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Prediction of oil yield from groundnut kernels in an hydraulic press using artificial neural network (ANN)
Journal of Food Engineering 81 (2007) 643–646 www.elsevier.com/locate/jfoodeng
Prediction of oil yield from groundnut kernels in an hydraulic press using artificial neural network (ANN) J.O. Olajide *, J.C. Igbeka 1, T.J. Afolabi 2, O.A. Emiola Department of Food Science and Engineering, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Nigeria Received 11 April 2005; accepted 13 June 2006 Available online 23 February 2007
Abstract Prediction of oil yield from groundnut kernels in an hydraulic press subject to process variables such as moisture content, pressing time, applied pressure, heating time and heating temperature was investigated. The technique of artificial neural network (ANN) was applied using experimental data from a previous study. These data were then used for network training and testing. The back propagation technique was then used for establishing the network. The prediction accuracy of the neural network model was significantly improved compared to statistical model, (R = 0.99). Ó 2006 Published by Elsevier Ltd. Keywords: Oil expression; Yield; Neural network; Prediction
1. Introduction Groundnut also known as peanuts, or earthnuts provides food for humans, and livestock and in the absence of meat forms a valuable dietary protein. Until 1974, Nigeria was the world’s leading exporter of groundnut with domestic/local consumption estimated at about 200,000 tons annually (Muller, 1988). The nuts are harvested and crushed to remove the kernels. Some of the kernels are consumed in the roasted form but a large percentage is used for the production of vegetable oil. The oil is the sixth most important vegetable oil worldwide in terms of metric tons produced. In 1997, Africa alone produced 1,117,756 tons of oil representing about a quarter of the world’s total production (Spore, 1998). The production of groundnut oil and its by-products, raw and fried cake, is an important
*
Corresponding author. Tel.: +234 38 710714. E-mail address: [email protected] (J.O. Olajide). 1 Department of Agricultural Engineering, University of Ibadan, Nigeria. 2 Department of Chemical Engineering, Ladoke Akintola University of Technology, Nigeria. 0260-8774/$ - see front matter Ó 2006 Published by Elsevier Ltd. doi:10.1016/j.jfoodeng.2006.06.007
source of income for women in African countries. The oil is used in the manufacture of salad and further processed as an ingredient for margarine production (Weimer & Altes, 1987). Mechanical expression using hydraulic presses is one of the ways by which oil is removed from oilseeds by the use of presses (Khan & Hannah, 1984). This method is generally preferred because of its lower initial and operational costs. It produces relatively uncontaminated oil as compared to the solvent extraction process and it allows the use of the cake residue. Investigation by previous researchers have shown that oil yield during this process is dependent primarily on process variables such as moisture content, particle size, heating temperature, heating time, applied pressure and pressing time (Khan & Hannah, 1981; Khan & Hannah, 1984; Mrema & Mc Nulty, 1985). Many researches have been carried out to predict oil yield in terms of process parameters using empirical equations/models developed by statistical methods. The oil yield from sun flower, conorphor nuts, peanut, rice bran and shea nut have been predicted using such empirical equations (Adeeko & Ajibola, 1990; Fasina & Ajibola, 1990; Olajide, 2000; Singh, Farsaie, Stewart, & Douglass,
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J.O. Olajide et al. / Journal of Food Engineering 81 (2007) 643–646
1984; Sivakumaran, Goodrum, & Bradley, 1985; Sivala, Bhole, & Mukherjee, 1991). Hamzat and Clarke (1993) also used the concept of Quasi Equilibrium oil yield to predict the oil yield from groundnuts. The empirical equations gave an insight into the influence of some of the parameters on the oil yield from these seeds. However, the prediction power of these models are limited. Neural network is a new class of information processing techniques. The most basic components of neural networks are modeled after the structure of the human brain like human information processing systems artificial neural systems or networks acquire, store and utilize knowledge. It has been applied in solving wide varieties of problems. The most common is the use of neural network to forecast what will most likely happen. It has a unique ability to recognize relationship before input and output events. Numerous researches have applied neural networks in the modeling of various systems in which no explicit scientific solutions were available. Zhang, Mesirov, and Waltz (1992) used a neural network model for prediction of the secondary structure of globular proteins. Based on the knowledge of the secondary structure of