TY - JOUR
T1 - Artificial neural network modelling of the electrical conductivity property of recombined milk
AU - Therdthai, Nantawan
AU - Zhou, Weibiao
PY - 2001
Y1 - 2001
N2 - This paper focuses on modelling the electrical conductivity of recombined milk by artificial neural network (ANN). It aims to establish a non-linear relationship that accounts for the effect of milk constituents (protein, lactose, and fat) and temperature on the electrical conductivity of recombined milk. Various ANNs of 3-layer and 4-layer were investigated. Compared with 3-layer ANN models, 4-layer ANN models provide better model performance. In addition, log-sigmoid transfer function is proved to perform more practically than tan-sigmoid transfer function. The best ANN model has a 4-4-4-1 structure with log-sigmoid transfer function. After being trained for 4.4×105 epochs by back-propagation, the model produced a correlation coefficient of 0.9937 between the actual electrical conductivity (actual EC) and the modelled electrical conductivity (modelled EC) and a SSE of 0.4864.
AB - This paper focuses on modelling the electrical conductivity of recombined milk by artificial neural network (ANN). It aims to establish a non-linear relationship that accounts for the effect of milk constituents (protein, lactose, and fat) and temperature on the electrical conductivity of recombined milk. Various ANNs of 3-layer and 4-layer were investigated. Compared with 3-layer ANN models, 4-layer ANN models provide better model performance. In addition, log-sigmoid transfer function is proved to perform more practically than tan-sigmoid transfer function. The best ANN model has a 4-4-4-1 structure with log-sigmoid transfer function. After being trained for 4.4×105 epochs by back-propagation, the model produced a correlation coefficient of 0.9937 between the actual electrical conductivity (actual EC) and the modelled electrical conductivity (modelled EC) and a SSE of 0.4864.
KW - artificial neural network
KW - electrical conductivity
KW - reconstituted milk
UR - http://handle.uws.edu.au:8081/1959.7/34008
M3 - Article
SN - 0260-8774
JO - Journal of Food Engineering
JF - Journal of Food Engineering
ER -