نوع مقاله : مقاله پژوهشی
نویسندگان
گروه علوم و مهندسی صنایع غذایی، دانشکده کشاورزی، دانشگاه زنجان، زنجان، ایران
چکیده
در دهه‌های اخیر، از سیستم‌های هوش مصنوعی برای ایجاد مدل‌های پیش‌بینی جهت تخمین و پیش‌بینی بسیاری از فرآیندهای کشاورزی استفاده شده است. در این مطالعه، خصوصیات فیزیکی و شیمیایی میوه زالزالک طی نگهداری در شرایط مختلف با استفاده از شبکه‌های عصبی مصنوعی و سیستم استنتاج عصبی-فازی سازگار پیش‌بینی گردید. از داده‌های تجربی حاصل از نگهداری میوه، برای آموزش و آزمایش این شبکه‌ها استفاده شد. تعداد کل لایه‌های پنهان و تعداد نورون در هر لایه پنهان به روش سعی و خطا انتخاب گردید. شبکه عصبی و سیستم استنتاج عصبی-فازی سازگار طراحی شده دارای ورودی شامل زمان نگهداری، رطوبت اولیه و دمای نگهداری و یک متغیر در لایه‌های خروجی (WL، F،c* ، *h و RPI) بود. مقادیر R2 بالا و RMSE کم گویای کارایی بالای مدل شبکه عصبی مصنوعی و سیستم استنتاج عصبی-فازی سازگار در پیش‌بینی خصوصیات کیفی زالزالک طی فرآیند نگهداری می‌باشد. نتایج نشان داد که شبکه عصبی پرسپترون چندلایه با الگوریتم یادگیری مومنتوم و تابع آستانه‌ای تان‌اکسون بهترین شبکه برای پیش‌بینی خصوصیات کیفی زالزالک در شرایط مختلف بود. نتایج مدل‌سازی با انفیس نشان داد که توابع عضویت ذوزنقه‌ای و گوسی بهترین عملکرد را به‌ترتیب در پیش‌بینی پارامترهای رنگی و فیزیکی داشت. با مقایسه نتایج حاصل از مدل‌سازی با شبکه عصبی مصنوعی و انفیس، تفاوت زیادی از نظر دقت و کارایی در پیش‌بینی مشاهده نشد، اگرچه شاخص RMSE در مدل‌سازی با کمک انفیس کمتر از شبکه عصبی مصنوعی بود که خود نمایان‌گر دقت بالاتر آن می‌باشد.
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1. Abbaspour Gilandeh, Y., A. Jahanbakhshi, and M. Kaveh. 2020. Prediction kinetic, energy and exergy of quince under hot air dryer using ANNs and ANFIS. Food Science & Nutrition 8: 594-611.
2. Ahangarnezhad, N., G. Najafi, and A. Jahanbakhshi. 2019. Determination of the physical and mechanical properties of a potato (the Agria variety) in order to mechanise the harvesting and post-harvesting operations. Research in Agricultural Engineering 65: 33-39.
3. Amiri Chayjan, R., M. Kaveh, and S. Khayati. 2015. Modeling Drying Characteristics of Hawthorn Fruit under Microwave Convective Conditions. Journal of Food Processing and Preservation 39: 239-253.
4. Aral, S., and A. Vildan Bese. 2016. Convective drying of hawthorn fruit (Crataegus spp.): Effect of experimental parameters on drying kinetics, color, shrinkage, and rehydration capacity. Food Chemistry 210: 577-584.
5. Arzate-Vazquez, I., J. J. Chanona-Perez, M. D. J. Perea-Flores, G. Calderon-Domı´nguez, M. Moreno-Armendariz, and G. Gutierrez-Lopez. 2011. Image processing applied to classification of avocado variety Hass (Persea americana Mill) during the ripening process. Food and Bioprocess Technology 4: 1307-1313.
6. Asghari, M. R., R. Ebrahimi, B. Hosseinzadeh, and D. Ghanbarian. 2017. Mulberry qualitative pramaters modelling in drying process using artificial neural networks. Iranian Journal of Biosystems Engineering 48: 9-18. (In Farsi).
7. Ashournezhad, M., and M. Ghasemnezhad. 2012. Effects of cellophane-film packaging and cold storage on the keeping quality and storage life of loquat fruit (Eriobotrya japonica). Iranian Journal of Nutrition Sciences & Food Technology 7: 95-102. (In Farsi).
8. Azarmdel, H., A. Jahanbakhshi, S. S. Mohtasebi, and A. R. Muñoz. 2020. Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM). Postharvest Biology and Technology 166: 111201.
