Machine learning-based optimal temperature management model for safety and quality control of perishable food supply chain
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 f...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-70638-6 |
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| author | Joy Eze Yanqing Duan Elias Eze Ramakrishnan Ramanathan Tahmina Ajmal |
| author_facet | Joy Eze Yanqing Duan Elias Eze Ramakrishnan Ramanathan Tahmina Ajmal |
| author_sort | Joy Eze |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cf76d7b6409f4ceaa9f4c80f27396e49 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-cf76d7b6409f4ceaa9f4c80f27396e492025-08-20T02:50:07ZengNature PortfolioScientific Reports2045-23222024-11-0114111210.1038/s41598-024-70638-6Machine learning-based optimal temperature management model for safety and quality control of perishable food supply chainJoy Eze0Yanqing Duan1Elias Eze2Ramakrishnan Ramanathan3Tahmina Ajmal4Department of Computing, School of Professional Studies, Science and Technology, Goldsmiths University of London, New CrossBusiness and Management Research Institute, University of BedfordshireSchool of Architecture, Computing and Engineering, University of East LondonDepartment of Management, College of Business Administration, University of SharjahSchool of Computer Science and Technology, Institute for Research in Engineering and Sustainable Environment, University of BedfordshireAbstract 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.https://doi.org/10.1038/s41598-024-70638-6Food technologyCold supply chainFood wasteModellingPerishable foodsMachine learning |
| spellingShingle | Joy Eze Yanqing Duan Elias Eze Ramakrishnan Ramanathan Tahmina Ajmal Machine learning-based optimal temperature management model for safety and quality control of perishable food supply chain Scientific Reports Food technology Cold supply chain Food waste Modelling Perishable foods Machine learning |
| title | Machine learning-based optimal temperature management model for safety and quality control of perishable food supply chain |
| title_full | Machine learning-based optimal temperature management model for safety and quality control of perishable food supply chain |
| title_fullStr | Machine learning-based optimal temperature management model for safety and quality control of perishable food supply chain |
| title_full_unstemmed | Machine learning-based optimal temperature management model for safety and quality control of perishable food supply chain |
| title_short | Machine learning-based optimal temperature management model for safety and quality control of perishable food supply chain |
| title_sort | machine learning based optimal temperature management model for safety and quality control of perishable food supply chain |
| topic | Food technology Cold supply chain Food waste Modelling Perishable foods Machine learning |
| url | https://doi.org/10.1038/s41598-024-70638-6 |
| work_keys_str_mv | AT joyeze machinelearningbasedoptimaltemperaturemanagementmodelforsafetyandqualitycontrolofperishablefoodsupplychain AT yanqingduan machinelearningbasedoptimaltemperaturemanagementmodelforsafetyandqualitycontrolofperishablefoodsupplychain AT eliaseze machinelearningbasedoptimaltemperaturemanagementmodelforsafetyandqualitycontrolofperishablefoodsupplychain AT ramakrishnanramanathan machinelearningbasedoptimaltemperaturemanagementmodelforsafetyandqualitycontrolofperishablefoodsupplychain AT tahminaajmal machinelearningbasedoptimaltemperaturemanagementmodelforsafetyandqualitycontrolofperishablefoodsupplychain |