Blockchain and Artificial Intelligence for Wine Industry Traceability and Recommendation: Current Trends and Research Gaps
Downloads
The wine industry faces growing demands for supply chain transparency, product authenticity verification, and consumer-oriented digital services. This paper presents a literature review of blockchain and artificial intelligence (AI) technologies and their applications in the wine industry. Drawing on existing research, the study examines the foundational principles of blockchain technology and its deployment in supply chain management, AI-based recommendation systems and their adaptation to wine-food pairing tasks, the digital transformation of the wine industry within Industry 4.0 and 5.0 frameworks, and fuzzy logic-based multi-criteria decision-making (MCDM) models for technology evaluation. The review identifies key research gaps, including the limited development of hybrid systems that integrate supply chain traceability with AI-driven recommendations in a unified ecosystem, insufficient application of fuzzy MCDM methods in blockchain platform selection for domain-specific contexts, and the underdevelopment of cross-cultural wine-food pairing models. The analysis demonstrates that the convergence of blockchain and AI technologies offers a promising foundation for the digital transformation of the wine industry, combining supply chain transparency with intelligent, personalized consumer services. Future research directions are discussed, emphasizing the need for integrated hybrid approaches that address the identified gaps.
Downloads
Abeyratne, S., & Monfared, R. (2016). Blockchain ready manufacturing supply chain using distributed ledger. International journal of research in engineering and technology, 5(9),, 1-10.
Adamashvili, N., Zhizhilashvili, N., & Tricase, C. (2024). The integration of the internet of things, artificial intelligence, and blockchain technology for advancing the wine supply chain. Computers, 13(3), 72.
Adomavicius, G. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, 17(6).
Aggarwal, C. C. (2016). Recommender Systems: The Textbook. doi:https://doi.org/10.1007/978-3-319-29659-3
Antonopoulos, A. M. (2014). Mastering Bitcoin: unlocking digital cryptocurrencies. O'Reilly Media, Inc.
Antonopoulos, A. M. (2018). Mastering ethereum: building smart contracts and dapps. O'reilly Media.
Belotti, M., Božić, N., Pujolle., G., & Secci, S. (2019). A vademecum on blockchain technologies: When, which, and how. IEEE Communications Surveys & Tutorials, 21(4), 3796-3838.
Biswas, K. M. (2017). Blockchain based wine supply chain traceability system.
Burke, R. (2000). Knowledge-Based Recommender Systems. Encyclopedia of library and information systems.
Burke, R. (2002). Hybrid recommender systems. User modeling and user-adapted interaction, 12(4), 331-370.
Buterin, V. (2013). Ethereum Whitepaper: A Next Generation Smart.
Cambria, E., & White, B. (2014). Jumping NLP curves: A review of natural language processing research. IEEE Computational intelligence magazine, 9(2),, (pp. 48-57). doi:10.1109/MCI.2014.230722
Carayannis, E. G., & Morawska-Jancelewicz, J. (2022). The futures of Europe: Society 5.0 and Industry 5.0 as driving forces of future universities. Journal of the Knowledge Economy, 13(4), , 3445-3471.
Casino, F. D. (2019). A systematic literature review of blockchain-based applications: Current status, classification and open issues. (pp. 55–81). Telematics and Informatics, 36,.
Chiaraluce, G., Bentivoglio, D., Finco, A., Fiore, M., & Galati, A. (2024). Exploring the role of blockchain technology in modern high-value food supply chains: global trends and future research directions. Agricultural and Food Economics, 12(1), 6.
Christidis, K. &. (2016). Blockchains and smart contracts for the internet of things. . (pp. 2292-2303). IEEE access, 4,.
Cimino, M. G. (2012). Enabling traceability in the wine supply chain. (pp. 397-412). Springer Berlin Heidelberg.
Copigneaux, B. V. (2020). Blockchain for supply chains and international trade.
Danese, P. M. (2021). Designing blockchain systems to prevent counterfeiting in wine supply chains: a multiple-case study. International Journal of Operations & Production Management, 41(13), 1-33.
Galvez, J. F.-G. (2018). Future challenges on the use of blockchain for food traceability analysis. (pp. , 222–232). Trends in Analytical Chemistry. doi:10.1016/j.trac.2018.08.011
Gupta, G., & Katarya, R. (2024). A computational approach towards food-wine recommendations. Expert Systems with Applications.
Harrington, R. J. (2007). Food and wine pairing: A sensory experience. John Wiley & Sons.
Jannach, D., Zanker, M., Felfernig, A., & Friedric. (2010). Recommender systems: an introduction. Cambridge university press.
Javaid, M., Haleem, A., Singh, R., & Suman, R. (2022). Artificial intelligence applications for industry 4.0: A literature-based study. Journal of Industrial Integration and Management, 7(01), 83-111.
Jurafsky, D. (2000). Speech and language processing. . Prentice-Hall.
