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Radio Frequency IDentification for Meat Supply-Chain Digitalisation

Digitalised supply-chain traceability systems can offer wide prospects both for improving safety as well as enhancing perceived quality. However, the coupling between physical goods and information is often difficult for agri-food items. A solution could be the use of RFID (Radio Frequency IDentific...

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Autores principales: Barge, Paolo, Biglia, Alessandro, Comba, Lorenzo, Ricauda Aimonino, Davide, Tortia, Cristina, Gay, Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506907/
https://www.ncbi.nlm.nih.gov/pubmed/32883048
http://dx.doi.org/10.3390/s20174957
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author Barge, Paolo
Biglia, Alessandro
Comba, Lorenzo
Ricauda Aimonino, Davide
Tortia, Cristina
Gay, Paolo
author_facet Barge, Paolo
Biglia, Alessandro
Comba, Lorenzo
Ricauda Aimonino, Davide
Tortia, Cristina
Gay, Paolo
author_sort Barge, Paolo
collection PubMed
description Digitalised supply-chain traceability systems can offer wide prospects both for improving safety as well as enhancing perceived quality. However, the coupling between physical goods and information is often difficult for agri-food items. A solution could be the use of RFID (Radio Frequency IDentification) systems. Due to its wide reading range, Ultra-High Frequency (UHF) technology is already widely used in logistics and warehousing, mostly for the identification of batches of items. A growing interest is also emerging in Near Field Communication (NFC), as several smartphones embed an integrated NFC antenna. This paper deals with the automatic identification of meat products at item level, proposing and evaluating the adoption of different RFID technologies. Different UHF and NFC solutions are proposed, which benchmark tag performances in different configurations, including four meat types (fatty beef, lean beef, chicken and pork), by using a specifically designed test bench. As avoiding the application of two different tags could be advantageous, dual frequency devices (UHF and NFC) are also considered. Significant differences in tag performances, which also depend on meat type and packaging, are highlighted. The paper highlights that tag positioning should consider the geometry of the packaging and the relative positioning of tag, meat and reader antenna.
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spelling pubmed-75069072020-09-30 Radio Frequency IDentification for Meat Supply-Chain Digitalisation Barge, Paolo Biglia, Alessandro Comba, Lorenzo Ricauda Aimonino, Davide Tortia, Cristina Gay, Paolo Sensors (Basel) Article Digitalised supply-chain traceability systems can offer wide prospects both for improving safety as well as enhancing perceived quality. However, the coupling between physical goods and information is often difficult for agri-food items. A solution could be the use of RFID (Radio Frequency IDentification) systems. Due to its wide reading range, Ultra-High Frequency (UHF) technology is already widely used in logistics and warehousing, mostly for the identification of batches of items. A growing interest is also emerging in Near Field Communication (NFC), as several smartphones embed an integrated NFC antenna. This paper deals with the automatic identification of meat products at item level, proposing and evaluating the adoption of different RFID technologies. Different UHF and NFC solutions are proposed, which benchmark tag performances in different configurations, including four meat types (fatty beef, lean beef, chicken and pork), by using a specifically designed test bench. As avoiding the application of two different tags could be advantageous, dual frequency devices (UHF and NFC) are also considered. Significant differences in tag performances, which also depend on meat type and packaging, are highlighted. The paper highlights that tag positioning should consider the geometry of the packaging and the relative positioning of tag, meat and reader antenna. MDPI 2020-09-01 /pmc/articles/PMC7506907/ /pubmed/32883048 http://dx.doi.org/10.3390/s20174957 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Barge, Paolo
Biglia, Alessandro
Comba, Lorenzo
Ricauda Aimonino, Davide
Tortia, Cristina
Gay, Paolo
Radio Frequency IDentification for Meat Supply-Chain Digitalisation
title Radio Frequency IDentification for Meat Supply-Chain Digitalisation
title_full Radio Frequency IDentification for Meat Supply-Chain Digitalisation
title_fullStr Radio Frequency IDentification for Meat Supply-Chain Digitalisation
title_full_unstemmed Radio Frequency IDentification for Meat Supply-Chain Digitalisation
title_short Radio Frequency IDentification for Meat Supply-Chain Digitalisation
title_sort radio frequency identification for meat supply-chain digitalisation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506907/
https://www.ncbi.nlm.nih.gov/pubmed/32883048
http://dx.doi.org/10.3390/s20174957
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