Evaluation and Modelling of Water Absorption Characteristics of Paddy

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S. J. Kale, S.K. Jha, G. K. Jha and D. V. K. Samuel JAE : 50 (3)

29

Journal of Agricultural Engineering Vol. 50 (3): July-September, 2013

Hydration of food grains is one of the most important pre-treatment during their processing. It is an essential step in parboiling of paddy, during which moisture content is raised to achieve adequate gelatinization of starch. Bhattacharya (1985) suggested that a minimum of 40-45% (d.b.) moisture content is needed to attain complete gelatinization of paddy grain.Water absorption is a temperature dependent process. At lower temperatures, water absorption rate is slow and increases with increase in temperature (Mohanty et al., 2002). Soaking at low temperature may cause microbial growth and affects aroma and colour of the grains. Higher temperatures reduce the soaking time, but may cause actual cooking of rice, excessive husk splitting and increased leaching losses undesirable during parboiling. Generally, hydration of paddy is suggested to be carried out at temperature below the gelatinization temperature of rice (Luh and Mikus, 1980).

The importance of hydration in processing has led the researchers to evaluate the water absorption characteristics of different food grains. Evaluation and modelling of water absorption behaviour have been reported by researchers for different grains such as paddy (Igathinathane et al., 2005; Thakur and Gupta, 2006; Kashaninejad et al., 2007; Bakalis et al., 2009; Perez et al., 2011), wheat (Kashaninejad et al., 2009), black gram (Swami et al., 2007), barley (Mayolle et al., 2012), soyabean (Gulati et al., 2010), sesame seeds

Evaluation and Modelling of Water Absorption Characteristics of Paddy

S. J. Kale1, S.K. Jha2, G. K. Jha3 and D. V. K. Samuel4

manuscript received: march, 2013 Revised manuscript accepted: July, 2013

AbstrACt

Water absorption characteristics of paddy (PB1121) were evaluated over a temperature range 40 to 80oC. The equilibrium moisture content of the grain during water absorption varied from 48.24 to 55% (d.b.). Average critical moisture content of PB1121 for soaking during parboiling was 41.67% (d.b.). Average moisture diffusivity during water absorption process was 5.06x10-10 m2.s-1 and activation energy was 53.85 kJ.mol-1.Modelling of water absorption behaviour using Page model revealed that it fitted well at individual temperatures (R2>0.9). However, the Generalized Page model provided poor performance with R2 of 0.65, MSE 0.0018 and mean relative deviation modulus (% P) 48.02, whereas multi-layer perceptron neural network model fitted well with R2 =0.99, MSE =0.0013 and P =14.69%.

Key words: Diffusivity, activation energy, Page model, neural network model

(Khazaei and Mohammadi, 2009), chickpeas (Yildirim et al., 2011), etc. Milk absorption characteristics of popped gorgon nut has been evaluated and modelled by Jha and Sharma (2010).

For modelling of water absorption behaviour of paddy, number of models have been tested and Page model has been reported to be the best (Kashaninejad et al., 2007). However, the efficacy of Page model has been reported at individual temperatures instead of within a range of temperatures. Recently, the use of artificial neural network (ANN) for modelling of various food processing operations has been cited by researchers (Erenturk and Erenturk, 2007; Movagharnejad and Nikzad, 2007; Lertworasirikul and Saetan, 2010; Gulsah and Cengiz, 2011). It has been reported that neural network models give better performance in terms of high computation rate, learning ability, prediction of unknown pattern and flexibility. Kashaninejad et al. (2009) reported that multi-layer perceptron (MLP) neural network model showed the best performance to describe the water absorption behaviour of wheat kernel, followed by Radial Basis Function (RBF) neural network and Generalized Page model with respect to the mean relative deviation modulus.