Kahraman, C. (2008). Fuzzy multi-criteria decision making: Theory and applications with recent developments. Springer. doi:10.1007/978-0-387-76813-7
Kamilaris et al. (2019). The rise of blockchain technology in agriculture and food supply chains. Trends in food science & technology, (pp. 640-652).
Kaur, A., Potdar, V., & Agrawal, H. (2023). Industry 4.0 adoption in food supply chain to improve visibility and operational efficiency—a content analysis. (pp. 73922-73958). IEEE Access 11.
Kechagias, E. P. (2023). An Ethereum-Based Distributed Application for Enhancing Food Supply Chain Traceability., (pp. 12(6), 1220). doi:10.3390/foods12061220
King, M., & Cliff, M. (2005). Evaluation of ideal wine and cheese pairs using a deviation‐from‐ideal scale with food and wine experts. Journal of food quality, 28(3),, 245-256.
Kshetri, N. (2018). Blockchain’s roles in meeting key supply chain management objectives. (pp. 80-89). International Journal of information management, 39. doi:10.1016/j.ijinfomgt.2017.12.005
Leng, J., Ruan, G., Jiang, P., Xu, K., & Liu, Q. (2020). Blockchain-empowered sustainable manufacturing and product lifecycle management in industry 4.0: A survey. Renewable and sustainable energy reviews, 132. doi:10.1016/j.rser.2020.110112
Matese, A., & Di Gennaro, S. (2015). Technology in precision viticulture: A state of the art review. International Journal of Wine Research, 7, 69–81. doi:10.2147/IJWR.S69405
Muradi, S. B. (2022). Systematic selection of blockchain platforms using fuzzy AHP-TOPSIS. University of Malaya.
Nahavandi, S. (2019). Industry 5.0—A Human-Centric Solution. Sustainability, 11(16). doi:doi.org/10.3390/su11164371
Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System.
Parry, G. M. (2024). Bottling up trust: a review of blockchain adoption in wine supply chain traceability. IEEE Access. doi:10.1109/ACCESS.2024.3505428
Patsatsia, G. (2026). The Science Behind Wine and Food Pairing. Georgian Scientists. Vol. 8 Issue 2. doi:https://doi.org/10.52340/gs.2026.08.02.56
Pesme, J. O. (2023). Tracing and tracking wine bottles: Protecting consumers and producers. 44th World Congress of Vine and Wine. doi:10.1051/bioconf/20236803028
Prescott, J. (2013 ). Taste matters: Why we like the foods we do. Reaktion Books.
Ricci, F., Rokach, L., & Shapira, B. (2021). Recommender systems: Techniques, applications, and challenges. Recommender systems handbook, 1-35.
Russell, S. J. (2010). Artificial intelligence a modern approach. Pearson Education, Inc.
Saberi, S. K. (2019). Blockchain technology and its relationships to sustainable supply chain management. (pp. 2117-2135). International journal of production research, 57(7).
Sirbiladze, G., Khutsishvili, I., & Ghvaberidze, B. (2014). Multistage decision-making fuzzy methodology for optimal investments based on experts’ evaluations. European journal of operational research, 232(1), 169-177.
Spence, C. (2020). Food and beverage flavour pairing: A critical review of the literature. . Food Research International, 133, , 109-124.
Taherdoost, H. (2023). Smart Contracts in Blockchain Technology: A Critical Review. Information, 14(2), 117. doi:10.3390/info14020117
Tian, F. (2017). A supply chain traceability system for food safety based on HACCP, blockchain & Internet of things. International Conference on Service Systems and Service Management (pp. 1–6). IEEE. doi:10.1109/ICSSSM.2017.7996119
Wang, L. X. (2021). Smart contract-based agricultural food supply chain traceability. (pp. 9296-9307). IEEE access, 9,.
Wohrer, M. &. (2018). Smart contracts: security patterns in the ethereum ecosystem and solidity. 2018 International Workshop on Blockchain Oriented Software Engineering (IWBOSE) (pp. 2-8). IEEE.
Xiang, W., Yu, K., Han, F., Fang, L., He, D., & Ha. (2023). Advanced manufacturing in industry 5.0: A survey of key enabling technologies and future trends. IEEE Transactions on Industrial Informatics, (pp. 1055-1068).
Xu, X, Lu, Y, Vogel-Heuser, B, & Wang, L. (2021). Industry 4.0 and Industry 5.0—Inception, conception and perception. Journal of manufacturing systems, 61,, 530-535. doi:10.1016/j.jmsy.2021.10.006
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353. doi:10.1016/S0019-9958(65)90241-X
Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2017). An overview of blockchain technology: Architecture, consensus, and future trends. (pp. 557-564). In 2017 IEEE international congress on big data (BigData congress).
Zimmermann, H. J. (2011). Fuzzy set theory—and its applications. . Springer Science & Business Media.
Copyright (c) 2026 Georgian Scientists
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
