Artificial intelligence in gastronomy: transforming culinary innovation, food design, and consumer experience
DOI:
https://doi.org/10.5281/zenodo.21864481Keywords:
artificial intelligence, gastronomy, culinary innovation, food design, personalized gastronomy, consumer experienceAbstract
Artificial intelligence (AI) has expanded its application in gastronomy across six main areas: computational culinary knowledge, assisted creativity, food design and personalization, intelligent execution, enhanced experience, and cultural and consumer sustainability. Evidence indicates that AI enables the analysis of relationships among ingredients, the generation and reformulation of recipes, the optimization of products according to nutritional, sensory, and environmental criteria, the personalization of recommendations, and the support of culinary processes through robotics and computer vision. It also contributes to predicting preferences, reducing food waste, and promoting more sustainable consumer decisions. However, important limitations remain regarding data quality, sensory validation, privacy, user acceptance, authorship, algorithmic bias, and the representation of cultural diversity. In this context, the most promising approach is augmented gastronomy based on human–AI collaboration, in which technology complements, rather than replaces, human creativity, cultural knowledge, and sensory judgment.
Downloads
References
Agrawal, K., Goktas, P., Kumar, N., & Leung, M.-F. (2025). Artificial intelligence in personalized nutrition and food manufacturing: A comprehensive review of methods, applications, and future directions. Frontiers in Nutrition, 12, Article 1636980. https://doi.org/10.3389/fnut.2025.1636980
Ahn, Y. Y., Ahnert, S. E., Bagrow, J. P., & Barabási, A. L. (2011). Flavor network and the principles of food pairing. Scientific Reports, 1, Article 196. https://doi.org/10.1038/srep00196
Bagler, G., & Goel, M. (2024). Computational gastronomy: Capturing culinary creativity by making food computable. npj Systems Biology and Applications, 10(1), Arti¬cle 72. https://doi.org/10.1038/s41540-024-00399-5
Bondevik, J. N., Bennin, K. E., Babur, Ö., & Ersch, C. (2024). A systematic review on food recommender systems. Expert Systems with Applications, 238, Article 122166. https://doi.org/10.1016/j.eswa.2023.122166
Bossard, L., Guillaumin, M., & Van Gool, L. (2014). Food- 101: Mining discriminative components with random forests. In European Conference on Computer Vision (ECCV) (pp. 446–461). Springer. https://doi.org/10.1007/978-3-319-10599-4_29
Cao, Y., Kementchedjhieva, Y., Cui, R., Karamolegkou, A., Zhou, L., Dare, M., Donatelli, L., & Hershcovich, D. (2024). Cultural adaptation of recipes. Transactions of the Association for Computational Linguistics, 12, 80– 99. https://doi.org/10.1162/tacl_a_00634
Davis, N., Hsiao, C.-P., Singh, K. Y., & Magerko, B. (2016). Co-creative drawing agent with object recognition. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 12(1), 9–15. https://doi.org/10.1609/aiide.v12i1.12863
de Vries, A. (2023). The growing energy footprint of artificial intelligence. Joule, 7(10), 2191–2194. https://doi.org/10.1016/j.joule.2023.09.004
del Moral, R. G. (2025). Gastronomic paradigm shifts revisited: From culinary abstraction to a post-digital integrative cuisine. Frontiers in Nutrition, 12, Article 1649945. https://doi.org/10.3389/fnut.2025.1649945
del Moral, R. G. (2026). Artificial intelligence in gastronomy: Cognitive, functional and regenerative frameworks for fine dining. International Journal of Gastronomy and Food Science, 44, Article 101482. https://doi.org/10.1016/j.ijgfs.2026.101482
Ekincek, S., & Günay, S. (2023). A recipe for culinary creativity: Defining characteristics of creative chefs and their process. International Journal of Gastronomy and Food Science, 31, Article 100633. https://doi.org/10.1016/j.ijgfs.2022.100633
