Investigation the effect of ultrasonic pretreatment on drying rate of cherry and process modeling using genetic algorithm-artificial neural network method

Subject Areas : Negah

F. Salehi ¹ , M. Inanloodoghouz ² , S. Ghazvineh ³

1 - Associate Professor of the Department of Food Science and Technology, Bu-Ali Sina University, Hamedan, Iran
2 - MSc Student of the Department of Food Science and Technology, Bu-Ali Sina University, Hamedan, Iran
3 - BSc Student of the Department of Food Science and Technology, Bu-Ali Sina University, Hamedan, Iran

Received: 2023-06-05 Accepted : 2023-07-15 Published : 2023-09-23

Keywords: Sensitivity analysis, Ultrasound, Cherry, Activation function, Rehydration,

Abstract :

Introduction: Due to their high moisture content, cherries have a very high rate of spoilage and require the use of some post-harvest treatments in order to be effectively preserved. Drying is one of these preservation methods. Drying time can be shortened by using ultrasonic waves as a pretreatment before drying agricultural products. The genetic algorithm–artificial neural network method has a high ability to find the optimal value of a complex objective function.Materials and Methods: In this study, the effect of sonication treatment for 0, 3, 6, and 9 minutes on drying time, weight changes, and rehydration of cherries was investigated. In the next step, this process was modeled by genetic algorithm–artificial neural network method with 2 inputs (drying time and ultrasonic pretreatment time) and 1 output (weight loss percentage).Results: The results of this research showed that sonication for up to 3 min increased the rate of moisture removal from cherries and thus reduced drying time. 3-min treatment with ultrasound increased the rehydration of dried cherries; but as the treatment time increased to 6 min and 9 min, the amount of rehydration decreased. Genetic algorithm–artificial neural network modeling results showed that a network with a 1-4-2 structure in one hidden layer and using the hyperbolic tangent activation function can predict the weight loss percentage of cherries during drying with a high correlation coefficient and a low error value. According to the results of sensitivity analysis test, drying time was the most effective factor in changing the weight loss percentage of cherries during the drying process.Conclusion: In general, the best conditions for drying cherries are pretreatment with ultrasound for 3 minutes followed by drying the product with hot-air. Based on the modeling results, the genetic algorithm–artificial neural network method can also be used to predict the parameters of the cherry drying process.

References:

Al-Khuseibi, M.K., Sablani, S.S. & Perera, C.O. (2005). Comparison of water blanching and high hydrostatic pressure effects on drying kinetics and quality of potato. Drying Technology, 23(12), 2449-2461. https://doi.org/10.1080/07373930500340734.

Amin Ekhlas, S., Pajohi-Alamoti, M.R. & Salehi, F. (2023). Effect of ultrasonic waves and drying method on the moisture loss kinetics and rehydration of sprouted wheat. Journal of Food Science and Technology, 20(135), 159-168. https://doi.org/10.22034/fsct.19.135.159.

Awad, T.S., Moharram, H.A., Shaltout, O.E., Asker, D. & Youssef, M.M. (2012). Applications of ultrasound in analysis, processing and quality control of food: A review. Food Research International, 48(2), 410-427. https://doi.org/10.1016/j.foodres.2012.05.004.

Cheng, D., Ma, Q., Zhang, J., Jiang, K., Cai, S., Wang, W., Wang, J. & Sun, J. (2022). Cactus polysaccharides enhance preservative effects of ultrasound treatment on fresh-cut potatoes. Ultrasonics Sonochemistry, 90, 106205. https://doi.org/10.1016/j.ultsonch.2022.106205.

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

Fadaie, M., Hosseini Ghaboos, S.H. & Beheshti, B. (2020a). Characterization of dried persimmon using infrared dryer and process modeling using genetic algorithm-artificial neural network method. Journal of Food Science and Technology, 17(100), 189-200. https://doi.org/10.52547/fsct.17.100.189.

Fadaie, M., Hosseini Ghaboos, S.H. & Beheshti, B. (2020b). Characterization of dried persimmon using infrared dryer and process modeling using genetic algorithm-artificial neural network method. Journal of Food Science and Technology, 17(100), 189-199. https://doi.org/10.29252/fsct.17.03.15.

Ghorbani, M., Naghipour, L., Karimi, V. & Farhoudi, R. (2013). Sensitivity analysis of the effective input parameters upon the ozone concentration using artificial neural networks. Iranian Journal of Health and Environment, 6(1), 11-22.

Ghorbani, R. & Esmaiili, M. (2022). Investigation of the effect of ultrasound pretreatment on shrinkage of cornelian cherry during hot air drying. Journal of Food Science and Technology, 19(123), 15-26. https://doi.org/10.52547/fsct.19.123.15.