India produces more than 70% of the world’s basmati rice production. Pusa Basmati 1121 (PB1121) is world’s longest paddy grain and the most popular basmati varieties

1Research Scholar and 2Senior Scientist, Division of Post Harvest Technology, ([email protected]), 3Senior Scientist, Division of Agricultural Economics, 4Head, Division of Agricultural Engineering, Indian Agricultural Research Institute, New Delhi.

mailto:[email protected]

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July-September, 2013 Evaluation and Modelling of Water Absorption Characteristics of Paddy

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of northern India, which commands major share of basmati exports (Patel, 2011) from the country. Due to it’s high length, there is excessive breakage of grain during milling. This high breakage can be reduced by parboiling the paddy, which requires the knowledge of water absorption behaviour of the grain, and not available yet. The objective of the present study was to evaluate the water absorption characteristics of PB1121 variety of paddy, and to model it using Page and neural network models.

MAtEriAls And MEthods

sample PreparationPaddy (PB1121) grains were obtained from Seed Processing Unit, IARI, New Delhi, India. Grains were cleaned and screened to get samples of uniform size. Grains were received at average moisture content 10.90 % (d.b.), which was packed and stored in a refrigerator at about 5oC till the trial was conducted. The moisture content of samples was determined in triplicate by oven drying at 103 ± 2oC until a constant weight was reached (AOAC, 2000).

Water Absorption testFor water absorption test, about 10±0.5g of paddy samples were put in 200 ml of distilled water. The temperature of water was maintained at seven different levels of 40, 50, 60, 65, 70, 75 and 80oC. Paddy, along with the soaking water, was kept in a water bath with temperature control facility having an accuracy of ±0.5oC. During water absorption test, samples were removed at predetermined time intervals and weighed after removing the surface moisture using blotting paper. All tests were conducted thrice to reduce error.Water absorption rate (WAR) was determined as g of water absorbed/h/100g of dry matter using moisture gained by sample at each interval.

Determination of equilibrium moisture content (Me) during water absorption process is an important parameter, as it gives an idea on to what extent the process will continue. This is a temperature dependent process, and is difficult to determine experimentally as water absorption process becomes very slow towards saturation point. It was, therefore, calculated by extrapolating the linear plot of rate of change of moisture (dM/dt) against the moisture content (M) to the point where dM/dt tends to zero (Kashaninejad et al., 2009).

Moisture diffusivity and Activation EnergyAn infinite series diffusion equation (Eq.1), suggested by Crank (1975), was used to describe the water absorption behaviour of the sample and moisture diffusivities at

all water temperatures. The equation (Eq.1) has been subsequently used by Das et al. (2004), Ahromrit et al. (2006), Thakur and Gupta (2006) and others for predicting the moisture ratio.

…(1)

Where,MR = Moisture ratio,M = Moisture content at any instance (% d.b.),Mo = Initial moisture content (% d.b.),Me = Equilibrium moisture content of water absorption

process (% d.b.),D = Moisture diffusivity, m2.s-1, Ai = Constant for a given solid shape, andt = Soaking time, min

Shape of paddy grain was considered as an infinite cylinder (Thakur and Gupta, 2006). Hence, for paddy,

224l

Ai

i λ=

…(2)

Where, ‘l’ is radius of infinite cylinder and λi is geometrical shape factor.

The infinite series of the right hand side of Eq. (1) converges to the first term when the Fourier number (F0 = Dt/l2) becomes large (Tagawa et al., 2003) and thus Eq. (1) may be written as,

)(–exp)–(– 2110 tDAmmmm ee λ= …(3)

In Eq. (3), Dλ12 = –k can be considered as the coefficient

of water diffusion rate. Values of A1 and k were evaluated by applying non-linear regression procedure (SPSS 16.0) to Eq. (3).

Using different values of D, activation energy was calculated with Arrhenius–type equation (Addo and Bart-Plange, 2009) as following:

=

TREDD –exp0

…(4)

Where,D0 = Constant, m2.s-1,

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E = Activation energy for water absorption, kJ.mol-1,R = Universal gas constant, 8.314 J.mol-1.K-1, andT = Absolute temperature of soaking water, K.