the existing proteins, the model predicts the secondary structure of local sequences of amino acids with a success rate of 64.3%. Sayeed, Whittaker, and Kehtarnavaz (1995) developed a back propagation neural network to predict the sensory attributes of a snack food. The performance of the trained neural network was reasonable as indicated by correct prediction rates ranging from 78% to 98%. Ruan, Almaer, and Zhang (1995) designed a neural network that accurately predicted (>94%) the rheological properties of dough from the torque developed during mixing. They reported that the neural network could be used on line for process control. Liao, Paulsen, and Reid (1993) used a neural network to classify corn kernel breakage. The neural network model accurately discriminated the broken kernels from the whole corn kernels. Fang, Biby, Haque, Hannah, and Spillman (1998) developed back propagation neural networks for the prediction of ground wheet samples. Compared to conventional statistical models the accuracy of prediction was improved substantially as indicated by the significant reduction in not mean square error and the improvement of coefficient of determination (r2 > 0.98). Ni and Gunasekaran (1998) developed neural network models to predict rheological properties of swiss cheese. The neural network model developed could better predict the properties than the pre-
viously multiple regression model. Considering a number of input parameters that influence the oil yield from groundnut and the perceived non-linear nature of their relationship this study is aimed at evaluating the suitability of Artificial Neural Networks (ANN) in predicting the oil yield from groundnut kernels. In achieving the set goal of the study, an artificial neural Network (ANN) was trained and validated. A total of 96 data sets obtained from the experimental work of Olajide (2000) were used in this study. About 60 sets of these data sets we assigned the training set while the remaining 36 sets were used as the validation sets. There are five input variables, which are: applied pressure (X1), pressing time (X2), moisture content (X3), heating temperature (X4) and heating time (X5). The desired output is the oil yield from the groundnut. The statistical description of the data set is given in Table 1. The ANN was trained using standard back propagation architecture with Levenberg–Marquadart training algorithm and this architecture used comprised of two layers. Table 2 shows the architecture of the network used. The tansigmod function was used as the transfer function in the hidden layer due to its suitable application for the data set of this kind. The output layer was made up of pure linear transfer function. The optimal hidden layer was determined by varying the total number of neurons from 1 to 20. The stop criteria were based on mean square error (MSE) on the validation set for model generalization. The optimum hidden layer comprised of 17 neurons. All these were executed on a commercial simulator MATLAB6R12. The algorithm model for groundnut oil yield prediction is as shown below. Incremental training style is used where the weights and biases of the network are updated each time an input is presented to the network. Its training function is trainbr. The performance function is set to the learning function used which is invoked by setting the learning parameter (Ir) Table 2 Architecture of the network used Network used
Levenberg–Marquadart back propagation neural network
Leaning Transfer function No of input Hidden layer No of output
Supervised Sigmoid, punch 5 2 1
Table 1 Statistical description of the variables Variables
Number of variables
Average value
Minimum value
Maximum value
Standard deviation
Applied pressure, X1 (MPa) Pressing time, X2 (min) Moisture content, X3 (%) Heating temperature, X4 (°C) Heating time, X5 (min) Oil yield, Y (%)
96 96 96 96 96 96
15.00 5 8.6 88.0 40 25.46
5 3 4.6 65 20 11.18
25 7 12.6 10.5 60 33.36
4.40 0.88 1.76 8.80 8.80 5.01
J.O. Olajide et al. / Journal of Food Engineering 81 (2007) 643–646
Exp. ANN
32
28 Yield (%)
net = newff([4.6 12.6;6.5 10.5;3 5;1 2;4 6],[17 1], {‘tansig’,‘purelin’},‘trainlm’); net.trainParam.epochs = 100; net.trainParam.goal = 0; net.trainParam.lr = 0.1 net.trainParam.show = 25; net = train(net,p,t); [pn,minp,maxp,tn,mint,maxt] = premnmx(p,t); a = sim(net,p) where p = supplied inputs t = supplied targets/outputs a = network outputs
645
24
20
16 0
2. Results and discussion
Table 3 Average ANN training outputs and experimental training outputs for groundnut oil yield S.No.
Experimental yield (%)
ANN output (%)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
27.59 24.88 31.04 25.45 23.29 26.99 27.11 25.98 27.06 25.44 33.36 30.53 24.28 25.38 31.38 17.56 16.28 16.96 25.83 28.96
27.59 25.15 31.00 25.22 23.29 26.99 27.11 25.98 27.02 25.44 33.40 30.96 24.28 25.38 30.95 17.56 16.28 16.96 25.83 28.97
8 12 Experimental number
16
20
Fig. 1. Groundnut oil yield vs. experimental number (testing).
