Using neural networks to predict thermal conductivity of food as a function of moisture content, temperature and apparent porosity

Shyam S. Sablani, M. Shafiur Rahman

Research output: Contribution to journalArticle

64 Citations (Scopus)

Abstract

An artificial neural network (ANN) model is presented for the prediction of thermal conductivity of food as a function of moisture content, temperature and apparent porosity. The food products considered in the present study were apple, pear, cornstarch, raisin, potato, ovalbumin, sucrose, starch, carrot and rice. Thermal conductivity data of food products (0.012-2.350 W/m K) were obtained from the literature for a wide range of moisture content (0.04-0.98 on wet basis, fraction), temperature (-42-130°C) and apparent porosity (0.0-0.70). Several configurations were evaluated while developing the optimal ANN model. The optimal model ANN model consisted two hidden layers with four neurons in each hidden layer. This model was able to predict thermal conductivity with a mean relative error of 12.6%, a mean absolute error of 0.081 W/m K. The model can be incorporated in heat transfer calculations during food processing where moisture, temperature and apparent porosity dependent thermal conductivity values are required. Rahman's model (data considered only above 0 °C) and a simple multiple regression model (all data points) predicted thermal conductivity with mean relative errors of 24.3 and 81.6%, respectively.

Original languageEnglish
Pages (from-to)617-623
Number of pages7
JournalFood Research International
Volume36
Issue number6
DOIs
Publication statusPublished - 2003

Fingerprint

Thermal Conductivity
Porosity
thermal conductivity
neural networks
porosity
Neural Networks (Computer)
water content
Food
Temperature
temperature
Starch
Pyrus
Daucus carota
Food Handling
Ovalbumin
Vitis
Malus
Solanum tuberosum
foods
Sucrose

Keywords

  • Back-propagation
  • Fruits
  • Heat transfer
  • Thermal properties
  • Vegetables

ASJC Scopus subject areas

  • Food Science

Cite this

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title = "Using neural networks to predict thermal conductivity of food as a function of moisture content, temperature and apparent porosity",
abstract = "An artificial neural network (ANN) model is presented for the prediction of thermal conductivity of food as a function of moisture content, temperature and apparent porosity. The food products considered in the present study were apple, pear, cornstarch, raisin, potato, ovalbumin, sucrose, starch, carrot and rice. Thermal conductivity data of food products (0.012-2.350 W/m K) were obtained from the literature for a wide range of moisture content (0.04-0.98 on wet basis, fraction), temperature (-42-130°C) and apparent porosity (0.0-0.70). Several configurations were evaluated while developing the optimal ANN model. The optimal model ANN model consisted two hidden layers with four neurons in each hidden layer. This model was able to predict thermal conductivity with a mean relative error of 12.6{\%}, a mean absolute error of 0.081 W/m K. The model can be incorporated in heat transfer calculations during food processing where moisture, temperature and apparent porosity dependent thermal conductivity values are required. Rahman's model (data considered only above 0 °C) and a simple multiple regression model (all data points) predicted thermal conductivity with mean relative errors of 24.3 and 81.6{\%}, respectively.",
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AU - Rahman, M. Shafiur

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AB - An artificial neural network (ANN) model is presented for the prediction of thermal conductivity of food as a function of moisture content, temperature and apparent porosity. The food products considered in the present study were apple, pear, cornstarch, raisin, potato, ovalbumin, sucrose, starch, carrot and rice. Thermal conductivity data of food products (0.012-2.350 W/m K) were obtained from the literature for a wide range of moisture content (0.04-0.98 on wet basis, fraction), temperature (-42-130°C) and apparent porosity (0.0-0.70). Several configurations were evaluated while developing the optimal ANN model. The optimal model ANN model consisted two hidden layers with four neurons in each hidden layer. This model was able to predict thermal conductivity with a mean relative error of 12.6%, a mean absolute error of 0.081 W/m K. The model can be incorporated in heat transfer calculations during food processing where moisture, temperature and apparent porosity dependent thermal conductivity values are required. Rahman's model (data considered only above 0 °C) and a simple multiple regression model (all data points) predicted thermal conductivity with mean relative errors of 24.3 and 81.6%, respectively.

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