Modelling of Water Absorption Process Water absorption process was modelled using Page as well as neural network models. The adequacy of the models was determined by coefficient of determination (R2), mean square error (MSE) and mean relative deviation modulus (%P) (Kashaninejad et al., 2007). Model with highest R2, lowest MSE or P value was inferred as the best to explain the water absorption behaviour.

Page model (Eq. 5) was fitted to the experimental data in the temperature range of 40 to 80oC. For this, the MR was determined using Eq.(6). First, the Page model was fitted to the data at individual temperature and then within a range of temperatures (referred as Generalized Page model (GP model) as named by Kashaninejad et al., 2009).

…(5)

Where,

…(6)

Relative deviation modulus (P) was calculated using Eq.(7).

…(7)

Parameters associated with Page model were estimated using non-linear regression procedure from the experimental data using SAS software.

Artificial neural networkArtificial neural network (ANN) provides a general and flexible framework for representing non-linear functional mappings between a set of input and output variables. A typical neural network architecture consists of an input layer that accepts external information, one or more hidden or

middle layer that provide non-linearity to the model and an output layer that provides the target value (Movagharnejad and Nikzad, 2007; Lertworasirikul and Saetan, 2010; Gulsah and Cengiz, 2011). Each layer contains one or more nodes. The input signal propagates through the network in a forward direction. This ensures that the network outputs can be calculated as explicit functions of the inputs and the weights. These neural networks are commonly referred to as multilayer perceptron (MLP). In the present study, a fully connected MLP neural network was used to model water absorption characteristics of paddy grains. The fully connected MLP neural network (Fig. 1) is one of the most popular neural networks in engineering problems. There exists a powerful and computationally efficient method known as the error back-propagation algorithm for such neural network.

Designing of multilayer perceptron neural networkThe MLP model was designed using neural network toolbox in MATLAB version 7.10. The neural network structure for a particular problem includes determination of number of layers and total number of nodes in each layer. This is usually determined through experimentation of the given data, as there is no theoretical basis for determining these parameters. It has been proved that neural networks with one hidden layer can approximate any non-linear function if given sufficient number of nodes at hidden layer and adequate data points for training. However, in few cases some advantage may be gained using two hidden layers (Lertworasirikul and Saetan, 2010). In this study, neural network with one and two hidden layers were tried. As there were two input variables (water temperature, time) and one output variable (moisture ratio), networks with two neurons in input layer and one neuron in output layer were designed. Water temperature ranged from 40 to 80oC, whereas time ranged from 15 to 960 min. Fig.1 shows the considered neural network topology and input and output parameters. The number of hidden units varied from 1 to 10. For p input nodes, q hidden nodes, one output node and biases at both hidden and

Fig.1: Multilayer perceptron neural network

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output layer, the total number of parameters (weights) in a MLP neural network with one hidden layer is q(p + 2) + 1. This clearly indicated that network with too many hidden nodes will over fit the data.

In order to build an appropriate neural network model for prediction of moisture ratio, the experimental data were divided into three parts namely training, validation and testing set. The training set is presented for parameter estimation, validation set is used for model selection and testing set provides output of the sample performance. In this study, a total of 108 data points were obtained for seven different water temperature of 40, 50, 60, 65, 70, 75 and 80oC.

First, the data set was randomly partitioned such that 81 observations were used for the training and validation process, and the rest 27 observations were used for testing.There are many variations of the back-propagation algorithm used for training feed-forward networks. In this study, the Levenberg-Marquardt algorithm (Trainlm) designed to approach second-order training speed without computing the Hessian matrix was employed. Trainlm function of MATLAB is a network training function that updates weight and bias states according to Levenberg–Marquardt optimization. Two sigmoid transfer functions namely logistic and tanh, were used individually in the hidden layer of the network and a linear transfer function was used in the output layer. Tansig and Logsig functions of MATLAB represent logistic and tanh transfer function while Purelin provides linear transfer function. These functions are given by:

xx

xx

eeeex −

−

=––)(tanh

…(8)

