نوع مقاله : مقاله کامل پژوهشی

نویسندگان

1 استادیار، گروه علوم و صنایع غذایی، واحد رودهن، دانشگاه آزاد اسلامی، رودهن، ایران

2 مربی، گروه علوم و صنایع غذایی و تغذیه، دانشگاه علوم پزشکی زاهدان، زاهدان، ایران

3 استادیار، گروه علوم و صنایع غذایی، دانشکده فنی و منابع طبیعی تویسرکان، دانشگاه بوعلی سینا، همدان، ایران

4 دانش‌آموختۀ دکتری، گروه علوم و صنایع غذایی، دانشگاه فردوسی مشهد، مشهد، ایران

10.22101/jrifst.2021.258797.1203

چکیده

امروزه، استفاده از شبیه‌ساز ریاضی و مدل‌سازی منحنی‌های خشک‌کردن، ابزار مفیدی برای بهبود سیستم‌های کنترل کیفیت محصول نهایی در شرایط مختلف است. این روش‌ها معمولاً برای مطالعۀ عوامل موجود در فراآیند، بهینه‌سازی شرایط و فاکتورهای کاری و پیش‌بینی سینتیک خشک‌شدن محصول اعمال می‌شود. در مقالۀ حاضر به‌منظور پیش‌بینی نسبت رطوبت ورقه‌های گوجه‌فرنگی خشک‌شده از دو ابزار هوشمند ازجمله شبکۀ عصبی مصنوعی (ANN) و الگوریتم ژنتیک (GA) استفاده شده است. برای‌این‌منظور، ابتدا 4 مدل ریاضی از سایر مطالعه‌ها گرفته شد و سپس با داده‌های تجربی مطابقت داده شدند. سپس بهترین مدل برازش برای منحنی خشک‌کردن گوجه‌فرنگی انتخاب شد. طبق نتایج، مدلی که توسط آغباشلو و همکاران پیشنهاد شده است، عملکرد بسیار خوبی به‌منظور پیش‌بینی نسبت رطوبت ورقه‌های گوجه‌فرنگی خشک‌شده نشان داد. علاوه‌بر این، از الگوریتم ژنتیک برای بهینه‌سازی بهترین مدل تجربی استفاده شد. درنهایت، نتایج این تحقیق با نتایج مشاهده‌شده در مدل‌های شبکۀ عصبی مصنوعی و الگوریتم ژنتیک مقایسه شد. نتایج نشان داد که مدل الگوریتم ژنتیک دقت بالاتری را به‌منظور پیش‌بینی نسبت رطوبت گوجه‌فرنگی خشک با ضریب همبستگی (R2) 0/9987 ارائه می‌دهد.

کلیدواژه‌ها

Abbaszadeh, A., Motevali, A., Khoshtaghaza, M., & Kazemi, M. (2011). Evaluation of thin-layer drying models and neural network for describing drying kinetics of Lasagnas angustifolia L. International Food Research Journal, 18(4), 1321.

Abdolshahi, A., Heydari Majd, M., Abdollahi, M., Fatemizadeh, S., & Monjazeb Marvdashti, L. (2020). Edible Film Based on Lallemantia peltata L. Seed Gum: Development and Characterization. Journal of Chemical Health Risks, in Press. doi:https://doi.org/10.22034/jchr.2020.1896596.1118

Aghajani, N., Kashaninejad, M., Dehghani, A. A., & Daraei Garmakhany, A. (2012). Comparison between artificial neural networks and mathematical models for moisture ratio estimation in two varieties of green malt. Quality Assurance and Safety of Crops & Foods, 4(2), 93-101. doi:https://doi.org/10.1111/j.1757-837X.2012.00125.x

Ajani, C., Curcio, S., Dejchanchaiwong, R., & Tekasakul, P. (2019). Influence of shrinkage during natural rubber sheet drying: Numerical modeling of heat and mass transfer. Applied Thermal Engineering, 149, 798-806. doi:https://doi.org/10.1016/j.applthermaleng.2018.12.054

Akanbi, C. T., Adeyemi, R. S., & Ojo, A. (2006). Drying characteristics and sorption isotherm of tomato slices. Journal of food engineering, 73(2), 157-163. doi:https://doi.org/10.1016/j.jfoodeng.2005.01.015

Aktaş, M., Şevik, S., Özdemir, M. B., & Gönen, E. (2015). Performance analysis and modeling of a closed-loop heat pump dryer for bay leaves using artificial neural network. Applied Thermal Engineering, 87,714-723. doi:https://doi.org/10.1016/j.applthermaleng.2015.05.049

AOAC. (1990). Official methods of analysis. Washington: Association of Official Analytical Chemists.

