| تعداد نشریات | 282 |
| تعداد نشریات سازمان | 80 |
| تعداد شمارهها | 3,220 |
| تعداد مقالات | 35,690 |
| تعداد مشاهده مقاله | 53,577,693 |
| تعداد دریافت فایل اصل مقاله | 92,856,546 |
مقایسه شبکه عصبی مصنوعی و روش شناسی سطح پاسخ در پیشبینی تغییرات برخی از ویژگیهای میوه کندس خشک شده به روش اسمز با پیش تیمار مایکروویو | ||
| تحقیقات مهندسی صنایع غذایی | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 01 تیر 1405 | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/fooder.2026.371675.1441 | ||
| نویسندگان | ||
| علیرضا قدس ولی* 1؛ محمد گنجه2؛ معصومه مقیمی3؛ حمید بخش آبادی2 | ||
| 1بخش تحقیقات فنی ومهندسی کشاورزی- مرکز گلستان | ||
| 2گروه کشاورزی، مرکز آموزش عالی میناب، دانشگاه هرمزگان، بندرعباس، ایران. | ||
| 3گروه شیمی، واحد گنبد کاووس، دانشگاه آزاد اسلامی، گنبد کاووس، ایران | ||
| چکیده | ||
| امروزه استفاده از روشهای غیر حرارتی، مانند خشک کردن اسمزی و آبگیری با استفاده از فراصوت و مایکروویو برای تولید محصولات غذایی افزایش یافته است، در این راستا این مطالعه با هدف پیشبینی روند تغییرات برخی از ویژگیهای میوة کُندس خشک شده به روش اسمز با پیشتیمار مایکروویو تحت تاثیر زمانهای مختلف مایکروویو (0 تا 4 دقیقه)، دماهای (40 تا 60 درجه سلسیوس) و زمان فرآیند اسمز (60 تا 240 دقیقه) اجرا شد. برای پیشبینی روند تغییرات این ویژگیها از روششناسی سطح پاسخ با یک طرح مرکب مرکزی و شبکه عصب مصنوعی استفاده شد. بهمنظور کمینه کردن میزان جذب مواد جامد و بیشینه کردن درصد کاهش آب و نسبت دفع آب به جذب مواد جامد مشخص شد که باید زمان مایکروویو، دما و زمان فرآیند اسمز بهترتیب 1.69 دقیقه، 60 درجه سلسیوس و 240 دقیقه باشد. تحت این شرایط میزان جذب مواد جامد (15.17 درصد)، کاهش وزن آب (24.21 درصد)، نسبت دفع آب به جذب مواد جامد (1.63) و کاهش وزن (10.00 درصد) است. با بررسی شبکههای مختلف، شبکه پسانتشار پیشخور با توپولوژیهای 4-5-3 با ضریب همبستگی بیشتر از 0.996 و میانگین مربعات خطای کمتر از 001/0 و با بکارگیری تابع فعالسازی تانژانت هیپربولیکی، الگوی یادگیری جهنده و چرخة یادگیری 1000 به عنوان بهترین مدل عصبی مشخص گردید. از طرفی با مقایسة ضریبهای همبستگی مدلهای حاصل از روش سطح پاسخ و شبکة عصب مصنوعی مشخص شد که روش شبکة عصب مصنوعی کارایی بیشتری برای پیشبینی روند تغییرات ویژگیهای میوة کُندس خشک شده با فرآیند اسمز با پیش تیمار مایکروویو دارد. | ||
| کلیدواژهها | ||
| اسمز؛ خشککردن؛ شبکه عصب مصنوعی؛ کُندس؛ مایکروویو | ||
| عنوان مقاله [English] | ||
| Comparison of Artificial Neural Network and Response Surface Methodology in Predicting Changes in Some Characteristics of Osmotic Dried Candied Fruit with Microwave Pretreatment | ||
| نویسندگان [English] | ||
| Mohammad Ganje2؛ Masumeh Moghimi3؛ Hamid Bakhshabadi2؛ | ||
| 2Department of Agriculture, Minab Higher Education Center, University of Hormozgan, Bandar Abbas, Iran | ||
| 3Department of Chemistry,Gonbad Kavoos Branch, Islamic Azad University, Gonbad Kavoos,Iran. | ||
| چکیده [English] | ||
| Today, the use of non-thermal methods such as osmotic drying, dehydration using ultrasound, and microwave has increased for the production of food products. This study aimed to predict the trend of changes in some characteristics of osmotic dried medlar fruit with microwave pretreatment under the influence of different microwave times (0 to 4 min), temperatures (40 to 60°C), and osmotic process times (60 to 240 min). To predict the trend of changes in these characteristics, response surface methodology with a central composite design and artificial neural network was utilized. It was determined that the optimal microwave time, temperature and osmotic process time should be 1.69 min, 60°C, and 240 min, respectively,so to minimize the solid gain and maximize the percentage of water loss and the of water loss/solid gain. Under these conditions, the solid gain was 15.17%, water loss was 24.21%, the water loss/solid gain was 1.63% and weight reduction was 10.00%. Examination of different networks, the feedforward back-diffusion network with 4-5-3 topologies was found to be the best neural model, with a correlation coefficient greater than 0.996 and a mean square error less than 0.001. The hyperbolic tangent activation function, hopping learning model, and 1000 learning cycles were also determined to be optimal. Comparing the correlation coefficients of the models obtained from the response surface method and the artificial neural network, it was concluded that the artificial neural network method is more effective in predicting the trend of changes of dried candied through the osmotic process with microwave . | ||
