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Enregistrement W1500863104 · doi:10.1111/1750-3841.12299

Food Product Tracing Technology Capabilities and Interoperability

2013· article· en· W1500863104 sur OpenAlex

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Notice bibliographique

RevueJournal of Food Science · 2013
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueFood Supply Chain Traceability
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSupply chainTracingInteroperabilityProduct (mathematics)BusinessProduct testingComputer scienceRisk analysis (engineering)MarketingWorld Wide WebMathematics

Résumé

récupéré en direct d'OpenAlex

Despite the best efforts of food safety and food defense professionals, contaminated food continues to enter the food supply. It is imperative that contaminated food be removed from the supply chain as quickly as possible to protect public health and stabilize markets. To solve this problem, scores of technology companies purport to have the most effective, economical product tracing system. This study sought to compare and contrast the effectiveness of these systems at analyzing product tracing information to identify the contaminated ingredient and likely source, as well as distribution of the product. It also determined if these systems can work together to better secure the food supply (their interoperability). Institute of Food Technologists (IFT) hypothesized that when technology providers are given a full set of supply-chain data, even for a multi-ingredient product, their systems will generally be able to trace a contaminated product forward and backward through the supply chain. However, when provided with only a portion of supply-chain data, even for a product with a straightforward supply chain, it was expected that interoperability of the systems will be lacking and that there will be difficulty collaborating to identify sources and/or recipients of potentially contaminated product. IFT provided supply-chain data for one complex product to 9 product tracing technology providers, and then compared and contrasted their effectiveness at analyzing product tracing information to identify the contaminated ingredient and likely source, as well as distribution of the product. A vertically integrated foodservice restaurant agreed to work with IFT to secure data from its supply chain for both a multi-ingredient and a simpler product. Potential multi-ingredient products considered included canned tuna, supreme pizza, and beef tacos. IFT ensured that all supply-chain data collected did not include any proprietary information or information that would otherwise identify the supply-chain partner who provided the information prior to sharing this information with product tracing technology providers. The 9 traceability solution providers who agreed to participate in this project have their systems deployed in a wide range of sectors within the food industry including, but not limited to, livestock, dairy, produce, fruits, seafood, meat, and pork; as well as in pharmaceutical, automotive, retail, and other industries. Some have also been implemented across the globe including Canada, China, USA, Norway, and the EU, among others. This broad commercial use ensures that the findings of this work are applicable to a broad spectrum of the food system. Six of the 9 participants successfully completed the data entry phase of this test. To verify successful data entry for these 6, a demo or screenshots of the data set from each system's user interface was requested. Only 4 of the 6 were able to provide us with this evidence for verification. Of the 6 that completed data entry and moved on to the scenarios phase of the test, 5 were able to provide us with the responses to the scenarios. Time metrics were useful for evaluating the scalability and usability of each technology. Scalability was derived from the time it took to enter the nonstandardized data set into the system (ranges from 7 to 11 d). Usability was derived from the time it took to query the scenarios and provide the results (from a few hours to a week). The time was measured in days it took for the participants to respond after we supplied them all the information they would need to successfully execute each test/scenario. Two of the technology solution providers successfully implemented and participated in a proof-of-concept interoperable framework during Year 2 of this study. While not required, they also demonstrated this interoperability capability on the FSMA-mandated food product tracing pilots for the U.S. FDA. This has significant real-world impact since the demonstration of interoperability enables U.S. FDA to obtain evidence on the importance and impact of data-sharing moving forward. Another real-world accomplishment is the modification or upgrade of commercial technology solutions to enhance or implement interoperability. As these systems get deployed by clients in the food industry, interoperability will no longer be an afterthought but will be built into their traceability systems. In turn, industry and regulators will better understand the capabilities of the currently available technologies, and the technology provider community will identify ways in which their systems may be further developed to increase interoperability and utility.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,650
Score d'incertitude au seuil0,502

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,017
Tête enseignante GPT0,215
Écart entre enseignants0,198 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle