Quality Estimation of Net Packaged Onions during Storage Periods using Machine Learning Techniques |
Nandita Irsaulul, Nurhisna
(Department of Biosystems Engineering, Seoul National University)
Sang-Yeon, Kim (Department of Biosystems Engineering, Seoul National University) Seongmin, Park (Department of Biosystems Engineering, Seoul National University) Suk-Ju, Hong (Department of Biosystems Engineering, Seoul National University) Eungchan, Kim (Department of Biosystems Engineering, Seoul National University) Chang-Hyup, Lee (Department of Biosystems Engineering, Seoul National University) Sungjay, Kim (Department of Biosystems Engineering, Seoul National University) Jiwon, Ryu (Department of Biosystems Engineering, Seoul National University) Seungwoo, Roh (Department of Biosystems Engineering, Seoul National University) Daeyoung, Kim (Department of Biosystems Engineering, Seoul National University) Ghiseok, Kim (Department of Biosystems Engineering, Seoul National University) |
1 | Sasongko, S.B., Hadiyanto, H., Djaeni, M., Perdanianti, A.M., and Utari, F.D. 2020. Effects of drying temperature and relative humidity on the quality of dried onion slice. Heliyon. 6(7):e04338. doi:10.1016/j.heliyon.2020.e04338 DOI |
2 | Baek H.-S. and Kim, I.S. 2020. An Analysis of the Impact of Climate Change on the Korean Onion Market. J Ind Bus. 11(3):39-50. doi:10.13106/jidb.2020.vol11.no3.39 DOI |
3 | Cho, J. -E., Bae, R. -N. and Lee, S.K. 2010. Current Research Status of Postharvest Technology of Onion (Allium cepa L.). Hortscience Tech. 28(3):522-527. |
4 | Sang, M. K., Han, G.D., Oh, J.Y., Chun, S.C. and Kim, K.D. 2014. Penicillium brasilianum as a novel pathogen of onion (Allium cepa L.) and other fungi predominant on market onion in Korea. Crop Prot. 65:138-142. doi:10.1016/j.cropro.2014.07.016 DOI |
5 | Isma'ila, M., Karu, E., Zhigila, D.A. and Yuguda, U. 2017. Postharvest Storage and Shelf Life Potentials among Selected Varieties of Onion (Allium cepa L). Scholars Acad J Biosci. 5(4):271-277. doi:10.21276/sajb DOI |
6 | Jang, S.-H. and Lee, S.-K. 2009. Current Research Status of Postharvest Technology of Onion. Korean J Hortscience Tech. 27(3):511-520. |
7 | Porras-Amores, C., Mazarron, F.R. and Canas, I. 2014. Study of the vertical distribution of air temperature in warehouses. Energies. 7(3):1193-1206. doi:10.3390/en7031193 DOI |
8 | Abbott, J.A. 1999. Quality measurement of fruits and vegetables. Postharvest Bio Tech. 15(3):207-225. doi:10.1016/S0925-5214(98)00086-6 DOI |
9 | Soliman, S.N. and El-Sayed, A.E. 2017. Penetration and Stress-Strain Behavior of Potato Tubers During Storage. Misr J Ag Eng. 34(4):2291-2310. doi:10.21608/mjae.2017.97514 DOI |
10 | Masoudi, H., Tabatabaeefar, A. and Borghaee, A. M. 2007. Determination of storage effect on mechanical properties of apples using the uniaxial compression test. Can Bio Eng. 49:3.29-33. |
11 | Eboibi, O. and Uguru, H. 2017. Storage conditions effect on physic-mechanical properties of Nandini cucumber. Int J Eng Tech Res. 7(11):48-56. |
12 | Mohsenin, N.N. 2020. Physical Properties of Plant and Animal Materials: V. 1: Physical Characteristics and Mechanical Properties. 2nd Ed. Routledge, New York, USA, pp. 702. doi:10.4324/9781003062325 DOI |
13 | Ferreira, A.P.S, de Souza, C.S., Pereira, A.M., Cardoso, D.S.C.P., Finger, F.L. and Rego, E.R. 2015. Storage of onions in farm scale ventilated silos. Proceeding II International Symposium on Horticulture in Europe. pp. 123-128. doi:10.17660/ActaHortic.2015.1099.11 DOI |
14 | Sharma, K., Ko, E.Y., Assefa, A.D., Nile, S.H and Park, S.W. 2015. A comparative study of anaerobic and aerobic decomposition of quercetin glucosides and sugars in onion at an ambient temperature. Front Life Sci. 8(2):117-123. doi:10.1080/21553769.2014.998298 DOI |
15 | Emana, B., Afari-Sefa, V., Kebede, D., Nenguwo, N., Ayana, A. and Mohammed, H. 2017. Assessment of postharvest losses and marketing of onion in Ethiopia. Int J Post Tech and Inn. 5(4):300-319. doi:10.1504/IJPTI.2017.092466 DOI |
16 | Shao, P., Liu, L. and Yu, J. 2021. An overview of intelligent freshness indicator packaging for food quality and safety monitoring. Trends Food Sci Technol. 118:285-296. doi:10.1016/j.tifs.2021.10.012 DOI |
