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Machine Vision Approach Coupled with a Hybrid EHD-Convective Dryer to Model Khalal Slices Drying Process with ANFIS | ||
| Biomechanism and Bioenergy Research | ||
| دوره 4، شماره 2 - شماره پیاپی 8، شهریور 2025، صفحه 11-25 اصل مقاله (782.42 K) | ||
| نوع مقاله: Original Research | ||
| شناسه دیجیتال (DOI): 10.22103/bbr.2025.24768.1116 | ||
| نویسندگان | ||
| Aydin Imani1؛ Seyed Saeid Mohtasebi* 2؛ Alireza Khoshroo3؛ Mahdi Keramat-Jahromi4 | ||
| 1Department of Soil Science, Faculty of Agriculture, Urmia University, Urmia, Iran. | ||
| 2Department of Agricultural Machinery Engineering, Faculty of Agriculture, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. | ||
| 3Department of Agronomy and Plant Breeding, Faculty of Agriculture, Yasouj University, Yasouj, Iran. | ||
| 4Department of Biosystems Engineering, Faculty of Agriculture, Shiraz University, Shiraz, Iran. | ||
| چکیده | ||
| Khalal is a product of date palm fruit before full ripeness and has a higher moisture content than Rutab and fully ripened date fruit. This study deals with monitoring the real-time drying process of Khalal thin slices in a hybrid electro-hydrodynamic (EHD)-convective hot air dryer. The real-time moisture ratio (MR) of Khalal slices was estimated with an intelligent online machine vision system and eliminating the conventional weighing system was investigated. For this purpose, the samples were photographed at specified time intervals during the drying process. An adaptive neuro-fuzzy inference system (ANFIS) was developed to extract real-time models for the drying process. The input features contained different combinations of the temperature of the chamber, air velocity, and drying time along with the L*, a*, and b* coefficients of the image were calculated at different times. The performance of the developed models was evaluated, and the best model was selected. The results revealed that the differential sigmoid membership function with six inputs can provide the best estimation for the moisture ratio (MR) of the product with the coefficient of determination of 0.988 and 0.987 for train and test data, respectively. Finally, it is concluded that the proposed online model can eliminate the need for an embedded weighing system through intelligent control of the EHD-convective dryer and provide a robust real-time prediction of the MR of Khalal thin slices. | ||
| کلیدواژهها | ||
| Image processing؛ EHD؛ Khalal؛ Date palm fruit؛ Drying؛ Moisture ratio؛ ANFIS | ||
| مراجع | ||
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Aghbashlo, M., Hosseinpour, S., & Ghasemi-Varnamkhasti, M. (2014). Computer vision technology for real-time food quality assurance during drying process. Trends in Food Science and Technology, 39(1), 76–84. https://doi.org/10.1016/j.tifs.2014.06.003
Aghilinategh, N., Rafiee, S., Hosseinpour, S., Omid, M., & Mohtasebi, S. S. (2016). Real-time color change monitoring of apple slices using image processing during intermittent microwave convective drying. Food Science and Technology International, 22(7), 634–646. https://doi.org/10.1177/1082013216636263
Anukiruthika, T., Moses, J. A., & Anandharamakrishnan, C. (2021). Electrohydrodynamic drying of foods: Principle, applications, and prospects. Journal of Food Engineering, 295, 110449. https://doi.org/10.1016/j.jfoodeng.2020.110449
Bashkir, I., Defraeye, T., Kudra, T., & Martynenko, A. (2020). Electrohydrodynamic Drying of Plant-Based Foods and Food Model Systems. Food Engineering Reviews, 12(4), 473–497. https://doi.org/10.1007/s12393-020-09229-w