| کلیدواژهها [English] | ||
| Artificial neural network, Medlar, Drying, Microwave, Osmotic | ||
| مراجع | ||
|
Alp, D., and Bulantekin, Ö. 2021. The microbiological quality of various foods dried by applying different drying methods: a review. European Food Research and Technology, 247(6), 1333-1343. Angilelli, K. G., Orives, J. R., da Silva, H. C., Coppo, R. L., Moreira, I., and Borsato, D. 2015. Multicomponent diffusion during osmotic dehydration process in melon pieces: influence of film coefficient. Journal of Food Processing and Preservation, 39(4), 329-337. Asghari, A., Zongo, P. A., Osse, E. F., Aghajanzadeh, S., Raghavan, V., and Khalloufi, S. 2024. Review of osmotic dehydration: Promising technologies for enhancing products’ attributes, opportunities, and challenges for the food industries. Comprehensive Reviews in Food Science and Food Safety, 23(3), e13346. Ataee, M., Khakbaz Heshmati, M., Dehghannya, J., and Dadashi, S. 2022. Comparison of drying method (Convective hot air, Microwave, Convective-microwave, Microwave-convective) on drying kinetic of broccoli foam. Food Research Journal, 32(2), 73-87. Bajželj, B., Quested, T. E., Röös, E., and Swannell, R. P. 2020. The role of reducing food waste for resilient food systems. Ecosystem services, 45, 101140. Bakhshabadi, H., Ganje, M., Gharekhani, M., Mohammadi-Moghaddam, T., Aulestia, C., and Morshedi, A. 2025. A review of new methods for extracting oil from plants to enhance the efficiency and physicochemical properties of the extracted oils. Processes, 13(4), 1124. Bakhshabadi, H., Ganje, M., Moghimi, M., Ghodsvali, A., and& Mohammadi-Moghaddam, T. 2024. Modeling and Optimization of the Osmotic Dehydration of Cantaloupe. J Food Chem Nanotechnol, 10(4), 192-199. Bakhshabadi, H., Mirzaei, H., Ghodsvali, A., Jafari, S. M., Ziaiifar, A. M., and Farzaneh, V. 2017. The effect of microwave pretreatment on some physico-chemical properties and bioactivity of Black cumin seeds’ oil. Industrial crops and products, 97, 1-9. Bakhshabadi, H., Moghimi, M., Dolatabadi, Z., and Asgharipour, S. 2020. Optimization of the osmotic dehydration process of nectarine and estimation of dehydration parameters by use of artificial nature network. Chen, H. Y., and Chen, C. 2025. A study of the response surface methodology model with regression analysis in three fields of engineering. Applied system innovation, 8(4), 99. Çınkır, N. İ., and Süfer, Ö. 2020. Microwave drying of TURKISH red meat (watermelon) radish (RAPHANUS SATIVUS l.): effect of osmotic dehydration, pre-treatment and slice thickness. Heat and Mass Transfer, 56(12), 3303-3313. Dursun Saydam, D. 2025. Optimizing Microwave‐Convectional Drying of Probiotic‐Infused Apple Snacks: Impact on Quality Attributes and Predictive Modeling with Equations and Artificial Neural Network. Journal of Food Science, 90(6), e70348. Erfaniannejad Hosseini Nabadou, F., Moghimi, M., Tahmasebi, A., and Bakhshabadi, H. 2025. Optimization of Millet Malting Parameters Using Artificial Neural Network and Response Surface Methodology. Food Science & Nutrition, 13(5), e70214. Francik, S., Łapczyńska-Kordon, B., Hajos, M., Basista, G., Zawiślak, A., and Francik, R. 2024. Modeling the Drying Process of Onion Slices Using Artificial Neural Networks. Energies, 17(13), 