Prediction of Physiological Characteristic Changes in Pears Subject to Dynamic Loading Using Artificial Neural Network (ANN)

Document Type : Research paper

Authors

1 Department of Bio-System Mechanical Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

2 Department of Mechanics, Faculty of Emam Ali, Kordkuy Branch, Technical and Vocational University (TVU), Golestan, Iran

Abstract

The current study was aimed to evaluate the physiological properties of pear influenced by two dynamics of loading force and the storage time. In this experiment, the pears were subjected to dynamic loading (300, 350 and 400 g) and different storage periods (5, 10 and 15 d). The amounts of fruit total phenol, antioxidant and Vitamin C contents were evaluated after each storage period. In the present study, multilayer perceptron (MLP) artificial neural network featuring a hidden layer and two activating functions (hyperbolic tangent-sigmoid) and a total number of 5 and 10 neurons in each layer were selected for the loading force and storage period so that the amounts of the total phenol, antioxidants and Vitamin C contents of the fruits could be forecasted. According to the obtained results, the highest R2 for dynamic loading in a network with 5 neurons in the hidden layer and a sigmoid activation function were obtained for total phenol content (R2 = 0.980), antioxidant (R2 = 0.983) and Vitamin C (R2 = 0.930). Additionally, considering the value of Epoch and Run for the network, the ability of the neural network to predict total phenol, antioxidant and Vitamin C contents can be used. According to the obtained results, the neural network with these two activation functions possesses an appropriate ability in overlapping and predicting the simulated data based on real data.

Keywords

References

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International Journal of Horticultural Science and Technology
Volume 9, Issue 3
September 2022
Pages 275-289

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History

  • Receive Date: 09 February 2021
  • Revise Date: 20 September 2021
  • Accept Date: 08 November 2021

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