Table 4 Average ANN testing outputs and expected experimental outputs for groundnut oil yield S.No.
Expected experimental yield (%)
ANN output
21 22 23 24 25 26 27 28 29 30 31 32
20.46 26.87 11.18 16.64 26.58 26.02 28.63 28.73 28.56 28.80 28.51 28.63
20.49 27.01 11.33 16.70 26.63 25.97 28.64 28.64 28.64 28.64 28.64 28.64
ANN outputs was 5.50 and 25.00 respectively. The closeness of both values confirms the degree of reliability of the network. Fig. 2 represents a graphical representation of the experimental oil yield to validate the network and the corre-
28 Exp. ANN.
24
Yield (%)
The values of the experimental oil yield used to train the network and the corresponding output given by the ANN is as shown in Table 3. The standard deviation and mean values of 4–49 and 25–77 was calculated for the experimental yield while the corresponding ANN outputs was 4–48 and 25.77 respectively. The closeness of both values confirms the degree of reliability of the network. Fig. 1 represent a graphical representation of the experimental oil yield to train the network and the corresponding yield given by the ANN. The closeness of the points on the plot further confirms the reliability of the network in predicting oil yields from groundnuts kernels provided the five inputs are supplied accounting. The values of the experimental oil yield used to validate the network and the corresponding output given by the ANN is as shown in Table 4. The standard deviation and mean values of 5.54 and 24.97 was calculated for the experimental yield while the corresponding
4
20
16
12 20
24 28 Experimental number
Fig. 2. Groundnut oil yield vs. experimental number (validation).
32
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J.O. Olajide et al. / Journal of Food Engineering 81 (2007) 643–646
Table 5 Results of oil yield prediction by statistical method: an ANN model Model
Training dataset
Validation dataset
MSE
Correlation index
MSE
Correlation index
Statistical ANN
0.0054 0.0007
0.8040 0.9920
0.0056 0.0008
0.8220 0.9940
sponding yield given by the ANN. The closeness of the points on the plot further confirms the reliability of the network in predicting oil yields from groundnuts kernels provided the five inputs are supplied accounting. From Table 5 mean square error (MSE) of 0.054 for training data set and 0.0056 for validation dataset were obtained for the statistical method composed to the corresponding values of 0.0007 and 0.0008 for the ANN. The correlation coefficient of 0.80 and 0.82 were obtained for the training and validation data sets using the statistical method as compared to corresponding 0.992 and 0.994 for the ANN. The correlation coefficient of 0.99 followed the same trend reported by the works of Sayeed et al. (1995), Fang et al. (1998) and Ruan et al. (1995). 3. Conclusion Back propagation neural network model was designed, trained and validated for the prediction of oil yield from groundnut kernels. The network had five input variables. The network performed well during validation. The accuracy of prediction was significantly improved compared to statistical models. The network model had R ;= 0.99 which showed that the neural network model was capable of learning the relationships among the input and output variables for given data set. References Adeeko, K. A., & Ajibola, O. O. (1990). Processing factors affecting yield & quality of mechanically expressed groundnut oil. Journal of Agricultural Engineering Research, 45, 31–43, Development of an experimental roller mill with computerized data acquisition..
