بررسی اثر پیش‌تیمار فراصوت بر سرعت خشک شدن گیلاس و مدل‌سازی فرآیند توسط روش الگوریتم ژنتیک- شبکه عصبی مصنوعی
محورهای موضوعی : روشهای نگهداری مواد غذایی
فخرالدین صالحی
1
(دانشیار گروه علوم و صنایع غذایی، دانشگاه بوعلی سینا، همدان، ایران)
معین اینانلودوقوز
2
(دانشجوی کارشناسی ارشد گروه علوم و صنایع غذایی، دانشگاه بوعلی سینا، همدان، ایران)
سارا قزوینه
3
(دانشجوی کارشناسی گروه علوم و صنایع غذایی، دانشگاه بوعلی سینا، همدان، ایران)
کلید واژه: گیلاس, آنالیز حساسیت, تابع فعال‌سازی, آبگیری مجدد, فراصوت,
چکیده مقاله :
مقدمه: به دلیل رطوبت بالا، سرعت فساد گیلاس بسیار زیاد است و برای نگهداری مؤثر نیاز به استفاده از برخی تیمارهای پس از برداشت دارد. خشک‌کردن یکی از این روشهای نگهداری است. امواج فراصوت را می‌توان به‌عنوان یک پیش‌تیمار قبل از خشک‌کردن محصولات کشاورزی به‌منظور کاهش زمان این فرآیند استفاده نمود. روش الگوریتم ژنتیک- شبکه عصبی مصنوعی دارای قابلیت بالایی برای یافتن مقدار بهینه یک تابع هدف پیچیده است.مواد و روش‌ها: در این پژوهش اثر تیماردهی با امواج فراصوت به مدت 0، 3، 6 و 9 دقیقه بر زمان خشک شدن، تغییرات وزن و آبگیری مجدد گیلاس بررسی شد. در مرحله بعد، این فرآیند توسط روش الگوریتم ژنتیک- شبکه عصبی مصنوعی با 2 ورودی (زمان خشک‌کردن و زمان پیش‌تیمار فراصوت) و 1 خروجی (درصد کاهش وزن) مدل‌سازی شد.یافته‌ها: نتایج این پژوهش نشان داد که تیمار فراصوت تا 3 دقیقه، سبب افزایش سرعت خروج رطوبت از گیلاس‌ها و در نتیجه باعث کاهش زمان خشک‌کردن می‌گردد. تیماردهی با امواج فراصوت به مدت 3 دقیقه باعث افزایش آبگیری مجدد گیلاس خشک شده شد؛ اما با افزایش زمان تیماردهی به 6 و 9 دقیقه مقدار آبگیری مجدد کاهش یافت. نتایج مدل‌سازی به روش الگوریتم ژنتیک- شبکه عصبی مصنوعی نشان داد شبکه‌ای با ساختار 1-4-2 در یک لایه پنهان و با استفاده از تابع فعال‌سازی تانژانت هیپربولیک می‌تواند درصد کاهش وزن گیلاس هنگام خشک شدن را با ضریب همبستگی بالا و مقدار خطا پایین پیش‌بینی نماید. بر اساس نتایج آزمون آنالیز حساسیت، زمان خشک‌کردن به‌عنوان مؤثرترین عوامل در تغییر درصد کاهش وزن گیلاس طی فرآیند خشک‌کردن بود.نتیجه‌گیری: به‌طورکلی، بهترین شرایط برای خشک‌کردن گیلاس، 3 دقیقه پیش‌تیمار با فراصوت و سپس خشک‌کردن محصول با هوای داغ است. با توجه نتایج به دست آمده از مدل‌سازی، از روش الگوریتم ژنتیک- شبکه عصبی مصنوعی نیز می‌توان برای پیش‌بینی پارامترهای فرآیند خشک‌کردن گیلاس استفاده نمود.
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.
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.
_||_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.