Freeze-drying behaviour prediction of button mushrooms using artificial neural network and comparison with semi-empirical models

Freeze-drying behaviour prediction of button mushrooms using artificial neural network and... 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Computing and Applications Springer Journals

Freeze-drying behaviour prediction of button mushrooms using artificial neural network and comparison with semi-empirical models

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Publisher
Springer London
Copyright
Copyright © 2018 by The Natural Computing Applications Forum
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Vision; Computational Biology/Bioinformatics
ISSN
0941-0643
eISSN
1433-3058
D.O.I.
10.1007/s00521-018-3567-1
Publisher site
See Article on Publisher Site

Abstract

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

Journal

Neural Computing and ApplicationsSpringer Journals

Published: Jun 2, 2018

References

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