Fernandes, F. A. N., & Rodrigues, S. (2026). The digital transformation of food systems: A review of artificial intelligence in food technology. Processes, 14(11), Article 1789. https://doi.org/10.3390/pr14111789
Gurel, E. (2026). AI-driven experiences in cultural and creative industries: A review of literature and development of a multifaceted framework. The Service Industries Journal, 46(7–8), 583–622. https://doi.org/10.1080/02642069.2025.2542822
Hassoun, A., & Galanakis, C. M. (2025). Industry 4.0 technologies promote healthy and nutritious food. Discover Food, 5, Article 333. https://doi.org/10.1007/s44187-025-00639-5
Kawano, Y., & Yanai, K. (2014). FoodCam-256: A large-scale real-time mobile food recognition system employing high-dimensional features and compression of classifier weights. In Proceedings of the 22nd ACM International Conference on Multimedia (pp. 761–762). Association for Computing Machinery. https://doi.org/10.1145/2647868.2654869
Kim, H., Choi, S., & Shin, H. H. (2025). Artificial intelligence in the kitchen: Can humans be replaced in recipe creation and food production? International Journal of Contemporary Hospitality Management, 37(5), 1641– 1661. https://doi.org/10.1108/IJCHM-04-2024-0549
Kuhl, E. (2025). AI for food: Accelerating and democratizing discovery and innovation. npj Science of Food, 9, Article 82. https://doi.org/10.1038/s41538-025-00441-8
Lee, H. H., Shu, K., Achananuparp, P., Prasetyo, P. K., Liu, Y., Lim, E.-P., & Varshney, L. R. (2020). RecipeGPT: Generative pre-training based cooking recipe generation and evaluation system. In Companion pr¬ceedings of the Web Conference 2020 (pp. 181–184). Association for Computing Machinery. https://doi.org/10.1145/3366424.3383536
McGuire, J., De Cremer, D., & Van de Cruys, T. (2024). Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence. Scientific Reports, 14, Article 18525. https://doi.org/10.1038/s41598-024-69423-2
Morales-Garzón, A., Gutiérrez-Batista, K., & Martin-Bautista, M. J. (2025). Adaptafood: An intelligent system to adapt recipes to specialised diets and healthy lifestyles. Multimedia Systems, 31, Article 87. https://doi.org/10.1007/s00530-025-01667-y
Motoki, K., Low, J., & Velasco, C. (2025). Generative AI framework for sensory and consumer research. Food Quality and Preference, 133, Article 105600. https://doi.org/10.1016/j.foodqual.2025.105600
Nunes, C. A., Ribeiro, M. N., de Carvalho, T. C. L., Ferreira, D. D., de Oliveira, L. L., & Pinheiro, A. C. M. (2023). Artificial intelligence in sensory and consumer studies of food products. Current Opinion in Food Science, 50, Article 101002. https://doi.org/10.1016/j.cofs.2023.101002
Oliveira, Â., Serra, P., & Fidalgo, F. (2025). Artificial intelligence in digital gastronomy: A systematic review and bibliometric analysis of trends and future directions [Preprint]. Preprints.org. https://doi.org/10.20944/preprints202512.1236.v1
Oz, E., & Oz, F. (2025). Artificial intelligence-enabled ingredient substitution in food systems: A review and conceptual framework for sensory, functional, nutritional, and cultural optimization. Foods, 14(22), Article 3919. https://doi.org/10.3390/foods14223919
Park, D., Kim, K., Kim, S., Spranger, M., & Kang, J. (2021). FlavorGraph: A large-scale food-chemical graph for generating food representations and recommending food pairings. Scientific Reports, 11, Article 931. https://doi.org/10.1038/s41598-020-79422-8
Pennells, J., Watkins, P., Bowler, A. L., Watson, N. J., & Knoerzer, K. (2025). Mapping the AI landscape in food science and engineering: A bibliometric analysis enhan¬ced with interactive digital tools and company case studies. Food Engineering Reviews, 17, 465–489. https://doi.org/10.1007/s12393-025-09413-w
