Thermal Conductivity of Food Products using: A Correlation Analysis Based on Artificial Neural Networks (ANNs)
Advances in Bioscience and Bioengineering
Volume 2, Issue 2, April 2014, Pages: 14-24
Received: Jun. 26, 2014;
Accepted: Jul. 11, 2014;
Published: Jul. 30, 2014
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Ajasa, Abiodun Afis, Department of Electronic and Computer Engineering, Faculty of Engineering, Lagos State University, Epe, Lagos, Nigeria
Adenowo, Adetokunbo Abayomi, Department of Electronic and Computer Engineering, Faculty of Engineering, Lagos State University, Epe, Lagos, Nigeria
Ogunlewe, Adeyinka Oluremi, Department of Electronic and Computer Engineering, Faculty of Engineering, Lagos State University, Epe, Lagos, Nigeria
Folorunso, Comfort Oluseyi, Department of Electronic and Computer Engineering, Faculty of Engineering, Lagos State University, Epe, Lagos, Nigeria
This paper presents the correlation between the predicted and desired/targeted thermal conductivity of food products as a function of moisture content, temperature and apparent density. The food products considered in this work are the bakery products which include bread, bread dough, cake, and whole-wheat dough. Statistical data of results from previous work in existing literatures were used in this work for a wide range of moisture contents, temperatures and apparent densities resulting from baking conditions. The results of this work showed straight line curves when the predicted values of thermal conductivity were plotted against the targeted values of thermal conductivity. This demonstrates correlation between the predicted and targeted thermal conductivities when the points are joined together (best fit-points), hence, a very good agreement between the predicted and the desired values of thermal conductivity. The two ANN models that were finally selected, after several configurations had been considered and evaluated, are the optimal ANN model that was found to be a network with two hidden layers and eight neurons and the simplest ANN model was equally found to be a network with one hidden layer and ten neurons. The estimated errors between the predicted and desired (or targeted) thermal conductivity values of the bakery products for both the optimal ANN and simplest ANN models are the MRE, MAE and SE. Moreover, the results also showed that the optimal ANN model had an MRE of 0.04878%, an MAE of 0.0054W/mK and an SE of 0.0015W/mK while the simplest ANN model was estimated to have an MRE of 0.03388%, an MAE of 0.0034W/mK and an SE of 0.0011W/mK. These errors are approximately equal to zero (i.e., 0 W/mK) and could, therefore, be regarded as a good result for the prediction. Since the simplest ANN model had the least values of all three errors (MRE, MAE and SE) when compared with other configurations, including the optimal ANN model, it is, however, regarded as the best ANN model and is, thus, recommended.
Ajasa, Abiodun Afis,
Adenowo, Adetokunbo Abayomi,
Ogunlewe, Adeyinka Oluremi,
Folorunso, Comfort Oluseyi,
Thermal Conductivity of Food Products using: A Correlation Analysis Based on Artificial Neural Networks (ANNs), Advances in Bioscience and Bioengineering.
Vol. 2, No. 2,
2014, pp. 14-24.
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