Machine learning-based optimal temperature management model for safety and quality control of perishable food supply chain

Article


Eze, J., Duan, Y., Eze, E., Ramanathan, R. and Ajmal, T. 2024. Machine learning-based optimal temperature management model for safety and quality control of perishable food supply chain. Scientific Reports. 14 (Art. 27228). https://doi.org/10.1038/s41598-024-70638-6
AuthorsEze, J., Duan, Y., Eze, E., Ramanathan, R. and Ajmal, T.
Abstract

The management of a food supply chain is difficult and complex because of the product's short shelf-life, time-sensitivity, and perishable nature which must be carefully considered to minimize food waste. Temperature-controlled perishable food supply chain provides the highly crucial facilities necessary to maintain the quality and safety of the product. The storage temperature is the most vital factor in maintaining both the quality and shelf-life of a perishable food. Adequate storage temperature control ensures that perishable foods are transported to the end-users in good quality and safe to consume. This paper presents perishable food storage temperature control through mathematical optimal control model where the storage temperature is regarded as the control variable and the deterioration of the perishable food’s quality follows the first-order reaction. The optimal storage temperature for a single perishable food is determined by applying the Pontryagin's maximum principle to solve the optimal control model problem. For multi-temperature commodities supply chain, an unsupervised machine learning (ML) method, called k-means clustering technique is used to determine the temperature clusters for a range of perishables. Based on descriptive analysis, it is observed that the k-means clustering technique is effective in identifying the best suitable storage temperature clusters for quality control of multi-commodity supply chain.

JournalScientific Reports
Journal citation14 (Art. 27228)
ISSN2045-2322
Year2024
PublisherSpringer Nature
Publisher's version
License
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.1038/s41598-024-70638-6
Publication dates
Online08 Nov 2024
Publication process dates
Accepted20 Aug 2024
Deposited18 Sep 2024
Copyright holder© The Author(s) 2024
Permalink -

https://repository.uel.ac.uk/item/8y4w7

Download files


Publisher's version
s41598-024-70638-6.pdf
License: CC BY-NC-ND 4.0
File access level: Anyone

  • 32
    total views
  • 8
    total downloads
  • 7
    views this month
  • 2
    downloads this month

Export as

Related outputs

A Comparative Study For Predicting House Price Based on Machine Learning
Eze, E., Sujith, S., Eze, J. and Sharif, S. 2023. A Comparative Study For Predicting House Price Based on Machine Learning. ICDABI 2023: 4th International Conference on Data Analytics for Business and Industry. University of Bahrain, Kingdom of Bahrain 25 - 26 Oct 2024 IEEE. https://doi.org/10.1109/ICDABI60145.2023.10629399
Aquaculture 4.0: Hybrid Neural Network Multivariate Water Quality Parameters Forecasting Model
Eze, E., Kirby, S., Attridge, J. and Ajmal, T. 2023. Aquaculture 4.0: Hybrid Neural Network Multivariate Water Quality Parameters Forecasting Model. Scientific Reports. 13 (Art. 16129). https://doi.org/10.1038/s41598-023-41602-7