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

Ajasa; Abiodun Afis; Adenowo; Adetokunbo Abayomi; Ogunlewe; Adeyinka Oluremi; Folorunso; Comfort Oluseyi

doi:doi:10.11648/j.abb.20140202.12

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

Ajasa, Abiodun Afis, Adenowo, Adetokunbo Abayomi, Ogunlewe, Adeyinka Oluremi, Folorunso, Comfort Oluseyi

Published in Advances in Bioscience and Bioengineering (Volume 2, Issue 2)

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.

Published in	Advances in Bioscience and Bioengineering (Volume 2, Issue 2)
DOI	10.11648/j.abb.20140202.12
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), 2014. 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

[1]	Baik, O. D., Marcotte, M., Sablani, S. S., and Castaigne, F. (2001): “Thermal and physical properties of Bakery products”; Critical Review in Food Science and Nutrition. CRC Press LLC; 2001.
[2]	Baik, O. D., Marcotte, M., Sablani, S. S., and Castaigne, F. (2006): “Modelling the Thermal properties of a Cup cake during Baking”; Journal of Food Science Volume 64, Issue 2, Pgs. 295 – 299; 2006.
[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.
[4]	Shyam Sablani, S and Shafiur Rahman, M. (2003), “Using neural networks to predict thermal conductivity of food as a function of moisture content, temperature and apparent porosity”; Food Research International. Elsevier. Volume 36, Issue 6, 2003, Pgs. 617 – 623; 2003.
[5]	Rask, C. (1989): “Thermal properties of dough and bakery products: A review of published data”; Journal of Food Engineering, 9, Pgs 167 – 193; 1989
[6]	Hussain, A. M. and Rahman, M. S. (1999): “Thermal conductivity prediction of fruits and vegetables using neural networks”; International Journal of Food Properties; 2(2); 1999.
[7]	Ni, H., and Gunasekaran, S. (1998): “Food quality prediction with neural networks”; Food Technology, 52(10), Pgs 60 – 65; 1998.
[8]	Sablani, S. S., Marcotte, M., Baik, O. D., and Castaigne, F. (1998): “Modelling of simultaneous heat and water transport in the baking process”; Lebensmittel Wissenchaft and Technologie, 31, Germany; Pgs 201 – 209; 1998.
[9]	Linko, P., and Zhu. Y. H. (1991): “Neural network programming in bioprocess variable estimation and state prediction”; Journal of Biotechnology, 21(3), Pgs 253–270; 1991.
[10]	Hornik, K., Stinchombe, M. and White, H.: (1989): “Multilayer feed-forward network and universal approximator. Neural Network”, 2, Pgs. 359 – 366; 1989.
[11]	Myhara, R. M., Sablani, S. S., Al-Alawi, S. M., and Taylor, M. S. (1998):”Water sorption isotherms of dates: Modelling using GAB equation and artificial neural network approaches”; Lebensmittel Wissenschaft und Technologies, 31(7/8), Germany; Pgs 699 – 706; 1998.
[12]	Murakami, E. G., and Okos, M. R. (1989): ‘‘Measurement and Prediction of thermal properties of foods’’. In R. P. Singh, and A. G. Medina (Eds.), Food Properties Computer-aided Engineering of Food Processing System; Pgs. 3 – 48; Norwell, MA; Kluwer Academic Publishers, 1989.
[13]	Xie, G. and Xiong, R. (1999): “Use of hyperbolic and neural network models in modelling quality changes of dry peas in long time cooking”; Journal of Food Engineering, 41, (3/4), Pgs. 151 – 162; 1999.
[14]	Bishop, M. C. (1994): “Neural networks and their applications”; Review in Scientific Instruments, 64(6), Pgs. 1803 – 1831; 1994.
[15]	Ruan, R., Almaer, S., & Zhang, J. (1995): “Prediction of dough rheological properties using neural networks”; Cereal Chemistry, 72(3), Pgs 308 – 311; 1995.
[16]	Rogers, G. F. C and Mayhew, Y. R. (1989): “Engineering Thermodynamics”; ELBS 3/e, Pgs. 469 – 502; 1989.
[17]	Sweat, V. E. (1985): “Thermal properties of low and intermediate moisture food”; ASHRAE Transaction, 91, Pgs. 369 – 389; 1985.
[18]	Fang, Q., Bilby, G., Haque, E., Hanna, M. A., and Spillman, C. K. (1998):” Neural network modelling of physical properties of ground wheat”; Cereal Chemistry, 75(2),Pgs 251 – 253; 1998.
[19]	Bakshi, S. A. and Yoon, J. (1984): “Thermophysical properties of bread rolls during baking”; Lebensmittel Wissenschaft und Technologie, 17, Pgs. 90 – 93; 1984.
[20]	Sablani, S. S. and Shayya, W. H. (2001): “Computerization of Stumbo’s method of thermal process calculations using neural networks”; Journal of Food Engineering, 47, Pgs. 233 – 240; 2001.
[21]	Christina Rask (1989): “Thermal properties of dough and bakery products”; A review of published data. Journal of Food Engineering. Volume 9, Issue 3, 1989, Pgs. 167 – 193; 1989.
[22]	Kulacki, F. A. and Kennedy, S. C. (1978): “Measurement of the thermo-physical properties of common cookie dough”; Journal of Food Science; 43, Pgs. 380 – 384; 1978.
[23]	Buhri, A. B. and Singh, R. P. (1993): “Measurement of food thermal conductivity using differential scanning calorimetry”; Journal of Food Science; 58, Pgs. 1145 – 1147; 1993.
[24]	Zhou, L., Puri, V. M. and Anantheswaran, R. C. (1994): “Measurement of coefficients for simultaneous heat and mass transfer in food products”; Drying Technology; 12; Pgs. 607 – 627; 1994.
[25]	Vos, B. H. (2005):”Measurements of thermal conductivity by a non-steady state method”; Applied Science Research; A5; Pgs. 425 – 438; 2005.
[26]	Gustafsson, S. E., Karawacki, E. and Khan, M. N. (2001): “Transient hot-strip method for simultaneously measuring thermal conductivity and thermal diffusivity of solids and fluids”; Journal of Applied Physics; 12; Pgs. 1411 – 1421; 2001.
[27]	Lind, I. (1998): “Thawing of minced meat and dough: thermal data and mathematical modeling”; Pro. Int. Symp. On progress in food preservation process; Vol. 1, CERIA, Brussels, Belgium; Pgs. 101 – 111; 1998.

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APA Style

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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ACS 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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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://article.sciencepublishinggroup.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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Author Information

Ajasa
Abiodun Afis
Adenowo
Adetokunbo Abayomi
Ogunlewe
Adeyinka Oluremi
Folorunso
Comfort Oluseyi

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APA Style

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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ACS 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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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://article.sciencepublishinggroup.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}
}

Copy | Download

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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