Fang, Q., Biby, G., Haque, E., Hannah, M. A., & Spillman, C. K. (1998). Neural network modeling of physical properties of ground wheat. Cereal Chemistry, 75(2), 251–253. Fasina, O. O., & Ajibola, O. O. (1990). Development of equations for the yield of oil expressed from conophor nuts. Journal of Agricultural Engineering Research, 46, 45–53. Hamzat, K. O., & Clarke, B. (1993). Prediction of oil yield from groundnuts using the concept of quasi-equilibrium oil yield. Journal of Agricultural Engineering Research, 55, 57–79. Khan, L. M., & Hannah, M. A. (1981). Expression of oil from oil seeds. ASAE Paper No. MCR-81-509. St. Joseph, MI: ASAE. Khan, L. M., & Hannah, M. A. (1984). Expression of soybean oil. Transactions of the ASAE, 27(1), 190–194. Liao, K., Paulsen, M. R., & Reid, J. F. (1993). Corn kernel breakage classification by machine vision using a neural network classifier. Transactions of ASAE, 36(6), 1511–1517. Mrema, G. C., & Mc Nulty, P. B. (1985). Mechanisms of mechanical oil expression from oil seeds. Journal of Agricultural Engineering Research, 31, 361–370. Muller, H. G. (1988). An introduction to tropical food science (pp. 116– 118). Cambridge, England: Cambridge University Press. Ni, H., & Gunasekaran, S. (1998). Food quality prediction with neural networks. Food Technology, 52(10), 60–65. Olajide, J. O. (2000). Process optimization and modeling of oil expression from sheanut and groundnut kernels. An unpublished PhD. Thesis. Department of Agric. Engineering, University of Ibadan, Ibadan Nigeria. Ruan, R., Almaer, S., & Zhang, J. (1995). Prediction of dough rheological properties using neural networks. Cereal Chemistry, 72(3), 308–311. Sayeed, M. S., Whittaker, A. D., & Kehtarnavaz, N. D. (1995). Snacks quality evaluation method based on image features and neural network prediction. Transactions of the ASAE, 38(4), 1239–1245. Singh, M. S., Farsaie, A., Stewart, L. E., & Douglass, L. W. (1984). Development of mathematical models to predict sunflower oil expression. Transactions of the ASAE, 27(1), 1190–1194. Sivakumaran, K., Goodrum, J. W., & Bradley, A. R. (1985). Expeller optimization of peanut oil production. Transactions of the ASAE, 28(1), 316–320. Sivala, K., Bhole, N. G., & Mukherjee, R. K. (1991). Effects of moisture on rice bran oil expression. Journal of Agricultural Engineering Research, 50, 81–91. Spore. (1998). Vegetable oils as a slippery slope to success. A publication of technical centre for agriculture and rural cooperation. Wageningen, Netherlands, No. 73. Weimer, H., & Altes, F. W. (1987). Small scale processing of oil fruits and oilseeds. A publication of German Appropriate Technology Exchange (GATE) Eschborn. Federal Republic of Germany. Zhang, X. M., Mesirov, J. P., & Waltz, D. L. (1992). Hybrid system for protein secondary structure prediction. Journal of Molecular Biology, 225, 1040–1063.
Prediction of oil yield from groundnut kernels in an hydraulic press using artificial neural network (ANN) J.O. Olajide *, J.C. Igbeka 1, T.J. Afolabi 2, O.A. Emiola Department of Food Science and Engineering, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Nigeria Received 11 April 2005; accepted 13 June 2006 Available online 23 February 2007
Abstract Prediction of oil yield from groundnut kernels in an hydraulic press subject to process variables such as moisture content, pressing time, applied pressure, heating time and heating temperature was investigated. The technique of artificial neural network (ANN) was applied using experimental data from a previous study. These data were then used for network training and testing. The back propagation technique was then used for establishing the network. The prediction accuracy of the neural network model was significantly improved compared to statistical model, (R = 0.99). Ó 2006 Published by Elsevier Ltd. Keywords: Oil expression; Yield; Neural network; Prediction
1. Introduction Groundnut also known as peanuts, or earthnuts provides food for humans, and livestock and in the absence of meat forms a valuable dietary protein. Until 1974, Nigeria was the world’s leading exporter of groundnut with domestic/local consumption estimated at about 200,000 tons annually (Muller, 1988). The nuts are harvested and crushed to remove the kernels. Some of the kernels are consumed in the roasted form but a large percentage is used for the production of vegetable oil. The oil is the sixth most important vegetable oil worldwide in terms of metric tons produced. In 1997, Africa alone produced 1,117,756 tons of oil representing about a quarter of the world’s total production (Spore, 1998). The production of groundnut oil and its by-products, raw and fried cake, is an important
*
