Importance of computational tools and artificial intelligence to improve drying processes for food preservation
DOI:
https://doi.org/10.46932/sfjdv4n5-011Keywords:
drying, preservation of foods, artificial neural networks, artificial intelligenceAbstract
Computational tools, including mathematical algorithms, specialized software, and artificial neural networks, along with the advancements in artificial intelligence, have brought significant advancements to industrial processes. Specifically, in food drying processes, such as those employed for grains, fruits, and vegetables, these tools have been demonstrated to play a crucial role in preserving the food itself and its nutritional value. This work highlights how artificial intelligence and computational tools have facilitated the automation of industrial processes (Engineering 4.0). Furthermore, it sheds light on the future potential of the man-machine interface, which is expected to give rise to Industry 5.0. The application of artificial intelligence in drying processes has demonstrated its impact on optimizing this unit operation by reducing process times, improving operating conditions, and predicting final quality characteristics of the products with remarkable accuracy, without requiring extensive experimentation or pilot tests.
References
Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Duajili, A., Duan, Y., Al.Shamma, O., Santamaría, J., Fadhel, M.A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data 8(53). https://doi.org/10.1186/s40537-021-00444-8
Chaudhary, V., Kumar, V., Sunil, Singh, B., Kumar, R., & Kumar. V. (2019). Impact of different drying methods on sensory properties of osmotic dehydrated pineapple slices. Asian Journal of Dairy and Food Research 38(1), 73-76. DOI: 10.18805/ajdfr.DR-1434
Espinoza-Vasquez, A.P., Galatro, D., Manzano, P., Choez-Guaranda, I., Cevallos, J.M., Salas, S.D., & Gonzalez, Y. (2023). Tray dryer design under feed uncertainty: A case study on a nutraceutical beverage. Journal of Food Engineering 341, e111341.
https://doi.org/10.1016/j.jfoodeng.2022.111341
Gilago, M.C., Mugi, V.R., & Chandramohan V.P. (2023). Performance assessment of passive indirect solar dryer comparing without and with heat storage unit by investigating the drying kinetics of carrot. Energy Nexus 9, e100178. https://doi.org/10.1016/j.nexus.2023.100178
Hamdi, I., Agrebi, S., ELkhadraoui, A., Chargui, R., & Kooli, S. (2023). Qualitative, energy and economic analysis of forced convective solar drying of tomatoes slices. Solar Energy 258, 244–252. https://doi.org/10.1016/j.solener.2023.04.021
Ju, H.-Y., Vidyarthi, S.K., Karim, M.A., Yu, X.-L., Zhang, W.-P., & Xiao, H.-W. (2023). Drying quality and energy consumption efficient improvements in hot air drying of papaya slices by step-down relative humidity based on heat and mass transfer characteristics and 3D simulation. Drying Technology 41(3), 460-476.
https://doi.org/10.1080/07373937.2022.2099416
Kalantari, D., Naji-Tabasi, S., Kaveh, M., Azadbakht, M., Majnooni, M., Khorshidi, Y., Asghari, A., & Khalife, E. (2023). Drying kinetics and shrinkage rate of thin-sliced pears in different drying stages. Journal of Food Process Engineering 46(3), e14264.
https://doi.org/10.1007/s12393-023-09333-7
Keskes, S., Hanini, S., Hentabli, M & Laidi, M. (2020). Artificial intelligence and mathematical modelling of the drying kinetics of pharmaceutical powders. Chemistry in Industry 69(3-4), 137–152. https://doi.org/10.15255/KUI.2019.038
Khan, P.W., Byun, Y.-C., & Park, N. (2020a). IoT-Blockchain enabled optimized provenance system for food industry 4.0 using advanced deep learning. Sensors 20, e2990.
https://doi.org/10.3390/s20102990
Khan, M.I.H., Sablani, S.S., Joardder, M.U.H, & Karim, M.A. (2020b). Application of machine learning-based approach in food drying: Opportunities and challenges. Drying Technology 40(6). https://doi.org/10.1080/07373937.2020.1853152
Kumar, P., & Yao, D.-J. (2022). Design process of a vacuum freeze dryer: Simultaneous endpoint determination using measurement of both temperature and relative humidity. Journal of Food Process Engineering 45(5), e14003.
https://doi.org/10.1111/jfpe.14003
Kushwah, A., Gaur, M.K., Kumar, A., & Singh, P. (2022). Application of ANN and prediction of drying behavior of mushroom drying inside hybrid greenhouse solar dryer: An experimental validation. Journal of Thermal Engineering 8(2), 221–234.