Badaoui, O., Hanini, S., Djebli, A., Haddad, B., & Benhamou, A. (2019). Experimental and modelling study of tomato pomace waste drying in a new solar greenhouse: Evaluation of new drying models. Renewable energy, 133, 144-155. doi:https://doi.org/10.1016/j.renene.2018.10.020

Castro, A., Mayorga, E., & Moreno, F. (2018). Mathematical modelling of convective drying of fruits: A review. Journal of food engineering, 223, 152-167. doi:https://doi.org/10.1016/j.jfoodeng.2017.12.012

Cernîşev, S. (2010). Effects of conventional and multistage drying processing on non-enzymatic browning in tomato. Journal of food engineering, 96(1), 114-118. doi:https://doi.org/10.1016/j.jfoodeng.2009.07.002

Demiray, E., Tulek, Y., & Yilmaz, Y. (2013). Degradation kinetics of lycopene, β-carotene and ascorbic acid in tomatoes during hot air drying. LWT-Food Science and Technology, 50(1), 172-176. doi:https://doi.org/10.1016/j.lwt.2012.06.001

Diamante, L. M., Ihns, R., Savage, G. P., & Vanhanen, L. (2010). A new mathematical model for thin layer drying of fruits. International journal of food science & technology, 4(9), 1956-1962. doi:https://doi.org/10.1111/j.1365-2621.2010.02345.x

Doymaz, I. (2004a). Convective air drying characteristics of thin layer carrots. Journal of food engineering, 61(3), 359-364. doi:https://doi.org/10.1016/S0260-8774(03)00142-0

Doymaz, İ. (2004b). Effect of pre-treatments using potassium metabisulphide and alkaline ethyl oleate on the drying kinetics of apricots. Biosystems Engineering, 89(3), 281-287. doi:https://doi.org/10.1016/j.biosystemseng.2004.07.009

Doymaz, İ., & İsmail, O. (2011). Drying characteristics of sweet cherry. Food and bioproducts processing, 89(1), 31-38. doi:https://doi.org/10.1016/j.fbp.2010.03.006

Erenturk, S., & Erenturk, K. (2007). Comparison of genetic algorithm and neural network approaches for the drying process of carrot. Journal of food engineering, 78(3), 905-912. doi:https://doi.org/10.1016/j.jfoodeng.2005.11.031

Figiel, A. (2010). Drying kinetics and quality of beetroots dehydrated by combination of convective and vacuum-microwave methods. Journal of food engineering, 98(4), 461-470. doi:https://doi.org/10.1016/j.jfoodeng.2010.01.029

Garau, M., Simal, S., Femenia, A., & Rosselló, C. (2006). Drying of orange skin: drying kinetics modelling and functional properties. Journal of food engineering, 75(2), 288-295. doi:https://doi.org/10.1016/j.jfoodeng.2005.04.017

Guiné, R. P., Pinho, S., & Barroca, M. J. (2011). Study of the convective drying of pumpkin (Cucurbita maxima). Food and bioproducts processing, 89(4), 422-428. doi:https://doi.org/10.1016/j.fbp.2010.09.001

Heydari-Majd, M., Ghanbarzadeh, B., Shahidi-Noghabi, M., Abdolshahi, A., Dahmardeh, S., & Mohammadi, M. M. (2020). Poly (lactic acid)-based bionanocomposites: effects of ZnO nanoparticles and essential oils on physicochemical properties. Polymer Bulletin, 1-23. doi:https://doi.org/10.1007/s00289-020-03490-z

Kashiri, M., Daraei Garmakhany, A., & Dehghani, A. A. (2012). Modelling of sorghum soaking using artificial neural networks (MLP). Quality Assurance and Safety of Crops & Foods, 4(4), 179-184. doi:https://doi.org/10.1111/j.1757-837X.2012.00184.x

Katoch, S., Chauhan, S. S., & Kumar, V. (2020). A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications, 1-36. doi:https://doi.org/10.1007/s11042-020-10139-6

Kerdpiboon, S., Kerr, W. L., & Devahastin, S. (2006). Neural network prediction of physical property changes of dried carrot as a function of fractal dimension and moisture content. Food research international, 39(10), 1110-1118. doi:https://doi.org/10.1016/j.foodres.2006.07.019

