A traceability-support system to control safety and sustainability indicators in food distribution

Gallo, Andrea; Accorsi, Riccardo; Goh, A.; Hsiao, Hsin I; Manzini, Riccardo

doi:10.1016/j.foodcont.2021.107866

Cited by 45 publications

(28 citation statements)

References 42 publications

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“…(3) C t is a new state that combines C t−1 and C t , as described in Equation (17). ft is used to determine the influence of previous memories from the forget gate, as well as to determine the current state of influence from the input gate;…”

Section: Resultsmentioning

confidence: 99%

“…Jing discussed applications of different links in the grain storage and transportation process; the author proposed a food storage and transportation safety information management platform to enhance the informatization process of food enterprises [16]. Gallo et al proposed a traceability support system to control safety and sustainability indicators in food distribution, aiding practitioners in the monitoring of unsupervised operations throughout the supply chain [17]. Todorović et al proposed the use of augmented reality to provide relevant information on food production and packaging systems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Table 1 shows a summary of the findings of the literature review. IoT V [11][12][13][14] V [11][12][13][14] V [12,14] Informational Assistance V [15][16][17][18] Blockchain V [19][20][21] V [19][20][21] Data Analysis V [22][23][24] V [24]…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Design of an IOTA Tangle-Based Intelligent Food Safety Service Platform for Bubble Tea

Chi

Ling

2021

Processes

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Many food safety incidents have occurred in the world in the past 20 years, causing major threats and harm to human life and health. Each country or region has established different food safety management systems (FSMSs) in response, to increase food safety and to reduce food safety risks. Hence, it is important to develop an FSMS service platform with convenience, consistency, effectiveness, scalability, and lightweight computing. The aim of this study is to design and propose an IOTA Tangle-based intelligent food safety service platform for bubble tea—called IF4BT—which modularizes and integrates hazard analysis and critical control point (HACCP) principles to increase data transparency. The deep learning inference engine is based on long short-term memory and Siamese networks to check and extract significant rare data of high-risk factors, exception factors, and noises, depending on daily check and audit. IF4BT can ensure the correctness of the information of food manufacturers, so as to increase food safety and to reduce food safety issues such as allergen cross-contamination, food expiration, food defense, and food fraud.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Design of an IOTA Tangle-Based Intelligent Food Safety Service Platform for Bubble Tea

Chi

Ling

2021

Processes

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“…Although lots of shared information have strengthened the communication and cooperation between each node of the food supply chain, these systems are not enough to handle the increasing challenges of current food supply chains, such as flexibility, compatibility, credibility, and comprehensive decision-making [ 11 ]. On the one hand, the growing information collected by various sensors of the IoT or shared by different supply chain partners enables factories to real-time solve the traceability problems of the food supply chain and improve food quality and safety, but many managers and researchers have now figured out that the plentiful data sources stored in the system are not used effectively since the existing RITS lacks an effective unified method to timely process such large-scaled, multisourced, and heterogeneous data of the whole supply chain, which are very valuable in today's highly competitive market [ 12 ].…”

Section: Related Workmentioning

confidence: 99%

Deep‐Stacking Network Approach by Multisource Data Mining for Hazardous Risk Identification in IoT‐Based Intelligent Food Management Systems

Kong

Yang

Wang

et al. 2021

Computational Intelligence and Neuroscience

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Food quality and safety issues occurred frequently in recent years, which have attracted more and more attention of social and international organizations. Considering the increased quality risk in the food supply chain, many researchers have applied various information technologies to develop real-time risk identification and traceability systems (RITSs) for preferable food safety guarantee. This paper presents an innovative approach by utilizing the deep-stacking network method for hazardous risk identification, which relies on massive multisource data monitored by the Internet of Things timely in the whole food supply chain. The aim of the proposed method is to help managers and operators in food enterprises to find accurate risk levels of food security in advance and to provide regulatory authorities and consumers with potential rules for better decision-making, thereby maintaining the safety and sustainability of food product supply. The verification experiments show that the proposed method has the best performance in terms of prediction accuracy up to 97.62%, meanwhile achieves the appropriate model parameters only up to 211.26 megabytes. Moreover, the case analysis is implemented to illustrate the outperforming performance of the proposed method in risk level identification. It can effectively enhance the RITS ability for assuring food supply chain security and attaining multiple cooperation between regulators, enterprises, and consumers.

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“…One way to guarantee food quality and safety is by implementing a traceability system [14,33,34]. Gallo, Accorsi, Goh, Hsiao, and Manzini [35] applied a traceability system along the supply chain to reduce carbon emissions from distribution to ensure product safety. Meanwhile, the relationship between traceability systems and carbon emissions was revealed by Muirhead and Porte [36].…”

Section: Introductionmentioning

confidence: 99%

Production-Distribution Model Considering Traceability and Carbon Emission: A Case Study of the Indonesian Canned Fish Food Industry

et al. 2021

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Background: Traceability systems and carbon emissions are two important factors involved in production and distribution activities. The involvement of these two factors in production and distribution activities along the supply chain will ensure the safety and quality of food through the manufacture, packaging and distribution of products with minimal costs and in an environmentally friendly way. Objective: This study aimed to develop a model of canned fish food production and distribution integration by considering traceability and carbon emissions to minimize total costs. Method: A mixed-integer linear programming (MILP) approach was used to develop mathematical models and the optimal solution of the model created was obtained using an open-source spreadsheet solver program. Results: The results show that the proposed models produce the minimum total production and distribution cost with high traceability and low carbon emissions. Conclusions: The sensitivity analysis from this study shows that there is a significant relationship between production, carbon emissions, and the total cost of production-distribution. Moreover, it was concluded that the production level, carbon emission level, and emission threshold can have a significant influence in the generation of the total carbon emissions.

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A traceability-support system to control safety and sustainability indicators in food distribution

Cited by 45 publications

References 42 publications

Design of an IOTA Tangle-Based Intelligent Food Safety Service Platform for Bubble Tea

Design of an IOTA Tangle-Based Intelligent Food Safety Service Platform for Bubble Tea

Deep‐Stacking Network Approach by Multisource Data Mining for Hazardous Risk Identification in IoT‐Based Intelligent Food Management Systems

Production-Distribution Model Considering Traceability and Carbon Emission: A Case Study of the Indonesian Canned Fish Food Industry

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