TY - JOUR
T1 - Using neural networks to predict thermal conductivity of food as a function of moisture content, temperature and apparent porosity
AU - Sablani, Shyam S.
AU - Rahman, M. Shafiur
PY - 2003
Y1 - 2003
N2 - 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.
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.
KW - Back-propagation
KW - Fruits
KW - Heat transfer
KW - Thermal properties
KW - Vegetables
UR - http://www.scopus.com/inward/record.url?scp=0038367721&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0038367721&partnerID=8YFLogxK
U2 - 10.1016/S0963-9969(03)00012-7
DO - 10.1016/S0963-9969(03)00012-7
M3 - Article
AN - SCOPUS:0038367721
SN - 0963-9969
VL - 36
SP - 617
EP - 623
JO - Food Research International
JF - Food Research International
IS - 6
ER -