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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.09445 |
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| _version_ | 1866917693413654528 |
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| author | Escolar, Emerson G. Shimada, Yuta Yuasa, Masahiro |
| author_facet | Escolar, Emerson G. Shimada, Yuta Yuasa, Masahiro |
| contents | In recent years, the use of data-driven methods has provided insights into underlying patterns and principles behind culinary recipes. In this exploratory work, we introduce the use of topological data analysis, especially persistent homology, in order to study the space of culinary recipes. In particular, persistent homology analysis provides a set of recipes surrounding the multiscale "holes" in the space of existing recipes. We then propose a method to generate novel ingredient combinations using combinatorial optimization on this topological information. We made biscuits using the novel ingredient combinations, which were confirmed to be acceptable enough by a sensory evaluation study. Our findings indicate that topological data analysis has the potential for providing new tools and insights in the study of culinary recipes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09445 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A topological analysis of the space of recipes Escolar, Emerson G. Shimada, Yuta Yuasa, Masahiro Algebraic Topology Machine Learning 62R40, 55N31, 90C27 In recent years, the use of data-driven methods has provided insights into underlying patterns and principles behind culinary recipes. In this exploratory work, we introduce the use of topological data analysis, especially persistent homology, in order to study the space of culinary recipes. In particular, persistent homology analysis provides a set of recipes surrounding the multiscale "holes" in the space of existing recipes. We then propose a method to generate novel ingredient combinations using combinatorial optimization on this topological information. We made biscuits using the novel ingredient combinations, which were confirmed to be acceptable enough by a sensory evaluation study. Our findings indicate that topological data analysis has the potential for providing new tools and insights in the study of culinary recipes. |
| title | A topological analysis of the space of recipes |
| topic | Algebraic Topology Machine Learning 62R40, 55N31, 90C27 |
| url | https://arxiv.org/abs/2406.09445 |