Benalia, S., Cubero, S., Prats-Montalbán, J. M., Bernardi, B., Zimbalatti, G., & Blasco, J. (2016). Computer vision for automatic quality inspection of dried figs (Ficus carica L.) in real-time. Computers and Electronics in Agriculture, 120, 17–25. https://doi.org/10.1016/j.compag.2015.11.002
Bora, G. C., Pathak, R., Ahmadi, M., & Mistry, P. (2018). Image processing analysis to track colour changes on apple and correlate to moisture content in drying stages. Food Quality and Safety, 2(2), 105–110. https://doi.org/10.1093/fqsafe/fyy003
Esehaghbeygi, A., Pirnazari, K., & Sadeghi, M. (2014). Quality assessment of electrohydrodynamic and microwave dehydrated banana slices. Lwt -Food Science and Technology, 55(2), 565–571. https://doi.org/10.1016/j.lwt.2013.10.010
Gao, K., Zhou, L., Bi, J., Yi, J., Wu, X., Zhou, M., Wang, X., & Liu, X. (2017). Evaluation of browning ratio in an image analysis of apple slices at different stages of instant controlled pressure drop-assisted hot-air drying (AD-DIC). Journal of the Science of Food and Agriculture, 97(8), 2533–2540. https://doi.org/10.1002/jsfa.8070
Ghaitaranpour, A., Rastegar, A., Tabatabaei Yazdi, F., Mohebbi, M., & Alizadeh Behbahani, B. (2017). Application of Digital Image Processing in Monitoring some Physical Properties of Tarkhineh during Drying. Journal of Food Processing and Preservation, 41(2), e12861. https://doi.org/10.1111/jfpp.12861
Hosseinpour, S., Rafiee, S., Aghbashlo, M., & Mohtasebi, S. S. (2015). Computer Vision System (CVS) for In-Line Monitoring of Visual Texture Kinetics During Shrimp (Penaeus Spp.) Drying. Drying Technology, 33(2), 238–254. https://doi.org/10.1080/07373937.2014.947513
Hosseinpour, S., Rafiee, S., Mohtasebi, S. S., & Aghbashlo, M. (2013). Application of computer vision technique for on-line monitoring of shrimp color changes during drying. Journal of Food Engineering, 115(1), 99–114. https://doi.org/10.1016/j.jfoodeng.2012.10.003
Iranshahi, K., Onwude, D. I., Martynenko, A., & Defraeye, T. (2022). Dehydration mechanisms in electrohydrodynamic drying of plant-based foods. Food and Bioproducts Processing, 131, 202–216. https://doi.org/10.1016/j.fbp.2021.11.009
Keramat-Jahromi, M., Mohtasebi, S. S., Mousazadeh, H., Ghasemi-Varnamkhasti, M., & Rahimi-Movassagh, M. (2021). Real-time moisture ratio study of drying date fruit chips based on on-line image attributes using kNN and random forest regression methods. Measurement, 172, 108899. https://doi.org/10.1016/j.measurement.2020.108899
Kırbaş, İ., Tuncer, A. D., Şirin, C., & Usta, H. (2019). Modeling and developing a smart interface for various drying methods of pomelo fruit (Citrus maxima) peel using machine learning approaches. Computers and Electronics in Agriculture, 165, 104928. https://doi.org/10.1016/j.compag.2019.104928
Martynenko, A. (2017). Computer Vision for Real-Time Control in Drying. Food Engineering Reviews, 9, 91–111. https://doi.org/10.1007/s12393-017-9159-5
Martynenko, A., Astatkie, T., Riaud, N., Wells, P., & Kudra, T. (2017). Driving forces for mass transfer in electrohydrodynamic (EHD) drying. Innovative Food Science and Emerging Technologies, 43, 18–25. https://doi.org/10.1016/j.ifset.2017.07.022
Martynenko, A., & Kudra, T. (2016). Electrically-induced transport phenomena in EHD drying - A review. Trends in Food Science and Technology, 54, 63–73. https://doi.org/10.1016/j.tifs.2016.05.019
Martynenko, A., & Zheng, W. (2016). Electrohydrodynamic drying of apple slices: Energy and quality aspects. Journal of Food Engineering, 168, 215–222. https://doi.org/10.1016/j.jfoodeng.2015.07.043
Miraei Ashtiani, S. H., Rohani, A., & Aghkhani, M. H. (2020). Soft computing-based method for estimation of almond kernel mass from its shell features. Scientia Horticulturae, 262, 109071. https://doi.org/10.1016/j.scienta.2019.109071