3199. Glew, R. H., Ayaz, F. A., Sanz, C., VanderJagt, D. J., Huang, H. S., Chuang, L. T., and Strnad, M. 2003. Changes in sugars, organic acids and amino acids in medlar (Mespilus germanica L.) during fruit development and maturation. Food chemistry, 83(3), 363-369. Haque, M. R., Hosain, M. M., Kamal, M. M., Islam, M. R., and Mondal, S. C. 2020. Optimization of osmotic dehydration parameters of banana under pulsed microwave conditions by response surface methodology. Journal of microbiology, biotechnology and food sciences, 9(6), 1053-1059. Harish, A., Vivek, B. S., Sushma, R., Monisha, J., and Krishna Murthy, T. P. 2014. Effect of microwave power and sample thickness on microwave drying kinetics elephant foot yam (Amorphophallus Paeoniifolius). American Journal of Food Science and Technology, 2(1), 28-35. Lin, R., Wang, B., Li, B., Zeng, S., Lv, W., and Xiao, H. 2025. Microwave‐assisted drying techniques to enhance the drying efficiency and quality of dried Gastrodia elata. Journal of the Science of Food and Agriculture, 105(8), 4537-4548. Manivannan, P., and Rajasimman, M. 2011. Optimization of process parameters for the osmotic dehydration of beetroot in sugar solution. Journal of Food Process Engineering, 34(3), 804-825. Manzoor, A., Khan, M. A., Mujeebu, M. A., and Shiekh, R. A. 2021. Comparative study of microwave assisted and conventional osmotic dehydration of apple cubes at a constant temperature. Journal of Agriculture and Food Research, 5, 100176. Mari, A., Parisouli, D. N., and Krokida, M. 2024. Exploring osmotic dehydration for food preservation: Methods, modelling, and modern applications. Foods, 13(17), 2783. Nistor, D. I., Marc, R. A., and Mureșan, C. C. 2024. Phytochemistry, nutritional composition, health benefits and future prospects of Mespilus germanica L.(Medlar): A review. Food Chemistry: X, 22, 101334. Onu, C. E., Igbokwe, P. K., Nwabanne, J. T., and Ohale, P. E. 2022. ANFIS, ANN, and RSM modeling of moisture content reduction of cocoyam slices. Journal of Food Processing and Preservation, 46(1), e16032. Pavkov , I., Radojčin, M., Stamenković, Z., Kešelj, K., Tylewicz, U., Sipos, P., and Sedlar, A. 2021. Effects of osmotic dehydration on the hot air drying of apricot halves: Drying kinetics, mass transfer, and shrinkage. Processes, 9(2), 202. Pardo, M., and Leiva, A. 2009. Combination of technologies for the dehydration of pineapple. Ingeniería y Competitividad, 11(1), 57-65. Ramírez, C., Núñez, H., Vallejos, R., Belmonte, K., Almonacid, S., and Marra, F. 2025. Simultaneous Application of Refractance Window and Microwave Drying: A Novel Hybrid Technique for Fruit Dehydration to reducing drying time and improve bioactive compound retention. Future Foods, 100709. Reyes, V., Cahill, E., and Mis Solval, K. E. 2024. The potential for reducing food waste through shelf-life extension: Actionable insights from data digitization. Sustainability, 16(7), 2986. Salehi, F., and Kashani Nejad, M. 2017. Physicochemical and Rheological Properties of Wild Medlar Concentrate. Journal of food science and technology (Iran), 13(59), 49-57. Song, C., Ma, X., Li, Z., Wu, T., Raghavan, G. V., and Chen, H. 2020. Mass transfer during osmotic dehydration and its effect on anthocyanin retention of microwave vacuum‐dried blackberries. Journal of the Science of Food and Agriculture, 100(1), 102-109. Voaides, C., Radu, N., Birza, E., and Babeanu, N. 2021. Medlar—A comprehensive and integrative review. Plants, 10(11), 2344. Zhao, S. Y., An, N. N., Zhang, K. Y., Li, D., Wang, L. J., and Wang, Y. 2023. Evaluation of drying kinetics, physical properties, bioactive compounds, antioxidant activity and microstructure of Acanthopanax sessiliflorus fruits dried by microwave-assisted hot air drying method. Journal of Food Engineering, 357, 111642. | ||
|
آمار تعداد مشاهده مقاله: 143 تعداد دریافت فایل اصل مقاله: 25 |
||