Corresponding author. Tel.: +234 38 710714. E-mail address: [email protected] (J.O. Olajide). 1 Department of Agricultural Engineering, University of Ibadan, Nigeria. 2 Department of Chemical Engineering, Ladoke Akintola University of Technology, Nigeria. 0260-8774/$ - see front matter Ó 2006 Published by Elsevier Ltd. doi:10.1016/j.jfoodeng.2006.06.007
source of income for women in African countries. The oil is used in the manufacture of salad and further processed as an ingredient for margarine production (Weimer & Altes, 1987). Mechanical expression using hydraulic presses is one of the ways by which oil is removed from oilseeds by the use of presses (Khan & Hannah, 1984). This method is generally preferred because of its lower initial and operational costs. It produces relatively uncontaminated oil as compared to the solvent extraction process and it allows the use of the cake residue. Investigation by previous researchers have shown that oil yield during this process is dependent primarily on process variables such as moisture content, particle size, heating temperature, heating time, applied pressure and pressing time (Khan & Hannah, 1981; Khan & Hannah, 1984; Mrema & Mc Nulty, 1985). Many researches have been carried out to predict oil yield in terms of process parameters using empirical equations/models developed by statistical methods. The oil yield from sun flower, conorphor nuts, peanut, rice bran and shea nut have been predicted using such empirical equations (Adeeko & Ajibola, 1990; Fasina & Ajibola, 1990; Olajide, 2000; Singh, Farsaie, Stewart, & Douglass,
644
J.O. Olajide et al. / Journal of Food Engineering 81 (2007) 643–646
1984; Sivakumaran, Goodrum, & Bradley, 1985; Sivala, Bhole, & Mukherjee, 1991). Hamzat and Clarke (1993) also used the concept of Quasi Equilibrium oil yield to predict the oil yield from groundnuts. The empirical equations gave an insight into the influence of some of the parameters on the oil yield from these seeds. However, the prediction power of these models are limited. Neural network is a new class of information processing techniques. The most basic components of neural networks are modeled after the structure of the human brain like human information processing systems artificial neural systems or networks acquire, store and utilize knowledge. It has been applied in solving wide varieties of problems. The most common is the use of neural network to forecast what will most likely happen. It has a unique ability to recognize relationship before input and output events. Numerous researches have applied neural networks in the modeling of various systems in which no explicit scientific solutions were available. Zhang, Mesirov, and Waltz (1992) used a neural network model for prediction of the secondary structure of globular proteins. Based on the knowledge of the secondary structure of the existing proteins, the model predicts the secondary structure of local sequences of amino acids with a success rate of 64.3%. Sayeed, Whittaker, and Kehtarnavaz (1995) developed a back propagation neural network to predict the sensory attributes of a snack food. The performance of the trained neural network was reasonable as indicated by correct prediction rates ranging from 78% to 98%. Ruan, Almaer, and Zhang (1995) designed a neural network that accurately predicted (>94%) the rheological properties of dough from the torque developed during mixing. They reported that the neural network could be used on line for process control. Liao, Paulsen, and Reid (1993) used a neural network to classify corn kernel breakage. The neural network model accurately discriminated the broken kernels from the whole corn kernels. Fang, Biby, Haque, Hannah, and Spillman (1998) developed back propagation neural networks for the prediction of ground wheet samples. Compared to conventional statistical models the accuracy of prediction was improved substantially as indicated by the significant reduction in not mean square error and the improvement of coefficient of determination (r2 > 0.98). Ni and Gunasekaran (1998) developed neural network models to predict rheological properties of swiss cheese. The neural network model developed could better predict the properties than the pre-
