Modeling of Blackberry Drying Process by Double sided infrared System using Genetic Algorithm–Artificial Neural Network Method

Document Type : Research Article

Authors

1 Ph. D. Student, Department of Food Science and Technology, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

2 M.sc student of Faculty of Food Science & Technology, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

3 Professor, Faculty of Food Science & Technology, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

Abstract

Black mulberry is a very important and rich source of polyphenols and anthocyanin’s that dry and maintain can be valuable. The use of infrared radiation as a new method in drying preserves the quality of the final product, which increases the speed of drying and reduces the cost of the process. The effect of 120, 180, 240 W of infrared power, 5, 10 and 15 cm of sample distance from infrared source and drying time on black mulberry drying were investigated. Modeling of black mulberry drying was used to predict the output of this study using genetic algorithm-artificial neural network with four inputs (infrared power, sample distance from infrared source and drying time) and one output (weight loss percentage). It was determined that by increasing the infrared power and reducing the distance between the samples and the infrared lamp, the drying rate of black mulberry was significantly increased by two-way infrared (P<0.05). The sigmoid activation function was selected as the activation function in the hidden layer and output layer due to the lower error value than other functions. According to the test method and the fault method, 15% of the data was used for training to achieve the best learning conditions for the relationships between inputs and outputs by the network. 15% of the data was used for the trained network test, and 60% of the remaining data was used to evaluate the network. According to the results, it can be concluded that using the sigmoid activation function and the network with 8 neurons in a hidden layer, we can well analyze the percentage of weight loss using the genetic algorithm-artificial neural network during the black mulberry drying process was predicted by two-way infra-red method (R2=0/999). Drying time was introduced as the most effective factor for controlling black mulberry weight loss using sensitivity analysis by optimal neural network.

Graphical Abstract

Modeling of Blackberry Drying Process by Double sided infrared System using Genetic Algorithm–Artificial Neural Network Method

Highlights

  • Determination percentage of weight losses  in black mulberry by two-way infrared irradiation
  • Determination effect of different powers, distances and times in black mulberry by two-way infrared irradiation
  • Genetic Algorithm - Artificial Neural Network modeling drying in black mulberry by two-way infrared irradiation

