Eze, Joy; Duan, Yanqing; Eze, Elias; Ramanathan, Ramakrishnan and Ajmal, Tahmina. 2024. Machine learning-based optimal temperature management model for safety and quality control of perishable food supply chain. Scientific Reports, 14, 27228. ISSN 2045-2322 [Article]

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Abstract or Description

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.

Item Type:	Article
Identification Number (DOI):	https://doi.org/10.1038/s41598-024-70638-6
Additional Information:	Funding: This research was carried-out under Interreg North-West Europe, grant number NWE831.
Data Access Statement:	The data that support the findings of this study are available from the first author upon reasonable request.
Keywords:	Food technology, Cold supply chain, Food waste, Modelling, Perishable foods, Machine learning, Food temperature control, k-means clustering
Departments, Centres and Research Units:	Computing
Dates:	Date Event 20 April 2024 Submitted 20 August 2024 Accepted 8 November 2024 Published
Item ID:	37938
Date Deposited:	05 Dec 2024 09:57
Last Modified:	05 Dec 2024 09:57
Peer Reviewed:	Yes, this version has been peer-reviewed.
URI:	https://research.gold.ac.uk/id/eprint/37938

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