)1(1)(logistic xe

x −+=

…(9)

xx =)(purelin …(10)

A total of 40 neural network models were tried before arriving at the final structure of the model. Tanh transfer function provided faster convergence of training algorithms than logistic function. Network structures with two hidden layers do not improve the efficiency of the model. In order to eliminate the effect of initial value and to increase the possibility of obtaining the true global minima, each network were trained 20 times using different initial

weights to select the best model for prediction using the given data set.

rEsults And disCussion

Water Absorption Characteristics Water absorption rate curves of PB1121 are presented in Figs.2 and 3, which illustrate the temperature dependence of the process. It is evident from the figures that higher the temperature, higher was the water absorption rate (WAR). This increase in WAR might be due to decrease in grain diffusion resistance. At higher temperatures, grains got expanded and softened which caused increased WAR. Temperature induced softening has also been reported earlier for paddy and milled rice by Miah et al.(2002) and Kashaninejad et al.(2007).

It was noticed from Figs. 2 and 3 that at temperatures ≥70°C, water absorption occurred in 3 distinct steps, whereas at <70°C, it occured in 2 steps only. The time, moisture content and average absorption rate in each step are given in Table 1. WAR was at its peak at the onset of the process and decreased sharply thereby ending the first step. During the second step, it remained almost constant. During the third step, WAR increased again and thereafter decreased gradually. Increase in WAR during third step might be due to the partial gelatinization of starch which generally takes place during soaking at or above gelatinization temperature. It was also observed that average WAR during each step was temperature dependent (Table 1). Hence, it was inferred that water temperature affected the WAR, and increase in the former led to the increase in the later, which in turn led to decrease in soaking time.

Fig.2: WAr versus time of Pb1121 at varying temperatures

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Critical moisture content (CMC) is an important parameter to understand for termination of soaking during parboiling operation, which is aimed to increase the moisture content of paddy. However, gelatinization is not desired during the process. It was calculated as the moisture content at which second rise in rate curve was noticed (Araullo et al., 1976). The CMC was, therefore, calculated using Fig.2 and was found to be 43.39% (d.b.) at 70°C, 41.50% (db) at 75°C and 40.11% (db) at 80°C. Thus, the average CMC was calculated to be 41.67% (d.b.) and was inferred to be sufficient moisture content for gelatinization of starch.

Equilibrium moisture content of PB1121 was also calculated at different water temperatures, and is shown in Table 2.

Fig.3: WAr versus time of Pb1121 at temperatures below 65°C

Table 1. Time elapsed, moisture content achieved and average water absorption rate at each step during hydration of Pb1121 at different temperatures

Water temperature

(oC)

step i step ii step iii

time (h)

MC(% d.b.)

Avg WAr (g/h/100g of

dM)time (h)

MC(% d.b.)


dM)time (h)

MC(% d.b.)


dM)

40 0.75 24.17 14.76 15.50 44.55 1.32 - - -

50 0.75 25.25 15.76 12.00 46.41 1.87 - - -

60 0.75 25.95 15.65 9.50 45.57 1.98 - - -

65 0.38 24.09 27.77 8.25 47.54 2.97 - - -

70 0.38 24.37 33.61 3.88 43.4 4.63 4.63 47.58 6.50

75 0.38 24.5 35.70 2.63 41.51 6.61 3.38 48.05 9.14

80 0.38 24.77 35.92 1.63 40.11 10.58 2.13 47.81 15.43

Table 2. Equilibrium moisture content of PB1121 soaked at different water temperatures

Water temperature(oC)

Equilibrium moisture content(% d.b.)

