of garlic slices also indicated the suitability of Page model over Exponential and Thompson model based on R2 , mean square error and mean relative deviation modulus. The report compiled by Sarasvadia, Sawhney, Pangavhane, and Singh (1999), indicated that the rate constants significantly varied depending on the experimental conditions, material characteristics during convective drying of brined onion slices. The solution of the Fick’s equation, with the assumptions of diffusion-based moisture migration, negligible shrinkage, constant diffusion co-efficients and temperature, is simplified to get the exponential equation (Lewis model) as Mt Me ¼ MR ¼ exp ðktÞ, M0 Me
(1)
where M t , M e and M 0 are the moisture content at time t, equilibrium and initial condition respectively. k is the drying parameter. Eq. (1) has been successfully used by some researchers to model the drying behavior of agricultural products (Parti, 1991; Diamante & Munro,
701
1991). A simplified solution of Fick’s diffusion equation valid for long drying time is MR ¼ k exp ðnt=d 2 Þ.
(2)
Another model which has been used to fit thin layer drying data is the Page equation (Eq. (3)). This is a simple empirical modification of the exponential law (Eq. (1)), involving another drying parameter n. MR ¼ exp ðktn Þ.
(3)
Eq. (3) has been used by many researchers to describe the rate of moisture loss during thin layer convective drying of agricultural materials under constant drying conditions (Li & Morey, 1984). Inclusion of another empirical coefficient ‘d’ (thickness) in Eq. (3), gives the modified Page equation (Eq. (4)): MR ¼ k expðt=d 2 Þn .
(4)
Though all the above models have been tried for hot air-drying of different thin layer food materials, no report is available on the suitability of these equations for combination drying. The objectives of the present investigation were (1) to study the suitability of a few thin layer models for different drying conditions of combination drying and to estimate the drying parameters, and (2) to develop a combined empirical equation to predict the drying characteristics during combination drying.
2. Materials and methods 2.1. Raw material The local (Bellary) variety of onion (Allium cepa L.) having initial moisture content around 85–90% was used for experimentation. The sample preparation involved trimming, peeling and slicing (25 mm diameter) of onion bulbs manually. 2.2. Experimental set-up The combined infrared and hot air heating system developed in CFTRI, Mysore (Hebbar & Ramesh, 2001) was used to carry out the experiments. The schematic diagram of the heating system used for experimentation is given in Fig. 1. The continuous dryer with overall dimensions of (5.5 (L) 0.9 (B) 1.4 (H) has a capacity of 16 kg/h of raw vegetables. The dryer consists of three numbers of insulated chambers, fitted with mid-infrared (MIR) heaters on either side of the wire mesh conveyor. A through flow hot air heating system was provided for convective heating.
ARTICLE IN PRESS D.G. Praveen Kumar et al. / LWT 39 (2006) 700–705
702
DRYING CHAMBER
BLOWER
IR HEATERS TEMPERATURE SENSOR
TEMPERATURE SENSOR
MATERIAL BED
WIRE MESH CONVEYOR
HEATER
Fig. 1. Schematic diagram of combined infrared and hot air-drying system.
2.3. Experimentation The experiments were carried out in batch mode with a loading density of 15 kg/m2. The experiments were carried out at different drying temperatures (60, 70 and 80 1C), slice thickness (2, 4 and 6 mm), inlet air temperatures (30, 40 and 50 1C) and air velocities (0.8, 1.4, and 2.0 m/s). The temperatures were controlled by means of thermostats while the air velocity was regulated by flow control valves. During experimentation, one of the above processing conditions was varied, while maintaining the other conditions constant. The samples were drawn at regular intervals for moisture analysis. The final moisture content in the product was 8–9% (w.b).
Standard deviation of difference (S D ) sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P ðMRExp MRPrd Þ2 ½ðMRExp MRPrd Þ2 =N SD ¼ . N1 (6) The average percentage error (%E) 100 X jMRExp MRPrd j E¼ , N MRExp
(7)
where MRExp and MRPrd are experimental and predicted values respectively, and N denotes the number of data points. The lower values of RMSD, S D and E (%) are chosen as the criteria for goodness of fit (Ramesh, 2000) and same was followed in the present study.
2.4. Moisture analysis
3. Results and discussion
The moisture content of the samples was measured as per the procedure detailed by Ranganna (1986). The moisture content values for all the samples were transformed to dimensionless moisture ratios assuming the final moisture content to be the equilibrium moisture.
The details of the processing conditions applied and the drying time for different conditions of combination mode drying are provided in Table 1.
