Journals / Safran Kültür ve Turizm Araştırmaları Dergisi / 2025 / Cilt: 8 Sayı: 3

The Impact of Gastronomy 4.0 Technologies on the Food Pairing Literature: A Bibliometric Analysis of Artificial Intelligence, 3D Printing, and Virtual Reality Applications

GASTRONOMİ 4.0 TEKNOLOJİLERİNİN FOOD PAİRİNG LİTERATÜRÜNE ETKİSİ: YAPAY ZEKÂ, 3D BASKI VE SANAL GERÇEKLİK UYGULAMALARININ BİBLİYOMETRİK ANALİZİ

Pages
402–419
Publication Date
2025-12-31
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Overall, Gastronomy 4.0 technologies are accelerating the transition of food pairing research from traditional sensory and chemical analyses toward algorithmic, model-based, and technology-integrated approaches.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In study, investigates the influence of Gastronomy 4.0 technologies—artificial intelligence, 3D printing, and virtual/augmented reality—on the evolution of the food pairing literature.
📊
Key Findings & Data:A total of 41 articles indexed in the Web of Science were analyzed using bibliometric techniques implemented through the bibliometrix and Biblioshiny packages.
💡
Conclusion & Impact:Overall, Gastronomy 4.0 technologies are accelerating the transition of food pairing research from traditional sensory and chemical analyses toward algorithmic, model-based, and technology-integrated approaches.
🔬Design:Modeling & Simulation
📍Scope:2017 Verileri
⚙️Variables:Food Pairing · Gastronomi 4.0 · Yapay Zekâ

Abstract

In study, investigates the influence of Gastronomy 4.0 technologies—artificial intelligence, 3D printing, and virtual/augmented reality—on the evolution of the food pairing literature. A total of 41 articles indexed in the Web of Science were analyzed using bibliometric techniques implemented through the bibliometrix and Biblioshiny packages. The analysis examined annual publication trends, leading authors, core journals, country productivity, keyword co-occurrence patterns, and thematic evolution. Results reveal a marked increase in research activity after 2017, with China and the United States emerging as dominant contributors. The findings further demonstrate that data-driven methodologies, computational flavor modeling, and digitally controlled food production systems have substantially reshaped the conceptual and methodological structure of the field. Overall, Gastronomy 4.0 technologies are accelerating the transition of food pairing research from traditional sensory and chemical analyses toward algorithmic, model-based, and technology-integrated approaches.

Özet

Çalışmada, Gastronomi 4.0 teknolojilerinin—yapay zekâ, 3D baskı ve sanal/artan gerçeklik uygulamalarının—food pairing literatürünün gelişimi üzerindeki etkisini incelemektedir. Web of Science veri tabanında indekslenen 41 makale, bibliometrix ve Biblioshiny paketleri kullanılarak bibliyometrik yöntemle analiz edilmiştir. Analiz kapsamında yıllara göre yayın eğilimleri, önde gelen yazar ve dergiler, ülke üretkenliği, anahtar kelime eş-oluşum ağları ve tematik gelişim haritaları değerlendirilmiştir. Bulgular, 2017 sonrası belirgin bir yayın artışı yaşandığını ve Çin ile ABD’nin alanda başat konumda olduğunu göstermektedir. Ayrıca veri bilimi, hesaplamalı lezzet modellemesi ve dijital gıda üretim sistemlerinin, food pairing araştırmalarının kavramsal çerçevesini önemli ölçüde dönüştürdüğü belirlenmiştir. Genel olarak Gastronomi 4.0 teknolojilerinin, food pairing çalışmalarını duyusal ve kimyasal analizlerden algoritmik, model-temelli ve teknoloji entegrasyonlu bir yapıya yönlendirdiği görülmektedir.

🔖

Cite This Article

Yurt, İ., Efe, S. N. (2025). The Impact of Gastronomy 4.0 Technologies on the Food Pairing Literature: A Bibliometric Analysis of Artificial Intelligence, 3D Printing, and Virtual Reality Applications. Safran Kültür ve Turizm Araştırmaları Dergisi, Cilt: 8 Sayı: 3, 402–419. https://ajindex.com/articles/1600509

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  1. Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.
  2. Chacko, A., Wang, Y., Reineccius, G., & Wang, T. (2023). Machine learning models for predicting odor characters. Journal of Agricultural and Food Chemistry, 71(32), 11936–11947. https://doi.org/10.1021/acs.jafc.3c02871
  3. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296.
  4. Fritz, M., Kou, X., Shi, P., Gao, C., Ma, P., & Zhang, D. (2023). Data-driven elucidation of flavor chemistry. Journal of Agricultural and Food Chemistry, 71(3), 1234–1248. https://doi.org/10.1021/acs.jafc.2c08345
  5. Guo, C., Zhang, M., & Bhandari, B. (2019). Model building and slicing in food 3D printing processes: A review. Comprehensive Reviews in Food Science and Food Safety, 18(6), 2011–2035.
  6. Guo, C., Zhang, M., & Bhandari, B. (2019). Model building and slicing in food 3D printing processes: A review. Comprehensive Reviews in Food Science and Food Safety, 18(6), 2011–2035. https://doi.org/10.1111/1541-4337.12417
  7. Hajmeer, M. N., Basheer, I. A., & Najjar, Y. M. (1997). Computational neural networks for predictive microbiology II: Application to microbial growth. International Journal of Food Microbiology, 34(1), 51–66.
  8. Hertafeld, E., Zhang, C., Jin, Z., Jakub, A., Russell, K., Lakehal, Y., Andreyeva, K., Bangalore, S. N., Mezquita, J., Blutinger, J., & Lipson, H. (2019). 3D printing and additive manufacturing: Multi-material FLM with simultaneous IR cooking. 3D Printing and Additive Manufacturing, 6(4), 1–15.
  9. Jain, A., Rakhi, N. K., & Bagler, G. (2015). Analysis of food pairing in regional cuisines of India. PLOS ONE, 10(10), e0139539.
  10. Kou, X., Shi, P., Gao, C., Ma, P., Xing, H., Huadong, H., & Zhang, D. (2023). Data-driven elucidation of flavor chemistry. Journal of Agricultural and Food Chemistry, 71(3), 1234–1248.
  11. Newsome, R. (2009). Development of a risk-ranking framework to evaluate potential high-threat microorganisms, toxins, and chemicals in food. Journal of Food Science, 74(5), R39–R45.
  12. Rodgers, S. (2016). Minimally processed functional foods: Technological and operational pathways. Journal of Food Science, 81(10), R2268–R2278.
  13. Tseng, Y. J., Chuang, P. J., & Appell, M. (2023). When machine learning and deep learning come to the big data in food chemistry. ACS Omega, 8(4), 3765–3780.
  14. Varvara, R. A., Szabo, K., & Vodnar, D. C. (2021). 3D food printing: Principles of obtaining digitally-designed nourishment. Nutrients, 13(11), 3617.
  15. Yu, P., Low, M. Y., & Zhou, W. (2018). Design of experiments and regression modelling in food flavour and sensory analysis: A review. Trends in Food Science & Technology, 71, 202–215.