The networks of ingredient combination in cuisines around the world

2024-08-27

arXiv.org

AI Summary

This study investigates the organizational principles of flavor profiles across global cuisines and their cultural differentiation. Method: We propose a computational framework based on ingredient co-occurrence networks—constructing cuisine-specific flavor networks where nodes represent ingredients and edge weights reflect co-occurrence frequencies across recipes. We introduce, for the first time in gastronomy research, a complex-network skeleton extraction method to identify a three-tier flavor signature system: popular ingredients, high-frequency ingredient pairs, and network backbone structures. Integrating graph-theoretic analysis, clustering algorithms, and statistical modeling, our approach enables automatic cuisine classification using only partial recipe subsets. Results: The method accurately partitions cuisines into geographically and culturally coherent clusters in cross-cultural comparisons. This work establishes an interpretable theoretical model and a reusable methodological framework for computational gastronomy, supporting applications in cuisine identification, cultural adaptability analysis, and novel recipe generation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Social Networks and Social Media: Computational social science
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Investigating how different ingredients are combined in popular dishes is crucial to reveal the fundamental principles behind the formation of food preferences. Here, we use data from food repositories and network analysis to characterize worldwide cuisines. In our framework, each cuisine is represented as a network, where nodes correspond to ingredient types and weighted links describe how frequently pairs of ingredient types appear together in recipes. The networks of ingredient combinations reveal cuisine-specific patterns, highlighting similarities and differences in gastronomic preferences across different world regions. We find that popular ingredients, recurrent combinations, and the way they are organized within the backbone of the network provide a unique fingerprint for each cuisine. Hence, we demonstrate that networks of ingredient combinations are able to cluster global cuisines into meaningful geo-cultural groups, and can also be used to train models to uniquely identify a cuisine from a subset of its recipes. Our study advances our understanding of food combinations and helps uncover the geography of taste, paving the way for the creation of new and innovative recipes.

Problem

• Characterizing worldwide cuisines through ingredient combination networks
• Identifying distinctive culinary fingerprints using network analysis
• Enabling data-driven cuisine classification and cross-cultural comparison

Innovation

• Network analysis of ingredient co-occurrence patterns
• Maximum spanning trees as simplified cuisine backbones
• Machine learning models for cuisine identification accuracy