Blockchain technology and traceability in the agrifood industry

Patelli, Niccolò; Mandrioli, Mauro

doi:10.1111/1750-3841.15477

Cited by 66 publications

(32 citation statements)

References 51 publications

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“…Furthermore, the e-nose may be installed as a stand-alone device at different stages of the production line to assess beer quality and detect any faults in real-time to take corrective actions in the processing line before the final product. These parameters obtained from the new system proposed here can be used as an integrative system coupled with blockchain for traceability and authentication of beers [62,63].…”

Section: Artificial Intelligence Applied To Beer Quality Assessmentmentioning

confidence: 99%

Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning

Viejo

Fuentes

2020

Fermentation

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Beer quality is a difficult concept to describe and assess by physicochemical and sensory analysis due to the complexity of beer appreciation and acceptability by consumers, which can be dynamic and related to changes in climate affecting raw materials, consumer preference, and rising quality requirements. Artificial intelligence (AI) may offer unique capabilities based on the integration of sensor technology, robotics, and data analysis using machine learning (ML) to identify specific quality traits and process modifications to produce quality beers. This research presented the integration and implementation of AI technology based on low-cost sensor networks in the form of an electronic nose (e-nose), robotics, and ML. Results of ML showed high accuracy (97%) in the identification of fermentation type (Model 1) based on e-nose data; prediction of consumer acceptability from near-infrared (Model 2; R = 0.90) and e-nose data (Model 3; R = 0.95), and physicochemical and colorimetry of beers from e-nose data. The use of the RoboBEER coupled with the e-nose and AI could be used by brewers to assess the fermentation process, quality of beers, detection of faults, traceability, and authentication purposes in an affordable, user-friendly, and accurate manner.

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Section: Artificial Intelligence Applied To Beer Quality Assessmentmentioning

confidence: 99%

Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning

Viejo

Fuentes

2020

Fermentation

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“…Downstream blockchain technology will help record data and protect data in the system so that it cannot be modified or lost. In the future, blockchain technology may be applied to increase transparency, sustainability, safety and security, fair trade, and price equality [127,170]. This technology can identify suppliers or buyers for the farmers.…”

Section: Discussion and Suggestions For Future Workmentioning

confidence: 99%

Enhancing Coffee Supply Chain towards Sustainable Growth with Big Data and Modern Agricultural Technologies

2021

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Modern agricultural technology management is nowadays crucial in terms of the economy and the global market, while food safety, quality control, and environmentally friendly practices should not be neglected. This review aims to give perspectives on applying big data analytic and modern technologies to increase the efficacy and effectiveness of the coffee supply chain throughout the process. It was revealed that several tools such as wireless sensor networks, cloud computing, Internet of Things (IoT), image processing, convolutional neural networks (CNN), and remote sensing could be implemented in and used to improve the coffee supply chain. Those tools could help in reducing cost as well as time for entrepreneurs and create a reliable service for the customer. It can be summarized that in the long term, these modern technologies will be able to assist coffee business management and ensure the sustainable growth for the coffee industry.

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“…In this case, traceability would be guaranteed, but validation is subject to the assessment and interpretations of those involved. Such cases have also been cited in studies on the integrity of food (Ling and Wahab, 2020), the analysis of different case studies (Patelli and Mandrioli, 2020) and traceability in agriculture (Demestichas et al , 2020). However, all of them lack analysis on the interceptions to which the data are subject.…”

Section: Case Analysismentioning

confidence: 99%

Blockchain in operations for food service distribution: steps before implementation

Vivaldini

2021

IJLM

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PurposeConsidering the importance of a safe food chain for consumers and the advent of blockchain technology (BT), this research studies a food service (FS) distributor. The research aims to understand the implications related to the functional processes of distribution in FS in which it would be possible to use blockchain to achieve agility, transparency of information and improvements in food safety.Design/methodology/approachFirstly, theory regarding blockchain technology in the supply chain (BT-SC) and FS was analyzed to contextualize the theme conceptually. A single case study including 11 supply chain companies was applied in a BT implementation study in an FS distributor.FindingsInvestment in infrastructure is often identified as a barrier to adoption of BT-SC. This was, however, not found in this case. Furthermore, the validation of users was only necessary for those parties directly participating in the process or information input. Finally, findings differentiate between qualifying criteria and operational processes when considering BT projects in FS.Research limitations/implicationsThe findings are restricted to this single case that provided an in-depth understanding of the topic. Statistical generalization is not possible at this stage of the research.Practical implicationsThe study is a practical example and can provide several insights to anyone looking to implement BT in their SC.Social implicationsThe social importance of the study lies in the importance of FS in the food sector, and by presenting ways that contribute to mitigating risks to consumers.Originality/valueReal-life cases of application of BT-SC illustrate its functionalities in operational processes.

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Blockchain technology and traceability in the agrifood industry

Cited by 66 publications

References 51 publications

Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning

Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning

Enhancing Coffee Supply Chain towards Sustainable Growth with Big Data and Modern Agricultural Technologies

Blockchain in operations for food service distribution: steps before implementation

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