A Model to Predict Shelf-Life Loss Ofhorticultural Produce During Distribution Withfluctuated Temperature and Vehicle Vibration

  • Weiwei Gong
  • Daoliang Li
  • Xue Liu
  • Jun Yue
  • Zetian FuEmail author
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 295)


Fresh fruits and vegetables has become a public concern from the food security aspect. And the prediction of shelf-life loss under the fluctuated temperature becomes one of the key problems in food supply chain operation. So this paper identifies the impact aspects of produce decaying during distribution. For the key temperature factor, the process is divided into three phases: sorting, traveling and door-opening. Based on time-temperature function, a model of shelf-life loss of horticultural produce during distribution is developed by evaluating respiration rate of vegetables and fruits considering both the environment fluctuated temperature and vehicle vibration during traveling. Taking eggplant as an example, the numerical experiment result demonstrates that the average cost for ambient distribution is 2.8 times of the insulation way.


shelf-life horticultural produce distribution respiration rate 


  1. B.T. Hargrave, 2002, A traffic light decision system for marine finfish aquaculture siting, Ocean & Coastal Management 45(2002):215–235. CrossRefGoogle Scholar
  2. Babovic-V; Bartoli-N; Yang-SSY An application of artificial neural networks in computational hydraulics. Water for a changing global community. Proceedings of theme B-environmental and coastal hydraulics: protecting the aquatic habitat. Vol. 1. 27th Congress of the International Association for Hydraulic Research, San Francisco, California, USA, 10–15 August 1997. 1997, 865–870; Google Scholar
  3. Borcherding,-J.; Volpers,-M. The “Dreissena-monitor” - First results on the application of this biological early warning system in the continuous monitoring of water quality. International Conferance on Rehabilitation of the River Rhine, Arnhem (Netherlands) 15–19 Mar 1993 Google Scholar
  4. Boyd, Claude E. 1982. Water Quality Management for Pond Fish Culture. Elsevier Scientific Publishing Company. pp 318 Google Scholar
  5. Douglas H. Ernst, John P. Bolte, 2000, AquaFarm: simulation and decision support for aquaculture facility design and management planning, Aquacultural Engineering 23(2000): 121–179 Google Scholar
  6. Fritz,Jack J. 1985.Mathematical Models for Waste Stabilization Pond. ‘Mathematical Modelsin Biological Waste Water Treatment’ Edited by Jorgensen S. E., & Gromiec,M. J. Guan-BT; An artificial neural network with partitionable outputs. Gertner-GZ. Computers-and-Electronics-in-Agriculture. 1996, 16: 1, 39–46 Google Scholar
  7. Hjelmfelt-AT Jr.; Wang M; Heatwole CD. Runoff hydrograph estimation using artificial neural networks. Application of advanced information technologies: effective management of natural resources. Proceedings of the 18–19 June 1993 conference, Spokane, Washington. 1993, 315–320; 5 ref Google Scholar
  8. Jan-JihnFa; Jan-JF. Artificial neural networks for classification of remote sensing data. Quarterly-Journal-of-the-Experimental-Forest-of-National-Taiwan-University. 1997, 11: 3, 79–89 Google Scholar
  9. John Bolte, Shree Nath, Doug Ernst, 2000, Development of decision support tools for aquaculture: the POND experience, Aquacultural Engineering 23(2000) 103–119. Google Scholar
  10. Lacroix-R; Salehi-F; Yang-XZ; Wade-KM. Effects of data processing on the performance of artificial neural networks for dairy yield prediction and cow culling classification. Transactions-of-the-ASAE. 1997, 40:3, 839–846; Google Scholar
  11. Loftus-R. World Watch for domestic animal diversity released by FAO and UNEP provides “early warning system”. Diversity. 1993, 9: 3, 34–36 Google Scholar
  12. Madenjian, C.P., Roger, G.L. & Fast, A. W. Predicting Night Time DO Loss in Prawn Ponds of Hawaii, Part II: A New Method. Aquacultural Engineering. 6: 209–225, 1987 CrossRefGoogle Scholar
  13. Madenjian, C.P. 1990. Nighttime Pone Respiration Rate: Oxygen or Temperature Dependent? Can. J. Fish. Aquat. Sci. 47: 180–183 CrossRefGoogle Scholar
  14. Meulenberg,-E.; Stoks,-P. The application of immunochemical methods in monitoring and early warning systems for water quality control. IWSA Specialized Conference on New Developments in Modelling, Monitoring and Control of Water Supply Systems, Amsterdam (Netherlands) 24–25 Sep 1996 Google Scholar
  15. Nikos Papandroulakis, Papaioannou Dimitris, Divanach Pascal, An automated feeding system for intensive hatcheries, Aquacultural Engineering 26 (2002) 13–26. CrossRefGoogle Scholar
  16. Rural Advancement Fund International. Genetic engineering of pyrethrins: early warning for East African pyrethrum farmers. RAFI-Communique. 1992, June, 3 Google Scholar
  17. Scott-MG; Hutchinson-TC; Piekarz-D. The use of lichen growth abnormalities as an early warning indicator of forest dieback. Special issue: Ecological indicators of the state of the environment. Papers presented at the workshop on ecological indicators of the state of the environment, held at the University of Toronto, Canada. Environmental-Monitoring-and-Assessment. 1990, 15: 3, 213–218 Google Scholar
  18. Selcuk Soyupak, Feza Karaer, Hasan Gurbuz Ersin Kivra. A Neural Network-based Approach for Calculating DO Profiles in Reserviors. Neural Commput and Appliction (2003) 12:166–172 CrossRefGoogle Scholar
  19. Stamou-GP; Stamou-GB; Straalen-NM-van; Krivolutsky-DA. Possible application of fuzzy system simulation models for biomonitoring soil pollution in urban areas. Bioindicator systems for soil pollution. Proceedings of the NATO Advanced Research Workshop on New Approaches to the Development of Bioindicator Systems for Soil Pollution, Moscow, Russia, 24–28 April 1995. 1996, 55–65 Google Scholar
  20. W.T. Miller, et al. CMAC: An associative neural networks alternative to back propagation, Proc. Of IEEE, Vol. 78, No.10, 1990 Google Scholar
  21. Wang R.M., Fu Z.T., Fu LZ.: Evaluation of the Aquaculture Pond Water Quality. Land and Water Management Decision Tllos and Practices Vol-II-Proceedings of the 7th Inter Regional Confere nc e on Environment and Water (2004) 1305– 1313 Google Scholar
  22. Yang-ChunChieh; Prasher-SO; Lacroix-R; Sreekanth-S; Patni-NK; Masse-L; Yang-CC. Artificial neural network model for subsurface-drained farmlands. Journal-of-Irrigation-and-Drainage-Engineering. 1993, 119: 6, 947–963 CrossRefGoogle Scholar
  23. Zion, B., Shklyar, A., Karplus, I., 2000. In vivo fish sorting by computer vision. Aquacultural, Engineering 22, 165–179. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  • Weiwei Gong
    • 1
  • Daoliang Li
    • 2
  • Xue Liu
    • 2
  • Jun Yue
    • 2
    • 3
  • Zetian Fu
    • 1
    • 4
    Email author
  1. 1.College of Economics and ManagementChina Agriculture UniversityBeijingP. R. China
  2. 2.College of Information and Electrical EngineeringChina Agriculture UniversityBeijingP. R. China
  3. 3.College of ManagementLudong UniversityShandong ProvinceP. R. China
  4. 4.College of Economics and ManagementChina Agriculture UniversityBeijingP. R. China

Personalised recommendations