Emerging Data Governance Issues in Big Data Applications for Food Safety

Sapienza, Salvatore; Palmirani, Monica

doi:10.1007/978-3-319-98349-3_17

Cited by 5 publications

(7 citation statements)

References 11 publications

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“…The facilitation of information and data sharing with developing nations would help to reduce the digital gap between LMICs and resource rich countries. With the fast development of big data applications in developed countries, data privacy and security issues remain a challenge and should be further addressed (Sapienza & Palmirani, 2018). With the increasing digitalization of the systems, cyber risks may emerge concurrently, which makes it imperative to ensure strong cybersecurity.…”

Section: Discussion and Synthesismentioning

confidence: 99%

Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools

Mu,

Kleter,

Bouzembrak

et al. 2024

Comp Rev Food Sci Food Safe

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To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify emerging food safety risks and to provide early warning signals in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things as part of early warning and emerging risk identification tools and methods in the food safety domain. There is an ongoing rapid development of systems fed by numerous, real‐time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real‐time food safety risk early warning systems. Although these developments increase the feasibility and effectiveness of prospective early warning and emerging risk identification tools, their implementation may prove challenging, particularly for low‐ and middle‐income countries due to low connectivity and data availability. It is advocated to overcome these challenges by improving the capability and capacity of national authorities, as well as by enhancing their collaboration with the private sector and international organizations.

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Section: Discussion and Synthesismentioning

confidence: 99%

Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools

Mu,

Kleter,

Bouzembrak

et al. 2024

Comp Rev Food Sci Food Safe

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“…After all, and on a positive note, a certain degree of consistency on high-level bioethical principles and AI principles has already been identified by qualified working groups [44], thus paving the way for a progressive integration at applied levels such as agritech. Moreover, this method has been proved to be correct elsewhere, when applied to food safety [33,45]. Furthermore, ethical contributions might be necessary to foster the current debate on forthcoming pieces of legislation in the EU (Data Act and Artificial Intelligence Act).…”

Section: Possible Research Trajectories and Their Connectionsmentioning

confidence: 99%

Smart Solutions in AgriTech: Research Trajectories in the Digital Transition

Sapienza

2022

Electronic Government and the Information Systems Perspective

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Some rights reserved. The terms and conditions for the reuse of this version of the manuscript are specified in the publishing policy. For all terms of use and more information see the publisher's website. This is the final peer-reviewed author's accepted manuscript (postprint) of the following publication:This item was downloaded from IRIS Università di Bologna (https://cris.unibo.it/).When citing, please refer to the published version.

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“…The privacy issue remains to be a significant concern in cultures when it comes to digitalized information, particularly social media data (Sapienza & Palmirani, 2018). Limited data access and anonymization of sensitive information are two primary methods of protecting privacy (Wu, Zhu, Wu, & Ding, 2014).…”

Section: Challengesmentioning

confidence: 99%

Utilization of text mining as a big data analysis tool for food science and nutrition

Tao

Yang

Feng

2020

Comp Rev Food Sci Food Safe

157

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Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument‐generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word‐level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer‐opinion mining, new‐product development, food knowledge discovery, food supply‐chain management, and online food services. The goal is to provide insights for intelligent decision‐making to improve food production, food safety, and human nutrition.

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Emerging Data Governance Issues in Big Data Applications for Food Safety

Cited by 5 publications

References 11 publications

Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools

Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools

Smart Solutions in AgriTech: Research Trajectories in the Digital Transition

Utilization of text mining as a big data analysis tool for food science and nutrition

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