9. Billy, L., E. Mehinagic, G. Royer, C. M. G. C. Renard, G. Arvisenet, and C. Prost. 2008. Relationship between texture and pectin composition of two apple cultivars during storage. Postharvest Biology and Technology 47: 315-324.
10. Cardenas-Perez, S., J. Chanona-Perez, J. V. Mendez-Mendez, G. Calderon-Domınguez, R. Lopez-Santiago, M. A. J. Perea-Flores, and I. Arzate-Vazquez. 2017. Evaluation of the ripening stages of apple (Golden Delicious) by means of computer vision system. Biosystems Engineering 159: 46-58.
11. Derili gharjalar, S., H. Hassanpour, and A. Farokhzad. 2017. Pomological characteristics of some hawthorn genotypes in West Azerbaijan province. Iranian Journal of Horticultural Science 48: 689-700.
12. Dhakala, S., V. M. Balasubramaniam, H. Ayvaza, and L. E. Rodriguez-Saona. 2018. Kinetic modeling of ascorbic acid degradation of pineapple juice subjected to combined pressure-thermal treatment. Journal of Food Engineering 224: 62-70.
13. Farzan, E., M. R. Rahimi, and V. Madadi Avargani. 2017. Drying kinetic and shrinkage study of a Hawthorn sample in a vibro fluidized bed dryer using an adsorption system in order to control of inlet air humidity. Journal of Innovative Food Technologies 107-122. (In Farsi).
14. Guadarrama, A., and S. Andrade. 2012. Physical, chemical and biochemical changes of Sweetsop (Annona squamosa L.) and golden apple (Spondias citherea Sonner) fruits during ripening. Journal of Agricultural Science and Technology 1148-1157.
15. Helrich, K. 1990. AOAC Official Methods of Analysis. Official Methods of Analysis of the AOAC International. Gaithersburg: Association of official analytical chemists Inc (AOAC International).
16. Jahanbakhshi, A., R. Yeganeh, and G. Shahgoli. 2019a. Determination of mechanical properties of banana fruit under quasi-static loading in pressure, bending, and shearing tests. International Journal of Fruit Science: 1-9
17. Jahanbakhshi, A., Y. Abbaspour Gilandeh, B. Ghamari, and K. Heidarbeigi. 2019b. Assessment of physical, mechanical, and hydrodynamic properties in reducing postharvest losses of cantaloupe (Cucumis melo var. Cantaloupensis). Journal of Food Process Engineering 42: e13091.
18. Jahanbakhshi, A., V. Rasooli Sharabiani, K. Heidarbeigi, M. Kaveh, and E. Taghinezhad. 2019c. Evaluation of engineering properties for waste control of tomato during harvesting and postharvesting. Food Science & Nutrition 7: 1473-1481.
19. Jahangiri-Saleh, M., S. R. Hassan-Beygi, M. Aboonajmi, and M. Lotfi. 2017. Prediction of Cucumber Acoustic Response, CrispnessIndex and Firmness Using Artificial Neural Networks. Food Science and Technology 14: 265-276. (In Farsi).
20. Kami, D., L. Shi, T. Sato, T. Suzuki, and K. Oosawa. 2009. Cryopreservation of shoot apices of hawthorn in vitro cultures originating from East Asia. Scientia Horticulturae 120: 84-88.
21. Karaman, S., I. Ozturk, H. Yalcin, A. Kayacier, and O. Sagdic. 2012. Comparison of adaptive neuro fuzzy inference system and artificial neural networks for estimation of oxidation parameters of sunflower oil added with some natural byproduct extracts. Journal of the Science of Food and Agriculture 92: 49-58.
22. Kaveh, M., Y. Abbaspour-Gilandeh, R. A. Chayjan, and R. Mohammadigol. 2019. Comparison of Mathematical Modeling, Artificial Neural Networks and Fuzzy Logic for Predicting the Moisture Ratio of Garlic and Shallot in a Fluidized Bed Dryer. Journal of Agricultural Machinery 9. (In Farsi).
23. Khayati, S., and R. A. Chayjan. 2016. Prediction of some thermal, physical and mechanical properties of terebinth fruit after semi-industrial continuous drying using artificial neural networks. Food Science and Technology 13: 161-172.
24. Li, T., J. Zhu, L. Guo, X. Shi, Y. Liu, and X. Yang. 2013. Differential effects of polyphenols-enriched extracts from hawthorn fruit peels and fleshes on cell cycle and apoptosis in human MCF-7 breast carcinoma cells. Food Chemistry 141: 1008-1018.
