Improving traceability and transparency of table grapes cold chain logistics by integrating WSN and correlation analysis

X. Xiao, Qile He, Z. Li, A.O. Antoce, X. Zhang

Research output: Contribution to journalArticlepeer-review

54 Citations (Scopus)
211 Downloads (Pure)


Effective and efficient measurement and determination of critical quality parameter(s) is the key to improve the traceability and transparency of the table grapes quality as well as the sustainability performance of the table grapes cold chain logistics, and ensure the table grapes quality and safety. This paper is to determine the critical quality parameter(s) in the cold chain logistics through the real time monitoring of the temperature fluctuation implemented with the Wireless Sensor Network (WSN), and the correlation analysis among the various quality parameters. The assessment was conducted through three experiments. Experiment I indicated that the temperature have a large fluctuation from 0°C to 30°C, and the critical temperatures could be determined as 0°C, 5°C,10°C, 15°C, 20°C, 25°C and 30°C. Experiment II described that the firmness and moisture loss rate, whose Pearson correlation coefficient with the sensory evaluation were all greater than 0.9 at the critical temperatures determined in Experiment I, could be the critical quality parameters. Experiment III illustrated that the critical quality parameters, firmness and moisture loss rate, could be reliable indicators of table grapes quality by the Arrhenius kinetic equation, and results showed that the evaluation model based on the firmness is better to predict the shelf life than that based on the moisture loss rate. The best quality table grapes could be provided for the consumers via the easily and directly tracing and controlling the critical quality parameters in real time in actual cold chain logistics.

Original languageEnglish
Pages (from-to)1556–1563
JournalFood Control
Issue numberB
Early online date20 Nov 2016
Publication statusPublished - Mar 2017

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Food Control. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Food Control, [73, B (2016)] DOI: 10.1016/j.foodcont.2016.11.019

© 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International


  • Quality parameters
  • Table grapes
  • Wireless sensor network
  • Correlation analysis
  • Cold chain logistics


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