Predicting the physiological characteristic changes in pears subjected to external loads using Artificial Neural Network (ANN)-Part 1: Static loading

Document Type : Research Article-en

Authors

• Mohsen Azadbakht ¹
• Mohammad Vahedi Torshizi ²
• Mohammad Javad Mahmoodi ²

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

² Department of Bio-System Mechanical Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.

https://doi.org/10.22067/ifstrj.v16i3.82445

Abstract

This research was aimed to study the effects of loading force and storage period on the physiological characteristic of pears. In this experiment, the pears were subjected to quasi-static loading (wide-edge and thin-edge) and different storage periods (5, 10 and 15 days). The amounts of the fruits’ 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 rates for thin-edge and wide-edge loading in a network with 10 neurons in the hidden layer and a sigmoid activation function were obtained for total phenol content( =0.9539- =0.9865), antioxidant ( =0.9839- =0.9649) and vitamin C ( =0.9758); as for wide-edge loading in a network with 5 neurons in the hidden layer and hyperbolic tangent activation function,  the highest R2 rate of vitamin C content was obtained equal to =0.9865. 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

• Pears’ internal contents
• Loading
• storage
• Neural Network
• Activation function

References

Azadbakht, M., Aghili, H., Ziaratban, A., & Vehedi Torshizi, M. (2017). Application of artificial neural network method to exergy and energy analyses of fluidized bed dryer for potato cubes. Energy, 120, 947–958.

Azadbakht, M., Torshizi, M. V., & Ziaratban, A. (2016). Application of Artificial Neural Network ( ANN ) in predicting mechanical properties of canola stem under shear loading. Agricultural Engineering International: CIGR Journal, 18(5), 413–424.

B. Khoshnevisan, Sh. Rafiee, M. Omid, M. Y. (2013). Prediction of environmental indices of Iran wheat production using artificial neural networks. International Journal of Energy and Environment, 4(2), 339–348.

Beale, R., & Jackson, T. (1998). Neural Computing: An Introduction. London, UK, Institude of Physics Publishing, Bristol BSI 6BE.

Galvis-Sanchez, A. C., Fonseca, S. C., Morais, A. M. M. B., & Malcata, F. X. (2004). Sensorial and physicochemical quality responses of pears (cv Rocha) to long-term storage under controlled atmospheres. Journal of the Science of Food and Agriculture, 84(13), 1646–1656.

Gurrieri, S., Miceli, L., Lanza, C. M., Tomaselli, F., Bonomo, R. P., & Rizzarelli, E. (2000). Chemical characterization of sicilian prickly pear (Opuntia ficus indica) and perspectives for the storage of its juice. Journal of Agricultural and Food Chemistry, 48(11), 5424–5431.

Jaramillo-Flores, M. E., Gonzalez-Cruz, L., Cornejo-Mazon, M., Dorantes-alvarez, L., Gutierrez-Lopez, G. F., & Hernandez-Sanchez, H. (2003). Effect of Thermal Treatment on the Antioxidant Activity and Content of Carotenoids and Phenolic Compounds of Cactus Pear Cladodes (Opuntia ficus-indica). Food Science and Technology International, 9(4), 271–278.

Kazem, A., Hassan, K., Mohamad-Jafar, M., & Mohsen, B. (2015). Postharvest physicochemical changes and properties of Asian ( Pyrus serotina Rehd .) & European ( Pyrus communis L .) pear cultivars Postharvest Fruit Physicochemical Changes and Properties of Asian. Hort. Environ. Biotechnol., 49(4)(2008), 244–252.

Li, W. L., Li, X. H., Fan, X., Tang, Y., & Yun, J. (2012). Response of antioxidant activity and sensory quality in fresh-cut pear as affected by high O2active packaging in comparison with low O2packaging. Food Science and Technology International, 18(3), 197–205.

Malakouti, M. J., Barzegar, M., Arzani, K., & Khoshghalb, H. (2009). Polyphenoloxidase activity, polyphenol and ascorbic acid concentrations and internal browning in Asian pear (Pyrus serotina Rehd.) Fruit during storage in relation to time of harvest. European Journal of Horticultural Science, 74(2), 61–65.

Mazloumzadeh, S. ., Alavi, S. ., & Nouri, M. (2008). Comparison of Artificial Neural and Wavelet Neural Networks for Prediction of Barley Breakage in Combine Harvester. Journal of Agriculture, 10(2), 181–195.

Meng, X., Zhang, M., & Adhikari, B. (2012). Prediction of storage quality of fresh-cut green peppers using artificial neural network. International Journal of Food Science & Technology, 47(8), 1586–1592.

Menhaj, M. (2000). Foundation of Artifitioal Neural Networks. Amir Kabir univercity.

Salehi, F. 1, Gohari Ardabili, A., Nemati, A. 2, & Latifi Darab, R. (2017). Modeling of strawberry drying process using infrared dryer by genetic algorithm–artificial neural network method. Journal Food Science and Technology, 14, 105–114.

Salehi, F., & Razavi, S. M. A. (2012). Dynamic modeling of flux and total hydraulic resistance in nanofiltration treatment of regeneration waste brine using artificial neural networks. Desalination and Water Treatment, 41(1–3), 95–104.

Soleimanzadeh, B., Hemati, L., Yolmeh, M., & Salehi, F. (2015). GA-ANN and ANFIS models and salmonella enteritidis inactivation by ultrasound. Journal of Food Safety, 35(2), 220–226.

Taheri-Garavand, A., Karimi, F., Karimi, M., Lotfi, V., & Khoobbakht, G. (2018). Hybrid response surface methodology–artificial neural network optimization of drying process of banana slices in a forced convective dryer. Food Science and Technology International, 24(4), 277–291.

Tavarini, S., Degl’Innocenti, E., Remorini, D., Massai, R., & Guidi, L. (2008). Antioxidant capacity, ascorbic acid, total phenols and carotenoids changes during harvest and after storage of Hayward kiwifruit. Food Chemistry, 107(1), 282–288.

Torkashvand, A. M., Ahmadi, A., & Nikravesh, N. L. (2017). Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network (ANN) and multiple linear regressions (MLR). Journal of Integrative Agriculture, 16(7), 1634–1644.

Yordi, E., Koeling, R., Mota, Y., Matos, M. J., Santana, L., Uriarte, E., & Molina, E. (2015). Application of KNN algorithm in determining the total antioxidant capacity of flavonoid-containing foods. In Proceedings of The 19th International Electronic Conference on Synthetic Organic Chemistry (p. e002). Basel, Switzerland: MDPI. https://doi.org/10.3390/ecsoc-19-e002

Yurtlu, Y. B., & Erdoǧan, D. (2005). Effect of storage time on some mechanical properties and bruise susceptibility of pears and apples. Turkish Journal of Agriculture and Forestry, 29(6), 469–482.

Zarifneshat, S., Rohani, A., Ghassemzadeh, H. R., Sadeghi, M., Ahmadi, E., & Zarifneshat, M. (2012). Predictions of apple bruise volume using artificial neural network. Computers and Electronics in Agriculture, 82, 75–86.