2.5. Statistical analysis
The temperature effect on drying of onion was studied at 60, 70 and 80 1C, keeping in view the product quality. The parameters k and n obtained from the linear plot of each model for different drying conditions are provided in Table 2. Page and modified Page models gave higher co-efficient of regression (R2 ) values (0.990–0.995) over Fick’s and Exponential models (0.767–0.933). An increase in temperature resulted in higher values of k in almost all the cases, as it largely depends on the temperature of processing. The n values did not show any particular trend as it depends on the other processing conditions also. A small difference in the slice thickness affects significantly the drying time, especially at low drying temperatures. Although, the infrared radiation has the ability to penetrate and heat the material from inside, it is essential to optimize the thickness based on the
The goodness-of-fit of different models at varied drying conditions was evaluated based on values of R2 . The regression technique was used to develop a combined equation for predicting the moisture ratios. Several criteria are available to evaluate the fitting of a model to experimental data. A model is considered to be good when the correlation coefficient value (R2 ) is higher and the mean square error (MSE) is lower (Noomhorm & Verma, 1986). Ramesh (2000) used the following statistical parameters to evaluate the comparative results. Root mean square difference (RMSD) sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P ðMRExp MRPrd Þ2 RMSD ¼ . (5) N
3.1. Drying parameters at varied processing conditions
ARTICLE IN PRESS D.G. Praveen Kumar et al. / LWT 39 (2006) 700–705
703
Table 1 Processing conditions adopted during drying of onion slices
Table 3 Equation parameters at different slice thickness of onion
S. Drying Slice No. temperature thickness (1C) (mm)
Inlet air Air temperature velocity (1C) (m/s)
Drying time (min)
Models
Slice thickness (mm)
k
n
R2
1 2 3
60 70 80
2 2 2
40 40 40
2 2 2
220 160 140
Page
2 4 6
0.000068 0.00060 0.00027
1.983 1.467 1.593
0.995 0.980 0.991
4 5 6
60 60 60
2 4 6
40 40 40
2 2 2
220 280 320
Modified Page
2 4 6
0.0011 0.0045 0.0025
1.983 1.467 1.593
0.995 0.980 0.991
7 8 9
60 60 60
2 2 2
30 40 50
2 2 2
220 220 340
Fick’s
2 4 6
1.91 1.63 1.70
0.060 0.042 0.040
0.874 0.882 0.909
10 11 12
60 60 60
2 2 2
40 40 40
2 1.4 0.8
220 260 320
Exponential
2 4 6
0.010 0.0078 0.0074
Table 2 Equation parameters at different drying temperatures of onion drying
— — —
0.789 0.808 0.831
Table 4 Equation parameters at different inlet air temperatures of onion drying
R2
Models
Inlet air temperature (1C)
k
1.984 1.613 1.589
0.995 0.990 0.989
Page
30 40 50
0.00016 0.000068 0.00072
1.639 1.983 1.423
0.991 0.995 0.986
0.0011 0.0066 0.0093
1.983 1.613 1.587
0.995 0.990 0.989
Modified Page
30 40 50
0.0015 0.0011 0.0052
1.639 1.983 1.432
0.991 0.995 0.986
60 70 80
1.91 1.68 1.68
0.060 0.075 0.087
0.874 0.912 0.933
Fick’s
30 40 50
1.58 1.93 1.39
0.032 0.061 0.036
0.889 0.873 0.906
60 70 80
0.010 0.014 0.016
0.767 0.825 0.845
Exponential
30 40 50
0.0059 0.010 0.0069
Models
Drying temperature (1C)
k
Page
60 70 80
0.000068 0.00071 0.0010
Modified Page
60 70 80
Fick’s
Exponential
n
— — —
product quality and process economics. Ginzburg (1969) reported that the depth of penetration of infrared depends on the wavelength of radiation and physicochemical nature of the material exposed. The R2 values and drying parameters for different slice thickness (2, 4 and 6 mm) are provided in Table 3. Page and modified Page models gave higher values (R2 ¼ 0:98020:995) over the other two models. Although, the drying time increased with the slice thickness, the k values did not show any clear trend (Table 3). This may be attributed to the variation in depth of penetration of infrared with the change in slice thickness, affecting heat and mass transfer. During the combination drying, the airflow helps in rapidly removing the moisture that has diffused to the
n
— — —
R2
0.809 0.767 0.840
material surface due to internal heating by infrared. The moisture removal from the surface creates the gradient within the material, further enhancing the rate of drying. However, the air temperature needs to be optimized as the cold air blast leads to drastic reduction in surface temperature while the higher temperature would cause the case hardening. In the present study, the inlet air temperature was varied, while maintaining the same drying temperature (60 1C) in the chamber. In the present case also Page and modified Page models gave higher R2 (0.987–0.995) values (Table 4). At lower air temperature (30 1C) the drying temperature was controlled by the infrared alone and on increasing the inlet air temperature, the convective heating also played a role, affecting the drying parameters.