40 48.24

50 48.40

60 48.55

65 48.70

70 50.00

75 52.50

80 55.00

Moisture diffusivity and activation energyIt was observed that moisture diffusivity was temperature dependent, and increase in water temperature resulted in corresponding increase in moisture diffusivity (Table 3). Its value varied from 1.51x10-10 to 1.07x10-9 m2.s-1, with an average value of 5.06x10-10 m2.s-1 at temperature range of 40 to 80 oC and initial moisture content of 10.90 % (d.b.). Mohanty et al.(2002) reported the average moisture diffusivity of paddy (var. Parijata) as 3.48x10-12 m2.s-1 for a temperature range of 40 to 60 oC and at an initial moisture content of 20.2% (d.b.), whereas Thakur and Gupta (2006) reported its value as 4.91x10-11 m2.s-1 for paddy (var. PR116) in the temperature range of 30 to 60 oC and initial moisture content of 15.74-16.28% (d.b.).

Increase in moisture diffusivity with increase in water temperature might be due to decrease in the grain diffusion resistance at higher water temperatures caused due to

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softening of the grain (Miah et al., 2002; Kashaninejad et al., 2007). Higher value of moisture diffusivity of PB1121 as compared to other reported results might be due to lower initial moisture content of the paddy used in this study. Zhang et al. (1984) reported that the average diffusivity of milled rice decreased from 1.78 x10-10 to 8.33 x10-11 m2.s-1 when initial moisture content increased from 13% to 50% (d.b.) during hydration of milled rice.

Activation energy for water diffusion of PB1121 was found to be 53.85 kJ.mol-1. Its value for paddy (var. PR116) in the temperature range of 30 to 60 oC was reported as 31.50 kJ.mol-1 (Thakur and Gupta, 2006). Sridhar and Manohar (2003) reported its value as 19.49 to 23.09 kJ.mol-1 for paddy (IR20) in the temperature range of 25 to 75oC, whereas Verma and Prasad (1999) reported the activation energy of maize as 28.38 kJ.mol-1.

Modelling of Water Absorption Process of Pb1121The water absorption process was modelled by fitting the

experimental data in Page model as well as MLP neural network model. A comparative study was performed between a Page model and MLP neural network to estimate their efficacy for prediction of moisture ratio in the water absorption process.

Table 4 shows the parameters of Page model fitted to the experimental data of PB1121. It was inferred from high values of R2 (>0.9) that the Page model gave a good fit at individual temperatures. However, when the model was applied in the temperature range of 40-80°C (referred as GP model), the value of R2 reduced to 0.65 indicating that only 65% of the variability in water absorption process could be predicted. MSE and P values increased with increase in the temperatures, except at 80oC. These results are in good agreement with Kashaninejad et al. (2007) and Kashaninejad et al. (2009). Lower values of MSE and P indicate better performance of the model. Hence, it is clear from Table 4 that Page model gave better performance at lower water temperatures, and as temperature increased

Table 3. Moisture diffusivity during water absorption of PB1121 at different temperatures

Water temperature(oC)

AK

(h-1)d

(m2.s-1)Average d

(m2.s-1)

40 0.8285 0.0017 1.51x10-10

5.06x10-10

50 0.8362 0.0031 2.75x10-10

60 0.8375 0.0039 3.37x10-10

65 0.8067 0.0044 3.68x10-10

70 0.8301 0.0069 5.95x10-10

75 0.8554 0.0084 7.43x10-10

80 0.9134 0.0114 1.07x10-9

Table 4. Parameters of Page model along with its performance indices

temperature(°C)

k n r2 MsEP

(%)

40 0.026 0.59 0.91 0.0051 22.21

50 0.035 0.62 0.97 0.0020 24.75

60 0.040 0.61 0.98 0.0011 14.84

65 0.048 0.60 0.96 0.0029 40.67

70 0.052 0.63 0.95 0.0028 27.80

75 0.058 0.63 0.96 0.0022 21.00

80 0.046 0.71 0.97 0.0035 15.74

GP model 0.116 0.43 0.65 0.0018 48.02

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the performance decreased. R2 of GP model (for the range 40 to 80oC) was 0.65 (Fig.4) and P and MSE values were found as 48.02% and 0.0018, respectively. The residual plot (Fig. 5) did not show randomness, indicating not a good fit.