DOI:10.18186/thermal.1086189
Leilayi, M., Arabhosseini, A., Kianmehr, M.H., & Amiri, H. (2023). Design, construction and performance evaluation of paddy rice solar drum dryer equipped with perforated drum. Clean Energy 7(2), 328–339. https://doi.org/10.1093/ce/zkac074
Martynenko, A. & Misra, N. N. (2019). Machine learning in drying. Drying Technology 38(5-6). https://doi.org/10.1080/07373937.2019.1690502
Nwosu-Obieogu, K., Emmanuel Olusola Oke, E.O., & Bright, S. (2022). Energy and exergy analysis of three leaved yam starch drying in a tray dryer: parametric, modelling and optimization studies. Heliyon 8, e10124. https://doi.org/10.1016/j.heliyon.2022.e10124
Oliveira, K.S., Bojorge, N. & Freitas, S.P. (2021). Lipid microencapsulation process using spray drying: Modeling and heat recovery study. Brazilian Journal of Chemical Engineering 38, 641–652. https://doi.org/10.1007/s43153-021-00182-7
Patil, R., & Gawande, R. (2016). A review on solar tunnel greenhouse drying system. Renewable and Sustainable Energy Reviews 56, 196–214. http://dx.doi.org/10.1016/j.rser.2015.11.057
Petikirige, J., Karim, A., & Millar, G. (2022). Effect of drying techniques on quality and sensory properties of tropical fruits. International Journal of Food Science and Technology 57, 6963–6979. DOI:10.1111/ijfs.16043
Przybył, K., Koszela, K., Adamski, F., Samborska, K., Walkowiak, K., & Polarczyk, M. (2021). Deep and machine learning using SEM, FTIR, and texture analysis to detect polysaccharide in raspberry powders. Sensors 21, 5823. https://doi.org/10.3390/s21175823
Raja Santhi, A., Muthuswamy, P. (2023). Industry 5.0 or industry 4.0S? Introduction to industry 4.0 and a peek into the prospective industry 5.0 technologies. International Journal on Interactive Design and Manufacturing 17, 947–979. https://doi.org/10.1007/s12008-023-01217-8
Rios-Campos, C., Mendoza, C.E.S., Aguirre, Z.I.R., Aguirre, Z.H.E., Castro, V.D.J., Suárez, P.W., Tapia, I.C.E., & Yeckle, A.R.M. (2023). Artificial intelligence and education. South Florida Journal of Development 4(2), 641-655. DOI: 10.46932/sfjdv4n2-001
Seerangurayar, T., Al-Ismailia, A.M., Jeewantha, L.H.J., & Al-Nabhani, A. (2019). Experimental investigation of shrinkage and microstructural properties of date fruits at three solar drying methods. Solar Energy 180, 445–455. https://doi.org/10.1016/j.solener.2019.01.047
Sharkawy, A.-N. (2020). Principle of neural network and its main types: Review. Journal of Advances in Applied & Computational Mathematics 7, 8-19.
https://doi.org/10.15377/2409-5761.2020.07.2
Verma, G., Dewangan, N., Ghritlahre, H.K., Verma, M., Kumar, S., Kumar, Y., & Agrawal, S. (2023). Experimental investigation of mixed mode ultraviolet tent house solar dryer under natural convection regime. Solar Energy 251, 51–67. https://doi.org/10.1016/j.solener.2022.12.052
Zhu, J., Liu, Y., Zhu, C., & Wei, M. (2022). Effects of different drying methods on the physical properties and sensory characteristics of apple chip snacks. LWT - Food Science and Technology 154, e112829. https://doi.org/10.1016/j.lwt.2021.112829
Zhu, L., Spachos, P., Pensini, E., & Plataniotis, K. N. (2021). Deep learning and machine vision for food processing: A survey. Current Research in Food Science 4, 233-249.