ARTICLE IN PRESS D.G. Praveen Kumar et al. / LWT 39 (2006) 700–705
704
Table 5 Equation parameters at different air velocity of onion drying R2
Models
Air velocity (m/s)
k
n
Page
2.0 1.4 0.8
0.000068 0.000084 0.00036
1.983 1.879 1.512
0.995 0.990 0.983
Modified Page
2.0 1.4 0.8
0.0011 0.0012 0.0029
1.983 1.879 1.512
0.995 0.990 0.983
Fick’s
2.0 1.4 0.8
1.91 1.98 1.59
0.060 0.053 0.035
0.874 0.901 0.914
Exponential
2.0 1.4 0.8
0.010 0.0092 0.0065
— — —
0.767 0.791 0.836
All four models were tested for three air velocities (0.8, 1.4 and 2.0 m/s). The R2 values, and parameters k and n for different air velocities are provided in Table 5. The R2 values were at a maximum for Page model and modified Page model (R2 ¼ 0:98320:995). A decrease in air velocity from 2.0 to 1.4 m/s did not affect the rate appreciably (Table 5). However, further reduction in air velocity reduced the rate of drying.
four models gave a fairly good fit for both the parameters k (0.852–0.989) and n (0.860–0.900) with modified Page (k ¼ 0:914, n ¼ 0:900) and Exponential (k ¼ 0:989) models giving higher values. The moisture ratios were estimated (Eqs. (1)–(4)) using the predicted parameters and compared with the experimental values. The best-fit model was chosen based on the least values of root mean square deviation (RMSD), standard deviation (SD) and percent error (%E) (Table 6). Modified Page model gave the lowest values of RMSD and E (%) over the other models. Though the percent error for the modified Page model was slightly higher (10.4%), it was close to the acceptable limit of 10% and considered as the best fit for the operated conditions. Earlier studies by the authors (Hebbar & Ramesh, 2003) indicated that combined drying of 2 mm onion slices at a drying temperature of 60 1C, assisted by hot air (40 1C) blown at 2 m/s velocity was optimum for retention of pungency and color. The pungency of the onion samples were measured in terms of pyruvic acid content, while the browning index was used as the measure of color. For the modified Page model a plot of experimental versus predicted moisture ratios for the above condition was drawn (Fig. 2), which showed a good fit (R2 ¼ 0:983).
Table 6 Statistical analysis for the comparison of experimental and predicted moisture values
3.2. Model development The partial least square (PLS) regression method was used to estimate the drying parameters for all the four models. The equations obtained for k and n with the modified Page and Exponential model are provided below: Modified Page model
Model
RMSD
SD
E (%)
Page Modified Page Fick’s Exponential
0.1496 0.0260 0.2992 0.1500
0.7285 0.2904 0.2985 0.1262
26.7 10.4 32.6 34.0
k ¼ 0:04127 þ 0:00055x1 0:00027x2
1
þ 1:6704 1006 x3 þ 0:00158x4 þ 3:086 1005 x5 , ð8Þ
x1 þ 0:000819x2
0:0004226x3 0:00218x4 6:211 10
05
Moisture Ratio
ð9Þ
Exponential model n ¼ 0:04243 þ 1:7625 10
Experimental Predicted
0.8 0.7
n ¼ 7:6758 0:0483x1 þ 0:0509x2 0:0279x3 0:2107x4 0:0063x5 .
05
0.9
0.6 0.6 0.4 0.3 0.2
x5 , ð10Þ
where x1 is the drying temperature (1C), x2 the slice thickness (mm), x3 the inlet air temperature (1C), x4 the air velocities (m/s), x5 the drying time (min). The above models were validated by comparing the experimental and predicted values of parameters. All the
0.1 0 0
40
80
120
160
200
240
Time (min)
Fig. 2. Comparison of experimental and predicted moisture ratios for modified Page model: drying temperature—60 1C; slice thickness— 2 mm; air temperature—40 1C and air velocity—2 m/s.
ARTICLE IN PRESS D.G. Praveen Kumar et al. / LWT 39 (2006) 700–705
4. Conclusions Thin layer drying models such as Page, modified Page, Fick’s and Exponential models, which are commonly used to describe the drying kinetics of food materials, were tested for combination mode drying. Page and Modified Page models gave a better fit (R2 ¼ 0:98020:995) over the other two models (R2 ¼ 0:76720:933). The combined regression equation developed to predict the drying parameters, gave a fairly good fit (R2 ¼ 0:85220:989) for all the four models tested. Moisture ratios estimated using predicted drying parameters gave a better fit for modified Page model.
