Document Type : Research Article-en


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


This study evaluated the effect of different dynamic and static loadings and different storage periods on the firmness of pear fruit. Pear fruit was first segregated into three groups of 27 pear in order to undergo three loadings: static thin-edge compression loading, static wide-edge compression loading and dynamic loading. All loaded pears were stored in accordance with three storage period designs: 5-day storage, 10-day storage, and 15-day storage. Following each period, the variations of pear texture were scanned by using the CT-Scan technique as a non-destructive test. Then, the firmness of pear texture was measured using a penetrometer. Data were simulated and evaluated using MLP and RBF artificial neural networks. The results showed that with increasing storage time and loading force , the firmness significantly decreased (1% level) in all three types of loading, In addition, pear texture was destructed under dynamic compression loading in order to compare with other two loadings. Best value artificial neural network for wide edge loading (12 neuron-RBF) was (R2 Wide edge= 0.9738– RMSE Wide edge=0.3419- MAE Wide edge =0.268) and for thin edge loading (4 neuron-RBF) was (R2Thin edge = 0.9946– RMSE Thin edge =0.170977- MAE Thin edge =0.133), also for dynamic loading (8 neuron-RBF) was (R2 Dynamic loading = 0.9933– RMSE Dynamic loading =0.230- MAE Dynamic loading= 0.187).


Afsharnia, F., Mehdizadeh, S. A., Ghaseminejad, M., & Heidari, M. (2017). The effect of dynamic loading on abrasion of mulberry fruit using digital image analysis. Information Processing in Agriculture, 4(4), Pages 291-299.
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.
Diels, E., van Dael, M., Keresztes, J., Vanmaercke, S., Verboven, P., Nicolai, B., et al. (2017). Assessment of bruise volumes in apples using X-ray computed tomography. Postharvest Biology and Technology, 128, 24–32.
Jahangiri, M., Hassan-Beygi, S. R., Aboonajmi, M., & Lotfi, M. (2016). Effects of storage duration and conditions on mechanical properties of viola cucumber fruit under compression loading. Agricultural Engineering International: CIGR Journal, 18(2), 323–332.
Khalifa, S., Komarizadeh, M. H., & Tousi, B. (2011). Usage of fruit response to both force and forced vibration applied to assess fruit firmnessa review. Australian Journal of Crop Science, 5(5), 516–522.
Leśniak, A., & Juszczyk, M. (2018). Prediction of site overhead costs with the use of artificial neural network based model. Archives of Civil and Mechanical Engineering, 18(3), 973–982.
Mazidi, M., Sadrnia, H., & Khojastehpour, M. (2016). Evaluation of orange mechanical damage during packaging by study of changes in firmness. International Food Research Journal, 23(2), 899–903.
Mirzaee, E., Rafiee, S., Keyhani, A., & Djom-Eh, Z. E. (2009). Physical properties of apricot to characterize best post harvesting options. Australian Journal of Crop Science, 3(2), 95–100.
Moggia, C., Graell, J., Lara, I., Gonzalez, G., & Lobos, G. A. (2017). Firmness at Harvest Impacts Postharvest Fruit Softening and Internal Browning Development in Mechanically Damaged and Non-damaged Highbush Blueberries (Vaccinium corymbosum L.). Frontiers in Plant Science, 8(April), 1–11.
Moggia, C., Graell, J., Lara, I., Schmeda-Hirschmann, G., Thomas-Valdes, S., & Lobos, G. A. (2016). Fruit characteristics and cuticle triterpenes as related to postharvest quality of highbush blueberries. Scientia Horticulturae, 211, 449–457.
Montero, C. R. S., Schwarz, L. L., Santos, L. C. dos, Andreazza, C. S., Kechinski, C. P., & Bender, R. J. (2009). Postharvest mechanical damage affects fruit quality of “Montenegrina” and “Rainha” tangerines. Pesquisa Agropecuaria Brasileira, 44(12), 1636–1640.
Read, S. J., Droutman, V., Smith, B. J., & Miller, L. C. (2017). Using neural networks as models of personality process: A tutorial. Personality and Individual Differences, 136,52-67.
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.
Yan, Y., Zhang, X., Zheng, X., Apaliya, M. T., Yang, Q., Zhao, L., et al. (2018). Control of postharvest blue mold decay in pears by Meyerozyma guilliermondii and it’s effects on the protein expression profile of pears. Postharvest Biology and Technology, 136(October 2017), 124–131.
Zhang, H., Wu, J., Zhao, Z., & Wang, Z. (2018). Nondestructive firmness measurement of differently shaped pears with a dual-frequency index based on acoustic vibration. Postharvest Biology and Technology, 138, 11–18.
Zhang, W., Cui, D., & Ying, Y. (2014). Nondestructive measurement of pear texture by acoustic vibration method. Postharvest Biology and Technology, 96, 99–105.