Pieroni, A. (2023). Gastronomy: Fostering a new and inclu¬sive scientific field. Gastronomy, 1(1), 1–2. https://doi.org/10.3390/gastronomy1010001
Rita, L., Southern, J., Laponogov, I., Higgins, K., & Veselkov, K. (2024). Optimizing ingredient substitution using large language models to enhance phytochemical content in recipes. Machine Learning and Knowledge Extraction, 6(4), 2738–2752. https://doi.org/10.3390/make6040131
Senath, T., Athukorala, K., Costa, R., Ranathunga, S., & Kaur, R. (2025). Large language models for ingredient substitution in food recipes using supervised fine-tuning and direct preference optimization. Natural Language Processing Journal, 12, Article 100177. https://doi.org/10.1016/j.nlp.2025.100177
Şener, E., & Ulu, E. K. (2024). Culinary innovation: Will the future of chefs’ creativity be shaped by AI technolo-gies? Tourism: An International Interdisciplinary Journal, 72(3), 340–352. https://doi.org/10.37741/t.72.3.4
Shettigar, Y. V., & Sumangala, N. (2026). The role of artificial intelligence in transforming traditional restaurants into smart ecosystems. International Journal of Engineering Research & Technology (IJERT), 14(1), 1–6. https://doi.org/10.17577/IJERTCONV14IS010007
Siddique, A., Gupta, A., Sawyer, J. T., Huang, T.-S., & Morey, A. (2025). Big data analytics in food industry: A state-of-the-art literature review. npj Science of Food, 9, Article 36. https://doi.org/10.1038/s41538-025-00394-y
Siraj, S., & Khan, M. S. U. H. (2024). The evolution of culinary arts: From traditional techniques to modernist cuisine. International Journal of Contemporary Issues in Social Sciences, 3(2), 3700–3705. https://ijciss.org/index.php/ijciss/article/view/1339
Sochacki, G., Zhang, X., Abdulali, A., & Iida, F. (2024). Towards practical robotic chef: Review of relevant work and future challenges. Journal of Field Robotics, 41(5), 1596–1616. https://doi.org/10.1002/rob.22321
Song, Z., & Li, B. (2026). Computational recipe intelligence: A survey of recipe design, generation, recommendation, and evaluation protocols. Electronics, 15(15), Article 3281. https://doi.org/10.3390/electronics15153281
Tac, V., Gardner, C. D., & Kuhl, E. (2026). Generative artificial intelligence creates delicious, sustainable, and nutritious burgers. npj Science of Food, 10(1), Article 199. https://doi.org/10.1038/s41538-026-00953-x
Velasco, C., Michel, C., & Spence, C. (2021). Gastrophysics: Current approaches and future directions. International Journal of Food Design, 6, 137–152. https://doi.org/10.1386/ijfd_00028_2
Wang, W., Chen, Z., & Kuang, J. (2025). Artificial intelligence-driven recommendations and functional food purchases: Understanding consumer decision-making. Foods, 14(6), Article 976. https://doi.org/10.3390/foods14060976
Wang, X., Sun, Z., Xue, H., & An, R. (2025). Artificial intelligence applications to personalized dietary recommendations: A systematic review. Healthcare, 13(12), Article 1417. https://doi.org/10.3390/healthcare13121417
Wang, Z., Hirai, S., & Kawamura, S. (2022). Challenges and opportunities in robotic food handling: A review. Frontiers in Robotics and AI, 8, Article 789107. https://doi.org/10.3389/frobt.2021.789107
Xu, J.-L., Argyropoulos, D., & da Costa, T. (2026). Generative artificial intelligence shaping the future of agri-food innovation. AgriFood: Journal of Agricultural Products for Food, 2(1), 23–29. https://doi.org/10.1002/agf2.70009
Zhou, P., Min, W., Fu, C., Jin, Y., Huang, M., Li, X., Mei, S., & Jiang, S. (2025). FoodSky: A food-oriented large language model that can pass the chef and dietetic examinations. Patterns, 6(5), Article 101234. https://doi.org/10.1016/j.patter.2025.101234
Published
Data Availability Statement
Not applicable.
Issue
Section
License
Copyright (c) 2026 Osmel Torres, Luis E. Márquez (Author)

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.




