Acknowledgements The authors wish to thank Dr. V. Prakash, Director, CFTRI, Mysore and Dr. KSMS Raghavarao, Head, Food Engineering Department, CFTRI for their kind support and encouragement. References Anonymous. (2001). Production Yearbook. Food and Agricultural Organization (FAO) p. 55. Anonymous. (2002). Export Statistics for Agro and Food Products. India: Agricultural and Processed Food Products Export Development Authority (APEDA) pp. 295–297. Chu, S. T., & Hustrulid, A. (1968). General characteristics of variable effective diffusivity process and the thermodynamic equilibrium moisture content. Transaction of the ASAE, 11(5) pp. 709–710, 715. Diamante, L. M., & Munro, P. A. (1991). Mathematical modeling of hot air drying of sweet potato slices. International Journal of Food Science and Technology, 26, 99–109. Ginzburg, A. S. (1969). Application of infrared radiation in food processing. London: Leonard Hill Books. Hebbar, U. H., & Ramesh, M. N. (2001). A combined infrared and hot air heating system for food processing, Indian patent application, 366/DEL/02. Hebbar, U. H., & Ramesh, M. N. (2003). An improved process for the retention of pungency during dehydration of onion rings, Indian patent application, 316/NF/03. Henderson, S. M., & Pabis, S. (1961). Grain drying theory. I. Temperature effect on drying coefficient. Journal of Agriculture Engineering Research, 6, 169–174. Kiranoudis, C. T., Maroulis, Z. B., & Kouris, M. D. (1992). Drying kinetics of onion and green pepper. Drying Technology, 10(4), 995–1011.
705
Li, H., & Morey, R. V. (1984). Thin-layer drying of yellow dent corn. Transaction of the ASAE, 27(2), 581–585. Li, Y., Cao, C. W., & Liu, D. Y. (1997). Simulation of recirculating circular grain dryer with temperature stage. Drying Technology, 15(1), 201–214. Luikov, A. V. (1966). Heat and mass transfer in capillary porous bodies. Oxford: Pergamon Press. Madamba, S. P., Driscoll, H. R., & Buckle, K. A. (1996). The thin layer drying characteristics of garlic slices. Journal of Food Engineering, 29, 75–97. Mongpraneet, S., Abe, T., & Tsurusaki, T. (2002). Accelerated drying of welsh onion by far infrared radiation under vacuum conditions. Journal of Food Engineering, 55, 147–156. Mazza, G., & Lemaguer, M. (1980). Dehydration of onion: Some theoretical and practical considerations. Journal of Food Technology, 15, 181–194. Noomhorm, A., & Verma, L. R. (1986). Generalized single layer rice drying models. Transaction of ASAE, 29(2), 587–591. Parti, M. (1991). Evaluation of selected mathematical models for grain drying. Drying 91. Amsterdam: Elsevier Science Publishers. Panchariya, P. C., Popovic, D., & Sharma, A. L. (2002). Thin layer modeling of black tea drying process. Journal of Food Engineering, 52, 349–357. Ramesh, M. N. (2000). Effect of cooking and drying on the thermal conductivity of rice. International Journal of Food Properties, 3(1), 77–92. Ranganna, S. (1986). Handbook of analysis and quality control for fruit and vegetable products. New Delhi: Tata McGraw-Hill Publishing Company Limited. Rapusas, R. S., & Driscoll, R. H. (1995). The thin layer drying characteristics of white onion slices. Drying Technology, 13(8&9), 1905–1931. Sarasvadia, P. N., Sawhney, R. L., Pangavhane, D. R., & Singh, S. P. (1999). Drying behavior of brined onion slices. Journal of Food Engineering, 40, 219–226. Sharma, G. P., & Prasad, S. (2001). Drying of garlic cloves by microwave-hot air combination. Journal of Food Engineering, 50, 99–105. Sharma, G. P., Prasad, S., & Datta, A. K. (2003). Drying kinetics of garlic cloves under convective drying conditions. Journal of Food Science and Technology, 40(1), 45–51. Singh, B., & Kumar, B. L. M. (1984). Dehydration of onion varieties. Indian Food Packer, 67–74. Sinicio, R., & Muir, W. (1996). Comparison of mathematical models to simulate aeration of wheat stored in Brazil. Journal of Agricultural Engineering Research, 64(2), 119–130. Steffe, J. F., & Singh, R. P. (1980). Liquid diffusivity of rough rice components. Transaction of ASAE, 23(3) 767–774, 782. Walton, C. R., White, G. M., & Ross, I. J. (1988). A cellular diffusion based drying model for corn. Transaction of ASAE, 31(1), 279–283. Whitaker, T. B., & Young, J. H. (1972). Simulation of moisture movement in peanut kernels; evaluation of diffusion equation. Transaction of ASAE, 16(1), 118–120.