Li, Z., Li, P., & Liu, J. (2011). Physical and mechanical properties of tomato fruits as related to robot’s harvesting. Journal of food engineering, 103(2), 170-178. doi:https://doi.org/10.1016/j.jfoodeng.2010.10.013

Mayor, L., & Sereno, A. (2004). Modelling shrinkage during convective drying of food materials: a review. Journal of food engineering, 61(3), 373-386. doi:https://doi.org/10.1016/S0260-8774(03)00144-4

Mewa, E. A., Okoth, M. W., Kunyanga, C. N., & Rugiri, M. N. (2019). Experimental evaluation of beef drying kinetics in a solar tunnel dryer. Renewable energy, 139, 235-241. doi:https://doi.org/10.1016/j.renene.2019.02.067

Mokhtarian, M., Heydari Majd, M., Koushki, F., Bakhshabadi, H., Daraei Garmakhany, A., & Rashidzadeh, S. (2014a). Optimisation of pumpkin mass transfer kinetic during osmotic dehydration using artificial neural network and response surface methodology modelling. Quality Assurance and Safety of Crops & Foods, 6(2), 201-214. doi:https://doi.org/10.3920/QAS2012.0121

Mokhtarian, M., Koushki, F., Bakhshabadi, H., Askari, B., Garmakhany, A. D., & Rashidzadeh, S. (2014b). Feasibility investigation of using artificial neural network in process monitoring of pumpkin air drying. Quality Assurance and Safety of Crops & Foods, 6(2), 191-199. doi:https://doi.org/10.3920/QAS2012.0119

Mousavi, M., & Javan, S. (2009). Modeling and simulation of apple drying, using artificial neural network and neuro-taguchi’s method. Journal of Agricultural Science and Technology (JAST), 11, 559-571.

Mozumder, N., Rahman, M., Kamal, M., Mustafa, A., & Rahman, M. (2012). Effects of pre-drying chemical treatments on quality of cabinet dried tomato powder. Journal of Environmental Science and Natural Resources, 5(1), 253-265.

Mpotokwane, S., Gaditlhatlhelwe, E., Sebaka, A., & Jideani, V. A. (2008). Physical properties of bambara groundnuts from Botswana. Journal of food engineering, 89(1), 93-98. doi:https://doi.org/10.1016/j.jfoodeng.2008.04.006

Pu, Y.-Y., & Sun, D.-W. (2017). Combined hot-air and microwave-vacuum drying for improving drying uniformity of mango slices based on hyperspectral imaging visualisation of moisture content distribution. Biosystems Engineering, 156, 108-119. doi:https://doi.org/10.1016/j.biosystemseng.2017.01.006

Salarbashi, D., Tafaghodi, M., & Heydari-Majd, M. (2020). Fabrication of curcumin-loaded soluble soy bean polysaccharide/TiO2 bio-nanocomposite for improved antimicrobial activity. Nanomedicine Journal, 7(4), 291-298. doi:https://doi.org/10.22038/NMJ.2020.07.00005

Shakouri, S., Ziaolhagh, H. R., Sharifi-Rad, J., Heydari-Majd, M., Tajali, R., Nezarat, S., & Da Silva, J. A. T. (2015). The effect of packaging material and storage period on microwave-dried potato (Solanum tuberosum L.) cubes. Journal of food science and technology, 52(6), 3899-3910. doi:https://doi.org/10.1007/s13197-014-1464-x

Singh, N. J. (2011). Neural network approaches for prediction of drying kinetics during drying of sweet potato. Agricultural Engineering International: CIGR Journal, 13(1).

Taheri-Garavand, A., Rafiee, S., & Keyhani, A. (2011). Mathematical modeling of thin layer drying kinetics of tomato influence of air dryer conditions. Int Trans. J. Eng. Manage. Sci. Tech, 2, 147-160.

Tavakolipour, H., & Mokhtarian, M. (2012). Neural network approaches for prediction of pistachio drying kinetics. International Journal of Food Engineering, 8(3). doi:https://doi.org/10.1515/1556-3758.2481

Zarein, M., Samadi, S. H., & Ghobadian, B. (2015). Investigation of microwave dryer effect on energy efficiency during drying of apple slices. Journal of the Saudi Society of Agricultural Sciences, 14(1), 41-47. doi:https://doi.org/10.1016/j.jssas.2013.06.002