Advances in Bioscience and Bioengineering

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Thermal Conductivity of Food Products using: A Correlation Analysis Based on Artificial Neural Networks (ANNs)

Received: 26 June 2014    Accepted: 11 July 2014    Published: 30 July 2014
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Abstract

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.

DOI 10.11648/j.abb.20140202.12
Published in Advances in Bioscience and Bioengineering (Volume 2, Issue 2, April 2014)
Page(s) 14-24
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Thermo-Physical Properties of Biological Products, Thermal Conductivity of Bakery Products, Back-Propagation, Artificial Neural Network, Mean Absolute Error, Mean Relative Error, Standard Error

References
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[3] Ajasa, A. A., Akinyemi, L. A., Shoewu, O. O. and Adenowo, A. A. (2014): “Exploitation of Artificial Neural Networks to predict the Thermal Conductivity of Food Products in Nigeria”; American Journal of Engineering Research (AJER); Volume 03,Issue 03, Pgs 22 – 29.
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    Ajasa, Abiodun Afis, Adenowo, Adetokunbo Abayomi, Ogunlewe, et al. (2014). Thermal Conductivity of Food Products using: A Correlation Analysis Based on Artificial Neural Networks (ANNs). Advances in Bioscience and Bioengineering, 2(2), 14-24. https://doi.org/10.11648/j.abb.20140202.12

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    Ajasa; Abiodun Afis; Adenowo; Adetokunbo Abayomi; Ogunlewe, et al. Thermal Conductivity of Food Products using: A Correlation Analysis Based on Artificial Neural Networks (ANNs). Adv. BioSci. Bioeng. 2014, 2(2), 14-24. doi: 10.11648/j.abb.20140202.12

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    AMA Style

    Ajasa, Abiodun Afis, Adenowo, Adetokunbo Abayomi, Ogunlewe, et al. Thermal Conductivity of Food Products using: A Correlation Analysis Based on Artificial Neural Networks (ANNs). Adv BioSci Bioeng. 2014;2(2):14-24. doi: 10.11648/j.abb.20140202.12

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  • @article{10.11648/j.abb.20140202.12,
      author = {Ajasa and Abiodun Afis and Adenowo and Adetokunbo Abayomi and Ogunlewe and Adeyinka Oluremi and Folorunso and Comfort Oluseyi},
      title = {Thermal Conductivity of Food Products using: A Correlation Analysis Based on Artificial Neural Networks (ANNs)},
      journal = {Advances in Bioscience and Bioengineering},
      volume = {2},
      number = {2},
      pages = {14-24},
      doi = {10.11648/j.abb.20140202.12},
      url = {https://doi.org/10.11648/j.abb.20140202.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.abb.20140202.12},
      abstract = {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.},
     year = {2014}
    }
    

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  • TY  - JOUR
    T1  - Thermal Conductivity of Food Products using: A Correlation Analysis Based on Artificial Neural Networks (ANNs)
    AU  - Ajasa
    AU  - Abiodun Afis
    AU  - Adenowo
    AU  - Adetokunbo Abayomi
    AU  - Ogunlewe
    AU  - Adeyinka Oluremi
    AU  - Folorunso
    AU  - Comfort Oluseyi
    Y1  - 2014/07/30
    PY  - 2014
    N1  - https://doi.org/10.11648/j.abb.20140202.12
    DO  - 10.11648/j.abb.20140202.12
    T2  - Advances in Bioscience and Bioengineering
    JF  - Advances in Bioscience and Bioengineering
    JO  - Advances in Bioscience and Bioengineering
    SP  - 14
    EP  - 24
    PB  - Science Publishing Group
    SN  - 2330-4162
    UR  - https://doi.org/10.11648/j.abb.20140202.12
    AB  - 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.
    VL  - 2
    IS  - 2
    ER  - 

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