The Page model parameters (k and n) for each water absorption test were calculated using non-linear regression technique, Table 4. A regular increase in k (indicator of WAR) was observed as temperature increased from 40 to 75oC. However, the k value decreased at 80oC. It might be because of softening of grain at this temperature and partial gelatinization of starch (Maskan, 2002; Kashaninejad et al., 2009).

Water absorption behaviour was also modelled using MLP neural network. Neural network model with two input nodes, one hidden layer with four hidden nodes and tanh as a transfer function provided the best test result on the

Fig. 4: Comparison between predicted and measured Mr values by GP model

Fig. 5: Plot of residuals for GP model

basis of performance indices (R2, MSE and P). Performance indices of MLP neural network model which gave best fit are presented in Table 5. MLP neural network model showed better performance in predicting the MR than the GP model. MLP neural network model had R2 as 0.99 and MSE as 0.0013 as compared to GP model (R2 0.65 and MSE 0.0018),

Table 5. Performance indices of MLP neural network model

data set r2 MsE P (%)

Complete set 0.99 0.0013 14.69Training set 0.98 0.0012 14.65

Validation set 0.97 0.0013 17.08Testing set 0.96 0.0016 12.39

Fig.6: Comparison between observed and predicted values of moisture ratio for training dataset

Fig.7: Comparison between observed and predicted values of moisture ratio for validation dataset

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Fig.12: Plot of residual for testing data set of MlP neural network model

Fig.8: Comparison between observed and predicted values of moisture ratio for test dataset

Fig.10: Plot of residuals for training dataset of MlP neural network model

Fig.13: Plot of residual for complete dataset of MlP neural network model

Fig.9: Comparison between observed and predicted values of moisture ratio for complete dataset

Fig.11: Plot of residual for validation dataset of MlP neural network model

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Table 6. This meant that the neural network based model was able to explain the 99% variability of the experimental data as compared 65% by the GP model. This clearly exhibits a nonlinear relationship between moisture ratio with water temperature and time. Kashaninejad et al. (2009) also reported similar results relating to water absorption of wheat kernels. The prediction performance of MLP neural network for training, validation, test and complete data set (for a range of 40 to 80oC) is shown in Figs. 6, 7, 8 and 9. Figures indicate very good agreement between predicted and observed moisture ratios for all data sets. High accuracy of all data sets indicated better generalization ability of the neural network based model. The plots of residuals (Figs. 10-13) obtained by the selected neural network model were randomly distributed, which meant that all the systematic pattern of the experimental data has been modelled using neural network model.

Table 6. Performance indices of GP model and MLP neural network model

Model r2 MsE P(%)G P model 0.65 0.0018 48.02

MLP model 0.99 0.0013 14.69

ConClusions

Paddy (PB1121) was evaluated for its water absorption characteristics over a temperature range of 40-80°C. Water absorption process at ≥70°C temperatures occurred in 3 distinct steps, while below 70°C only two steps were observed. Average critical moisture content, a useful parameter for soaking during parboiling of the grain, was found to be 41.67% (d.b.). WAR curves indicated that diffusivity of water into the grain during water absorption increased with increase in water temperature. The average moisture diffusivity was determined to be 5.06x10-10 m2.s-1 and activation energy as 53.85 kJ.mol-1. At lower water temperatures, Page model provided better fit for predicting moisture ratio, but the adequacy of model decreased with increasing temperature. However, Generalized Page model gave a poor fit (R2=0.65 and MSE= 0.0018) for predicting moisture ratio. MLP neural network model with two input nodes, one hidden layer with four hidden nodes and tanh as a transfer function provided high prediction performance with coefficient of determination as 0.99 and MSE 0.0013. High value of R2 underscored a nonlinear relationship between moisture ratio with water temperature and soaking time.

ACKnoWlEdGEMEnt

The authors duly acknowledge the financial support and

facilities provided by the PG School, Indian Agricultural Research Institute, New Delhi and the Council of Scientific and Industrial Research, New Delhi during the course of the study.

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Evaluation and Modelling of Water Absorption Characteristics of Paddy

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