Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Mar 15;6(4):843-859.
doi: 10.1002/fsn3.613. eCollection 2018 Jun.

Hierarchical network modeling with multidimensional information for aquatic safety management in the cold chain

Lu Liu  1 , Xinlei Liu  1 , Wubin Li  1
Affiliations

Hierarchical network modeling with multidimensional information for aquatic safety management in the cold chain

Lu Liu et al. Food Sci Nutr. .

Abstract

The cold-chain information has characterized by the loss and dispersion according to the different collecting methods. The description for the quality decay factors of aquatic products can be defined as the multidimensional information. A series of nodes with multidimensional information are assembled to be hierarchies aiming at describing the environment conditions and locations in the supply chain. Each of the single hierarchy levels constitutes a sequence of node information in a network, which is applied as internal information analysis. The cross-layer information structure is defined as "bridge" information which is able to record the information transmissions among every hierarchy from the point of view of the whole chain. The study has established a novel structured modeling to describe the cold chain of aquatic products based on a network-hierarchy framework. An organized and sustainable transmission process can be built and recorded by the multidimensional attributes for the whole course of cold chain of aquatic products. In addition, seamless connections among every hierarchy are attainable by the environmental information records continuously to monitor the quality of aquatic products. The quality assessments and shelf life predictions are estimated properly as the risk control in order to monitor and trace the safety of aquatic products under the supply chain perspective.

Keywords: aquatic product; cold chain; hierarchical network; multidimensional information; quality safety.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Multidimensional information architecture. Full‐dimensional information could be classified in multidimensional data to form a massive data analysis center as 3D information structure
Figure 2
Figure 2
The bridge node information architecture. The network structure of the processing stage consists of all of processing information. The green node is the beginning of processing information, while the blue node is the ending of processing information. The bridge is just a connection between the ending of the processing stage and the beginning of the storage stage. Another bridge is a connection between the ending of the storage stage and the beginning of the freightage stage, and so on. By this way, information and data direction can be explained by the stage information and the connecting information
Figure 3
Figure 3
The multihierarchical network. In the 3D network‐hierarchy modeling, the circulation status of each node is elaborated through describing processing and environment information based on the attribute value of multidimensional nodes
Figure 4
Figure 4
The whole‐process temperature information for the cold chain of tilapia. The analysis of the entire temperature data as the most critical information is shown by extracting temperature attribute value for nodes across the entire cold chain
See this image and copyright information in PMC

References

    1. Alam, S. , Dobbie, G. , & Rehman, S. U. (2015). Analysis of particle swarm optimization based hierarchical data clustering approaches. Swarm and Evolutionary Computation, 25, 36–51. https://doi.org/10.1016/j.swevo.2015.10.003 - DOI
    1. Aligon, J. , Gallinucci, E. , Golfarelli, M. , Marcel, P. , & Rizzi, S. (2015). A collaborative filtering approach for recommending OLAP sessions. Decision Support Systems, 69, 20–30. https://doi.org/10.1016/j.dss.2014.11.003 - DOI
    1. Ang, B. W. , & Wang, H. (2015). Index decomposition analysis with multidimensional and multilevel energy data. Energy Economics, 51, 67–76. https://doi.org/10.1016/j.eneco.2015.06.004 - DOI
    1. Aung, M. M. , & Chang, Y. S. (2014). Temperature management for the quality assurance of a perishable food supply chain. Food Control, 40, 198–207. https://doi.org/10.1016/j.foodcont.2013.11.016 - DOI
    1. Baranyi, J. , & Roberts, T. A. (1994). A dynamic approach to predicting bacterial growth in food. International Journal of Food Microbiology, 23, 277–294. https://doi.org/10.1016/0168-1605(94)90157-0 - DOI - PubMed