Gitiban, A. & Asefi, N. (2019). Modeling of hardness and drying kinetics of "quince" fruit drying in an infrared convection dryer using the artificial neural network. Iranian Food Science and Technology Research Journal, 15(4), 465-475. https://doi.org/10.22067/ifstrj.v15i4.76323.

Hosseini, Z. (2006). Common Methods in Food Analysis. Shiraz University Pub.

Hu, T., Subbiah, V., Wu, H., Bk, A., Rauf, A., Alhumaydhi, F.A. & Suleria, H.A.R. (2021). Determination and characterization of phenolic compounds from australia-grown sweet cherries (Prunus avium L.) and their potential antioxidant properties. ACS Omega 6(50), 34687-34699. https://doi.org/10.1021/acsomega.1c05112.

Karami, H., Nejat Lorestani, A. & Tahvilian, R. (2021). The effect of different drying methods on drying kinetics, mathematical modeling, quantity and quality of thyme essential oil. Journal of Food Science and Technology, 18(113), 135-146. https://doi.org/10.52547/fsct.18.113.135.

Kroehnke, J., Szadzińska, J., Radziejewska-Kubzdela, E., Biegańska-Marecik, R., Musielak, G. & Mierzwa, D. (2021). Osmotic dehydration and convective drying of kiwifruit (Actinidia deliciosa) – The influence of ultrasound on process kinetics and product quality. Ultrasonics Sonochemistry, 71, 105377. https://doi.org/10.1016/j.ultsonch.2020.105377.

Onwude, D.I., Hashim, N., Janius, R.B., Nawi, N. & Abdan, K. (2016). Modelling the convective drying process of pumpkin (Cucurbita moschata) using an artificial neural network. International Food Research Journal 23, S237.

Salehi, F. (2020a). Physico-chemical properties of fruit and vegetable juices as affected by ultrasound: A review. International Journal of Food Properties, 23(1), 1748-1765. https://doi.org/10.1080/10942912.2020.1825486.

Salehi, F. (2020b). Recent advances in the modeling and predicting quality parameters of fruits and vegetables during postharvest storage: A review. International Journal of Fruit Science, 20(3), 506-520. https://doi.org/10.1080/15538362.2019.1653810.

Salehi, F. (2021). Recent applications of heat pump dryer for drying of fruit crops: A review. International Journal of Fruit Science, 21(1), 546-555. https://doi.org/10.1080/15538362.2021.1911746.

Salehi, F. (2023). Recent advances in the ultrasound-assisted osmotic dehydration of agricultural products: A review. Food Bioscience, 51, 102307. https://doi.org/10.1016/j.fbio.2022.102307.

Salehi, F., Cheraghi, R. & Rasouli, M. (2022). Influence of sonication power and time on the osmotic dehydration process efficiency of banana slices. Journal of Food Science and Technology, 19(124), 197-206. https://doi.org/10.52547/fsct.19.124.197.

Satorabi, M., Salehi, F. & Rasouli, M. (2021). The influence of xanthan and balangu seed gums coats on the kinetics of infrared drying of apricot slices: GA-ANN and ANFIS modeling. International Journal of Fruit Science, 21(1), 468-480. https://doi.org/10.1080/15538362.2021.1898520.

Shahidi, F. & Maleki Aysak, M. (2017). Studying the influence of ultrasound treatment on osmosis dehydration of turnip and optimization of hot-air drying conditions. Journal of Food Science and Technology, 14(68), 203-2014.

Vursavuş, K., Kelebek, H. & Selli, S. (2006). A study on some chemical and physico-mechanic properties of three sweet cherry varieties (Prunus avium L.) in Turkey. Journal of Food Engineering, 74(4), 568-575. https://doi.org/10.1016/j.jfoodeng.2005.03.059.

Xu, B., Sylvain Tiliwa, E., Wei, B., Wang, B., Hu, Y., Zhang, L., Mujumdar, A.S., Zhou, C. & Ma, H. (2022). Multi-frequency power ultrasound as a novel approach improves intermediate-wave infrared drying process and quality attributes of pineapple slices. Ultrasonics Sonochemistry, 88, 106083. https://doi.org/10.1016/j.ultsonch.2022.106083.

Yusefi, A., Dilmaghanian, S., Ziaforoughi, A. & Moezzi, M. (2019). Study on infrared drying kinetics of quince slices and modelling of drying process using genetic algorithm-artificial neural networks (GA-ANNs). Innovative Food Technologies, 6(2), 175-186. https://doi.org/10.22104/jift.2018.2871.1694.