25. Li, W.-Q., Q.-P. Hu, and J.-G. Xu. 2015. Changes in physicochemical characteristics and free amino acids of hawthorn (Crataegus pinnatifida) fruits during maturation. Food Chemistry 175: 50-56.
26. Liu, P., B. Yang, and H. Kallio. 2010. Characterization of phenolic compounds in Chinese hawthorn (Crataegus pinnatifida Bge. var. major) fruit by high performance liquid chromatography–electrospray ionization mass spectrometry. Food Chemistry 121: 1188-1197.
27. Liu, P., H. Kallio, and B. Yang. 2011a. Phenolic compounds in hawthorn (Crataegus grayana) fruits and leaves and changes during fruit ripening. Journal of Agricultural and Food Chemistry 59: 11141-11149.
28. Liu, P., H. Kallio, D. Lü, C. Zhou, and B. Yang. 2011b. Quantitative analysis of phenolic compounds in Chinese hawthorn (Crataegus spp.) fruits by high performance liquid chromatography-electrospray ionisation mass spectrometry. Food Chemistry 127: 1370-1377.
29. Luo, Y., G. Chen, B. Li, B. Ji, Y. Guo, and F. Tia. 2009. Evaluation of antioxidative and hypolipidemic properties of a novel functional diet formulation of Auricularia auricula and Hawthorn. Innovative Food Science and Emerging Technologies 10: 215-221.
30. Majidzadeh, H., B. Emadi, and A. A. Farzad. 2015. Prediction the moisture content of kiwifruit in vacuum drier using artificial neural network. Iranian Food Science and Technology Research Journal 11: 107-117. (In Farsi).
31. Menz, G., and F. Vriesekoop. 2010. Physical and chemical changes during the maturation of gordal sevillana olives (Olea europaea L., cv. Gordal Sevillana). Journal of Agricultural and Food Chemistry 58: 4934-4938.
32. Mohammadigol, R., F. Azadshahraki, and V. Lotfi. 2017. Quantification of total phenol in grape by near infrared spectroscopy and artificial neural network. Journal of Research and Innovation in Food Science and Technology 6: 313-320. (In Farsi).
33. Mraihi, F., M. Hidalgo, S. Pascual-Teresa, M. Trabelsi-Ayadi, and J. K. Cherif. 2015. Wild grown red and yell ow Hawthorn fruits from Tunisia as source of antioxidants. Arabian Journal of Chemistry 8: 570-578.
34. Nordey, T., M. Lechaudel, M. Genard, and J. Joas. 2014. Spatial and temporal variations in mango colour, acidity, and sweetness in relation to temperature and ethylene gradients within the fruit. Journal of Plant Physiology 171: 1555-1563.
35. Opara, U. L., and M. R. Al-Ani, and N. M. Al-Rahbi. 2012. Effect of fruit ripening stage on physico-chemical properties, nutritional composition and antioxidant components of tomato (Lycopersicum esculentum) cultivars. Food and Bioprocess Technology 5: 3236-3243.
36. Razavi, F., M. Roghayeh, V. Rabiei, M. Soleimani Aghdam, and A. Soleimani. 2018. Glycine betaine treatment attenuates chilling injury and maintains nutritional quality of hawthorn fruit during storage at low temperature. Scientia Horticulturae 233: 188-194.
37. Velez-Rivera, N., J. Blasco, J. Chanona-Perez, G. Calderon-Dominguez, M. D. J. Perea-Flores, I. Arzate-Vazquez, S. Cubero, and R. Farrera-Rebollo. 2014. Computer Vision System Applied to Classification of “Manila” Mangoes During Ripening Process. Food and Bioprocess Technology 7: 1183-1194.
38. Wang, H., F. Chen, H. Yang, Y. Chen, L. Zhang, and H. An. 2012. Effects of ripening stage and cultivar on physicochemical properties and pectin nanostructures of jujubes. Carbohydrate Polymers 89: 1180-1188.
39. Wen, L., X. Guo, R. Hai Liu, L. You, A. Mehmood Abbasi, and X. Fu. 2015. Phenolic contents and cellular antioxidant activity of Chinese hawthorn “Crataegus pinnatifida”. Food Chemistry: 54-62.
40. Zheng, H. Z., Y. I. Kim, and S. K. Chung. 2012. A profile of physicochemical and antioxidant changes during fruit growth for the utilisation of unripe apples. Food Chemistry 131: 106-110.
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