viously multiple regression model. Considering a number of input parameters that influence the oil yield from groundnut and the perceived non-linear nature of their relationship this study is aimed at evaluating the suitability of Artificial Neural Networks (ANN) in predicting the oil yield from groundnut kernels. In achieving the set goal of the study, an artificial neural Network (ANN) was trained and validated. A total of 96 data sets obtained from the experimental work of Olajide (2000) were used in this study. About 60 sets of these data sets we assigned the training set while the remaining 36 sets were used as the validation sets. There are five input variables, which are: applied pressure (X1), pressing time (X2), moisture content (X3), heating temperature (X4) and heating time (X5). The desired output is the oil yield from the groundnut. The statistical description of the data set is given in Table 1. The ANN was trained using standard back propagation architecture with Levenberg–Marquadart training algorithm and this architecture used comprised of two layers. Table 2 shows the architecture of the network used. The tansigmod function was used as the transfer function in the hidden layer due to its suitable application for the data set of this kind. The output layer was made up of pure linear transfer function. The optimal hidden layer was determined by varying the total number of neurons from 1 to 20. The stop criteria were based on mean square error (MSE) on the validation set for model generalization. The optimum hidden layer comprised of 17 neurons. All these were executed on a commercial simulator MATLAB6R12. The algorithm model for groundnut oil yield prediction is as shown below. Incremental training style is used where the weights and biases of the network are updated each time an input is presented to the network. Its training function is trainbr. The performance function is set to the learning function used which is invoked by setting the learning parameter (Ir) Table 2 Architecture of the network used Network used
Levenberg–Marquadart back propagation neural network
Leaning Transfer function No of input Hidden layer No of output
Supervised Sigmoid, punch 5 2 1
Table 1 Statistical description of the variables Variables
Number of variables
Average value
Minimum value
Maximum value
Standard deviation
Applied pressure, X1 (MPa) Pressing time, X2 (min) Moisture content, X3 (%) Heating temperature, X4 (°C) Heating time, X5 (min) Oil yield, Y (%)
96 96 96 96 96 96
15.00 5 8.6 88.0 40 25.46
5 3 4.6 65 20 11.18
25 7 12.6 10.5 60 33.36
4.40 0.88 1.76 8.80 8.80 5.01
J.O. Olajide et al. / Journal of Food Engineering 81 (2007) 643–646
Exp. ANN
32
28 Yield (%)
net = newff([4.6 12.6;6.5 10.5;3 5;1 2;4 6],[17 1], {‘tansig’,‘purelin’},‘trainlm’); net.trainParam.epochs = 100; net.trainParam.goal = 0; net.trainParam.lr = 0.1 net.trainParam.show = 25; net = train(net,p,t); [pn,minp,maxp,tn,mint,maxt] = premnmx(p,t); a = sim(net,p) where p = supplied inputs t = supplied targets/outputs a = network outputs
645
24
20
16 0
2. Results and discussion
Table 3 Average ANN training outputs and experimental training outputs for groundnut oil yield S.No.
Experimental yield (%)
ANN output (%)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
27.59 24.88 31.04 25.45 23.29 26.99 27.11 25.98 27.06 25.44 33.36 30.53 24.28 25.38 31.38 17.56 16.28 16.96 25.83 28.96
27.59 25.15 31.00 25.22 23.29 26.99 27.11 25.98 27.02 25.44 33.40 30.96 24.28 25.38 30.95 17.56 16.28 16.96 25.83 28.97
8 12 Experimental number
16
20
Fig. 1. Groundnut oil yield vs. experimental number (testing).
Table 4 Average ANN testing outputs and expected experimental outputs for groundnut oil yield S.No.
Expected experimental yield (%)
ANN output
21 22 23 24 25 26 27 28 29 30 31 32
20.46 26.87 11.18 16.64 26.58 26.02 28.63 28.73 28.56 28.80 28.51 28.63
20.49 27.01 11.33 16.70 26.63 25.97 28.64 28.64 28.64 28.64 28.64 28.64
ANN outputs was 5.50 and 25.00 respectively. The closeness of both values confirms the degree of reliability of the network. Fig. 2 represents a graphical representation of the experimental oil yield to validate the network and the corre-
28 Exp. ANN.
24
Yield (%)
The values of the experimental oil yield used to train the network and the corresponding output given by the ANN is as shown in Table 3. The standard deviation and mean values of 4–49 and 25–77 was calculated for the experimental yield while the corresponding ANN outputs was 4–48 and 25.77 respectively. The closeness of both values confirms the degree of reliability of the network. Fig. 1 represent a graphical representation of the experimental oil yield to train the network and the corresponding yield given by the ANN. The closeness of the points on the plot further confirms the reliability of the network in predicting oil yields from groundnuts kernels provided the five inputs are supplied accounting. The values of the experimental oil yield used to validate the network and the corresponding output given by the ANN is as shown in Table 4. The standard deviation and mean values of 5.54 and 24.97 was calculated for the experimental yield while the corresponding
4
20
16
12 20
24 28 Experimental number
Fig. 2. Groundnut oil yield vs. experimental number (validation).