Miraei Ashtiani, S. H., Sturm, B., & Nasirahmadi, A. (2018). Effects of hot-air and hybrid hot air-microwave drying on drying kinetics and textural quality of nectarine slices. Heat and Mass Transfer, 54, 915–927. https://doi.org/10.1007/s00231-017-2187-0
Mirzaei-Baktash, H., Hamdami, N., Torabi, P., Fallah-Joshaqani, S., & Dalvi-Isfahan, M. (2022). Impact of different pretreatments on drying kinetics and quality of button mushroom slices dried by hot-air or electrohydrodynamic drying. Lwt, 155, 112894. https://doi.org/10.1016/j.lwt.2021.112894
Nadian, M. H., Abbaspour‐Fard, M. H., Sadrnia, H., Golzarian, M. R., Tabasizadeh, M., & Martynenko, A. (2017). Improvement of kiwifruit drying using computer vision system (CVS) and ALM clustering method. Drying Technology, 35(6), 709–723. https://doi.org/10.1080/07373937.2016.1208665
Nadian, M. H., Rafiee, S., Aghbashlo, M., Hosseinpour, S., & Mohtasebi, S. S. (2015). Continuous real-time monitoring and neural network modeling of apple slices color changes during hot air drying. Food and Bioproducts Processing, 94, 263–274. https://doi.org/10.1016/j.fbp.2014.03.005
Okonkwo, C. E., Olaniran, A. F., Adeyi, A. J., Adeyi, O., Ojediran, J. O., Erinle, O. C., Mary, I. Y., & Taiwo, A. E. (2022). Neural network and adaptive neuro-fuzzy inference system modeling of the hot air-drying process of orange-fleshed sweet potato. Journal of Food Processing and Preservation, 46(3), e16312. https://doi.org/10.1111/jfpp.16312
Paul, A., & Martynenko, A. (2021). Electrohydrodynamic drying: Effects on food quality. Drying Technology, 39(11), 1745–1761. https://doi.org/10.1080/07373937.2021.1906694
Pirnazari, K., Esehaghbeygi, A., & Sadeghi, M. (2014). Assessment of quality attributes of banana slices dried by different drying methods. International Journal of Food Engineering, 10(2), 251–260. https://doi.org/10.1515/ijfe-2013-0059
Polat, A., & Izli, N. (2022a). Drying characteristics and quality evaluation of ‘Ankara’ pear dried by electrohydrodynamic-hot air (EHD) method. Food Control, 134, 108774. https://doi.org/10.1016/j.foodcont.2021.108774
Polat, A., & Izli, N. (2022b). Drying of garlic slices by electrohydrodynamic-hot air method. Journal of Food Process Engineering, 45(3), e13980. https://doi.org/10.1111/jfpe.13980
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. https://doi.org/10.1016/j.biosystemseng.2017.01.006
Raj, G. V. S. B., & Dash, K. K. (2020). Microwave vacuum drying of dragon fruit slice: Artificial neural network modelling, genetic algorithm optimization, and kinetics study. Computers and Electronics in Agriculture, 178, 105814. https://doi.org/10.1016/j.compag.2020.105814
Shafaei, S. M., Nourmohamadi-Moghadami, A., & Kamgar, S. (2016). Development of artificial intelligence based systems for prediction of hydration characteristics of wheat. Computers and Electronics in Agriculture, 128, 34–45. https://doi.org/10.1016/j.compag.2016.08.014
Taghian Dinani, S., Hamdami, N., Shahedi, M., & Havet, M. (2014). Mathematical modeling of hot air/electrohydrodynamic (EHD) drying kinetics of mushroom slices. Energy Conversion and Management, 86, 70–80. https://doi.org/10.1016/j.enconman.2014.05.010
Udomkun, P., Nagle, M., Argyropoulos, D., Wiredu, A. N., Mahayothee, B., & Müller, J. (2017). Computer vision coupled with laser backscattering for non-destructive colour evaluation of papaya during drying. Journal of Food Measurement and Characterization, 11, 2142–2150. https://doi.org/10.1007/s11694-017-9598-y
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