17 | Falayi, F.R., Yusuf, H.A. and State, O. 2014. Performance Evaluation of a Modified Onion Storage Structure. J Emerging trends in Eng App Sci. 5(6):334-339. |
18 | Badia-Melis, R., Mishra, P. and Ruiz-Garcia, L. 2015. Food traceability: New trends and recent advances. A review. Food Control. 57:393-401. doi:10.1016/j.foodcont.2015.05.005 DOI |
19 | Chen, R.Y. 2017. An intelligent value stream-based approach to collaboration of food traceability cyber physical system by fog computing. Food Control. 71:124-136. doi:10.1016/j.foodcont.2016.06.042 DOI |
20 | Xiao, X., He, Q., Li, Z., Antoce, A.O. and Zhang, X. 2017. Improving traceability and transparency of table grapes cold chain logistics by integrating WSN and correlation analysis. Food Control. 73:1556-1563. doi:10.1016/j.foodcont.2016.11.019 DOI |
21 | Karim, A.B., Hassan, A.Z., Akanda, M.M. and Mallik, A. Monitoring food storage humidity and temperature data using IoT. 2018. MOJ Food Process Technol. 6(4):400-404. doi:10.15406/mojfpt.2018.06.00194 DOI |
22 | Sarmah, B. and Aruna, G. 2020. Detection of food quality and quantity at cold storage using IoT. 2020 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), pp. 200-203. |
23 | Accorsi, R., Bortolini, M., Gamberi, M., Guidani, B., Manzini, R. and Ronzoni, M. 2021. Simulating product-packaging conditions under environmental stresses in a food supply chain cyber-physical twin. J Food Eng. 320:110930. doi:10.1016/j.jfoodeng.2021.110930 DOI |
24 | Huang, X., Chen, M., Wang, W., Ge, Y. and Xie J. 2020. Shelf-life Prediction of Chilled Penaeus vannamei Using Grey Relational Analysis and Support Vector Regression. J Aquat Food Prod Tech. 29(6):507-519. doi:10.1080/10498850.2020.1766616 DOI |
25 | Ramzi, M., Kashaninejad, M., Salehi, F., Sadeghi, Mahoonak, A.R. and Ali, R.S.M. 2015. Modeling of rheological behavior of honey using genetic algorithm-artificial neural network and adaptive neuro-fuzzy inference system. Food Biosci.9(1):60-67. doi:10.1016/j.fbio.2014.12.001 DOI |
26 | Chen, C.R., Ramaswamy, H.S. and Alli, I. 2001. Prediction of quality changes during osmo-convective drying of blueberries using neural network models for process optimization. Drying Tech. 19(3-4):507-523. doi:10.1081/DRT-100103931 DOI |
27 | Correa-mosquera, A.R., Quicaz, M.C. and Zuluaga-dominguez, C.M. 2022. Shelf-life prediction of pot-honey subjected to thermal treatments based on quality attributes at accelerated storage conditions. Food Control. 142:109237. doi:10.1016/j.foodcont.2022.109237 DOI |
28 | Devahastin, S. and Niamnuy, C. 2010. Modelling quality changes of fruits and vegetables during drying: A review. Int J Food Sci Technol. 45(9):1755-1767. doi:10.1111/j.1365-2621.2010.02352.x DOI |
29 | Mitra, J., Shrivastava, S. L. and Rao, P. S. 2015. Non-enzymatic browning and flavour Kinetics of vacuum dried onion slices. Int Agrophys. 29(1):91-100. doi:10.1515/intag-2015-0010 DOI |
30 | Escobedo-Avellaneda, Z., Velazquez, G., Torres, J. A., & Welti-Chanes, J. 2012. Inclusion of the variability of model parameters on shelf-life estimations for low and intermediate moisture vegetables. LWT Food Sci Tech. 47(2):364-370. doi:10.1016/j.lwt.2012.01.032 DOI |
31 | Kaymak-Ertekin, F. and Gedik, A. 2005. Kinetic modelling of quality deterioration in onions during drying and storage. J Food Eng. 68(4):443-453. doi:10.1016/j.jfoodeng.2004.06.022 DOI |
32 | Sastry, S. K. 1985. Moisture losses from perishable commodities: recent research and developments. Int J Refrig. 8(6):343-346. doi:10.1016/0140-7007(85)90029-5 DOI |
33 | ASAE standard 368. 4. 2008. Compression test of food materials of convex shape. American Society of Agricultural and Biological Engineers. 2000 (MAR95):580-587. http://elibrary.asabe.org/abstract.asp?aid=42544&t=2 |
34 | Bovi, G.G., Caleb, O.J., Linke, M., Rauh, C. and Mahajan, P.V. 2016. Transpiration and moisture evolution in packaged fresh horticultural produce and the role of integrated mathematical models: A review. Biosyst Eng. 150:24-39. doi:10.1016/j.biosystemseng.07.013 DOI |
![]() |