Keywords

Main Subjects

References

 [1]Ercisli, S., & Orhan, E. (2008). Some physico-chemical characteristics of black mulberry (Morus nigra L.) genotypes from Northeast Anatolia region of Turkey. Sci. Hortic, 116(1), 41-46.‏
 [2]Suh, H. J., Noh, D. O., Kang, C. S., Kim, J. M., & Lee, S. W. (2003). Thermal kinetics of color degradation of mulberry fruit extract. Food - Nahrung, 47(2), 132-135.‏
[3Lin, J. Y., & Tang, C. Y. (2007). Determination of total phenolic and flavonoid contents in selected fruits and vegetables, as well as their stimulatory effects on mouse splenocyte proliferation. Food chem, 101(1), 140-147.‏
 [4] Hertog, M. G., Sweetnam, P. M., Fehily, A. M., Elwood, P. C., & Kromhout, D. (1997). Antioxidant flavonols and ischemic heart disease in a Welsh population of men: the Caerphilly Study. Am J Clin Nutr, 65(5), 1489-1494.‏
[5] Singhal, B. K., Khan, M. A., Dhar, A., Baqual, F. M., & Bindroo, B. B. (2010). Approaches to industrial exploitation of mulberry (mulberry sp.) fruits. J. Fruit Ornam Plant Res, 18, 83-99.‏
 [6] Arslan, O., Erzengin, M., Sinan, S., & Ozensoy, O. (2004). Purification of mulberry (Morus alba L.) polyphenol oxidase by affinity chromatography and investigation of its kinetic and electrophoretic properties. Food chem, 88(3), 479-484.‏
 [7] Fellows, P. J. (2009). Food processing technology: principles and practice. Elsevier.
[8] Moreira, R., Figueiredo, A., & Sereno, A. (2000). Shrinkage of apple disks during drying by warm air convection and freeze drying. Drying Technol, 18(1-2), 279-294.‏
 [9] Lurie, S., & Nussinovitch, A. (1996). Compression characteristics, firmness, and texture perception of heat treated and unheated apples. j . Food Sci Technol Res, 31(1), 1-5.‏
 [10] Lin, Y. P., Lee, T. Y., Tsen, J. H., & King, V. A. E. (2007). Dehydration of yam slices using FIR-assisted freeze drying. J. Food Eng, 79(4), 1295-1301.‏
 [11] Jun, S., Krishnamurthy, K., Irudayaraj, J., & Demirci, A. (2010). Fundamentals and theory of infrared radiation. Pan, Z., and Atungulu, GG, eds., Infrared heating for food and agricultural processing: Boca Raton, Florida, CRC Press, 1-9.‏
 [12] Aidani, E., Hadadkhodaparast, M., & Kashaninejad, M. (2017). Experimental and modeling investigation of mass transfer during combined infrared‐vacuum drying of Hayward kiwifruits. Food Sci Nutr, 5(3), 596-601.‏
 [13] Wu, J., Zhang, H., & Li, F. (2017). A study on drying models and internal stresses of the rice kernel during infrared drying. Drying Technol, 35(6), 680-688.‏
 [14] Younis, M., Abdelkarim, D., & El-Abdein, A. Z. (2018). Kinetics and mathematical modeling of infrared thin-layer drying of garlic slices. Saudi J. Biol. Sci, 25(2), 332-338.‏
 [15] Orikasa, T., Ono, N., Watanabe, T., Ando, Y., Shiina, T., & Koide, S. (2018). Impact of blanching pretreatment on the drying rate and energy consumption during far-infrared drying of Paprika (Capsicum annuum L.). Food Quality and Safety, 2(2), 97-103.‏
[16Rad, S. J., Kaveh, M., Sharabiani, V. R., & Taghinezhad, E. (2018). Fuzzy logic, artificial neural network and mathematical model for prediction of white mulberry drying kinetics. Heat Mass Transf, 54(11), 3361-3374.‏
 [17] Jafari, S. M., Ganje, M., Dehnad, D., & Ghanbari, V. (2016). Mathematical, fuzzy logic and artificial neural network modeling techniques to predict drying kinetics of onion. J FOOD PROCESS PRES, 40(2), 329-339.‏
 [18] Bahmani, A., Jafari, S. M., Shahidi, S. A., & Dehnad, D. (2016). Mass transfer kinetics of eggplant during osmotic dehydration by neural networks. J FOOD PROCESS PRES, 40(5), 815-827.‏
 [19] Bahmani, A., Jafari, S. M., Shahidi, S. A., & Dehnad, D. (2016). Mass transfer kinetics of eggplant during osmotic dehydration by neural networks. J FOOD PROCESS PRES, 40(5), 815-827.‏
 [20]Bahramparvar, M., Salehi, F., & Razavi, S. M. (2014). Predicting total acceptance of ice cream using artificial neural network. J FOOD PROCESS PRES, 38(3), 1080-1088.‏
 [21] Salehi, F., Abbasi Shahkoh, Z., & Godarzi, M. (2014). Apricot Osmotic Drying Modeling Using Genetic Algorithm-Artificial Neural Network. Innov Food Sci Technol.‏
 [22] محمدرضا اصغری، رحیم ابراهیمی، بهرام حسین زاده و داود قنبریان. (1396). " مدل‌سازی پارامترهای کیفی توت سفید در فرآیند خشک‌شدن با استفاده از شبکه عصبی مصنوعی". مهندسی بیوسیستم ایران، 48:1، 9-18.
[23]Erenturk, S., & Erenturk, K. (2007). Comparison of genetic algorithm and neural network approaches for the drying process of carrot. J. Food Eng, 78(3), 905-912.‏
 [24] Lertworasirikul, S., & Saetan, S. (2010). Artificial neural network modeling of mass transfer during osmotic dehydration of kaffir lime peel. J. Food Eng, 98(2), 214-223.‏
 [25] Salehi, F., & Razavi, S. M. (2016). Modeling of waste brine nanofiltration process using artificial neural network and adaptive neuro-fuzzy inference system. Desalination and Water Treatment, 57(31), 14369-14378.‏
 [26] اسدی امیر آبادی, کاشانی نژاد, صالحی, & فخرالدین. (2017). مدل سازی فرآیند خشک کردن بادمجان توسط سامانه مادون قرمز به روش الگوریتم ژنتیک-شبکه عصبی مصنوعی. نشریه فرآوری و نگهداری مواد غذایی, 9(1), 85-96.
[27] آیدانی, عماد, حدادخداپرست, & کاشانی نژاد. (2017). بررسی خصوصیات کیوی خشک شده با سامانه مادون قرمز و مدل‌سازی فرآیند. علوم غذایی و تغذیه, 14, 53-66.
[28] Puente-Díaz, L., Ah-Hen, K., Vega-Gálvez, A., Lemus-Mondaca, R., & Scala, K. D. (2013). Combined infrared-convective drying of murta (Ugni molinae Turcz) berries: kinetic modeling and quality assessment. Drying Technol, 31(3), 329-338.‏
Innovative Food Technologies
Volume 7, Issue 2
February 2020
Pages 201-210

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History

  • Receive Date: 17 June 2018
  • Revise Date: 07 August 2018
  • Accept Date: 16 September 2018

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