32
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J.O. Olajide et al. / Journal of Food Engineering 81 (2007) 643–646
Table 5 Results of oil yield prediction by statistical method: an ANN model Model
Training dataset
Validation dataset
MSE
Correlation index
MSE
Correlation index
Statistical ANN
0.0054 0.0007
0.8040 0.9920
0.0056 0.0008
0.8220 0.9940
sponding yield given by the ANN. The closeness of the points on the plot further confirms the reliability of the network in predicting oil yields from groundnuts kernels provided the five inputs are supplied accounting. From Table 5 mean square error (MSE) of 0.054 for training data set and 0.0056 for validation dataset were obtained for the statistical method composed to the corresponding values of 0.0007 and 0.0008 for the ANN. The correlation coefficient of 0.80 and 0.82 were obtained for the training and validation data sets using the statistical method as compared to corresponding 0.992 and 0.994 for the ANN. The correlation coefficient of 0.99 followed the same trend reported by the works of Sayeed et al. (1995), Fang et al. (1998) and Ruan et al. (1995). 3. Conclusion Back propagation neural network model was designed, trained and validated for the prediction of oil yield from groundnut kernels. The network had five input variables. The network performed well during validation. The accuracy of prediction was significantly improved compared to statistical models. The network model had R ;= 0.99 which showed that the neural network model was capable of learning the relationships among the input and output variables for given data set. References Adeeko, K. A., & Ajibola, O. O. (1990). Processing factors affecting yield & quality of mechanically expressed groundnut oil. Journal of Agricultural Engineering Research, 45, 31–43, Development of an experimental roller mill with computerized data acquisition..
Fang, Q., Biby, G., Haque, E., Hannah, M. A., & Spillman, C. K. (1998). Neural network modeling of physical properties of ground wheat. Cereal Chemistry, 75(2), 251–253. Fasina, O. O., & Ajibola, O. O. (1990). Development of equations for the yield of oil expressed from conophor nuts. Journal of Agricultural Engineering Research, 46, 45–53. Hamzat, K. O., & Clarke, B. (1993). Prediction of oil yield from groundnuts using the concept of quasi-equilibrium oil yield. Journal of Agricultural Engineering Research, 55, 57–79. Khan, L. M., & Hannah, M. A. (1981). Expression of oil from oil seeds. ASAE Paper No. MCR-81-509. St. Joseph, MI: ASAE. Khan, L. M., & Hannah, M. A. (1984). Expression of soybean oil. Transactions of the ASAE, 27(1), 190–194. Liao, K., Paulsen, M. R., & Reid, J. F. (1993). Corn kernel breakage classification by machine vision using a neural network classifier. Transactions of ASAE, 36(6), 1511–1517. Mrema, G. C., & Mc Nulty, P. B. (1985). Mechanisms of mechanical oil expression from oil seeds. Journal of Agricultural Engineering Research, 31, 361–370. Muller, H. G. (1988). An introduction to tropical food science (pp. 116– 118). Cambridge, England: Cambridge University Press. Ni, H., & Gunasekaran, S. (1998). Food quality prediction with neural networks. Food Technology, 52(10), 60–65. Olajide, J. O. (2000). Process optimization and modeling of oil expression from sheanut and groundnut kernels. An unpublished PhD. Thesis. Department of Agric. Engineering, University of Ibadan, Ibadan Nigeria. Ruan, R., Almaer, S., & Zhang, J. (1995). Prediction of dough rheological properties using neural networks. Cereal Chemistry, 72(3), 308–311. Sayeed, M. S., Whittaker, A. D., & Kehtarnavaz, N. D. (1995). Snacks quality evaluation method based on image features and neural network prediction. Transactions of the ASAE, 38(4), 1239–1245. Singh, M. S., Farsaie, A., Stewart, L. E., & Douglass, L. W. (1984). Development of mathematical models to predict sunflower oil expression. Transactions of the ASAE, 27(1), 1190–1194. Sivakumaran, K., Goodrum, J. W., & Bradley, A. R. (1985). Expeller optimization of peanut oil production. Transactions of the ASAE, 28(1), 316–320. Sivala, K., Bhole, N. G., & Mukherjee, R. K. (1991). Effects of moisture on rice bran oil expression. Journal of Agricultural Engineering Research, 50, 81–91. Spore. (1998). Vegetable oils as a slippery slope to success. A publication of technical centre for agriculture and rural cooperation. Wageningen, Netherlands, No. 73. Weimer, H., & Altes, F. W. (1987). Small scale processing of oil fruits and oilseeds. A publication of German Appropriate Technology Exchange (GATE) Eschborn. Federal Republic of Germany. Zhang, X. M., Mesirov, J. P., & Waltz, D. L. (1992). Hybrid system for protein secondary structure prediction. Journal of Molecular Biology, 225, 1040–1063.