Investigation of Physical Properties Changes of Kiwi Fruit during Different Loadings, Storage, and Modeling with Artificial Neural Network. (21st September 2020)

• Record Type: Journal Article
• Authors: Vahedi Torshizi, Mohammad; Khojastehpour, Mehdi; Tabarsa, Farhad; Ghorbanzadeh, Amir; Akbarzadeh, Ali
• Is Part Of: International journal of fruit science. Volume 20(2020)Supplement 3
• Page Start: S1417
• Page End: S1435
• Publication Date: 2020-09-21
• Subjects: Kiwi -- physical properties -- storage -- artificial neural network; Fruit-culture -- Periodicals; Tree crops -- Periodicals; Fruit trees -- Periodicals; 634
• DOI: 10.1080/15538362.2020.1796889
• Languages: English
• ISSNs: 1553-8362

Abstract

ABSTRACT: Considering that damages and forces to the fruit cause quantitative and qualitative changes in the fruit, in this study, the effects of three levels of loading force (wide and thin edges) (15, 30, and 45 N), 2 fixed positions on the Instron fixed jaw (vertical and horizontal), and 3 storage periods on Hayward kiwi were investigated. Experiments were analyzed as a completely randomized factorial design using SAS statistical software and data were analyzed for prediction using a multilayer perceptron artificial neural network. Statistical results showed that weight, volume, and density of kiwi fruit were decreased for loading of wide and thin edges, and according to the results, it can be concluded that weight loss in wide edge loading was more than loading of thin edges. Also, the weight, volume, and density of the fruit decreased significantly when the fruit was extensively loaded. For neural networks the best R value for weight, volume, and density were 0.9992, 0.99840, and 0.997, respectively, and for RMSE which should be the lowest among the networks, 0.22584, 3091.13 and 0.0049, respectively. Overall, it can be stated that the neural network was capable of predicting weight, volume, and density for both types of loading. But for the wide edge, equivalent, geometric, and arithmetic diameters, and for the thin edge of the aspect ratio and rationality coefficient have had a far greater impact on artificial neural network improvement and data prediction. In brief, for loading the thin edge of the network with loading force input, storage period, loading direction, spherical coefficient, spherical coefficient, aspect ratio coefficient, length, width, and thickness (network 2) and for loading wide edge, loading force, storage period, loading direction, equivalent diameter, geometric diameter, arithmetic diameter, length, width, and thickness were the best in terms of accuracy and error.