The application of artiﬁcial neural networks (ANN) in the freeze-drying of button mushrooms has been investigated. Networks with a single hidden layer, different training algorithms and complexity in terms of the number of neurons were evaluated for identifying the best ANN infrastructure. Moisture content, moisture ratio and drying rate were taken as output drying parameters for which ANN models provided an overall correlation coefﬁcient (R) of 0.994, 0.991 and 0.992, respectively. The predictive efﬁciency of ANN was compared to semi-empirical models. Coefﬁcients for semi-empirical models of moisture ratio were determined. Logarithm model gave the best ﬁt (R = 0.985) for moisture ratio prediction but with larger mean square error and lower correlation than ANN model. The study highlights that ANN models with low complexity can be developed to precisely predict drying behaviour of biological materials while providing comparable and even superior results to that obtained from available semi-empirical drying models. Keywords Artiﬁcial neural network Training algorithm Freeze-drying Button mushroom List of symbols w Weight of connection from ith ij a, b, c, k, n, k , k , k Model coefﬁcients neuron to jth neuron 0 1 2 b , b Weight bias of jth and
Neural Computing and Applications – Springer Journals
Published: Jun 2, 2018
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