Food Development through Co-creation with AI: bread with a "taste of love"
Takuya Sera, Izumi Kuwata, Yuki Taya, Noritaka Shimura, Yosuke Motohashi
TL;DR
The study investigates AI-assisted, emotion-driven food development by translating romance-themed atmospheres into ingredient choices for a set of five breads called Romance Bread. It leverages a multimodal pipeline (NESA for transcription, DE for emotion enrichment, and cotomi LLM for generation) to produce $32$-dimensional emotion vectors and uses cosine similarity to select top ingredients from a large lyric-derived pool, followed by human bread development and product-description generation. Evaluations with $n=31$ tasters yield an overall accuracy of $43.8\%$, with notably higher precision for the Jealousy flavor at $65.6\%$, suggesting meaningful alignment between AI cues and human taste, though some themes (e.g., Mutual Love) remain harder to distinguish. The findings support the potential of AI–human collaboration to create emotionally engaging food experiences, while also highlighting opportunities to extend the approach with interactive feedback and broader multimodal design in future work.
Abstract
This study explores a new method in food development by utilizing AI including generative AI, aiming to craft products that delight the senses and resonate with consumers' emotions. The food ingredient recommendation approach used in this study can be considered as a form of multimodal generation in a broad sense, as it takes text as input and outputs food ingredient candidates. This Study focused on producing "Romance Bread," a collection of breads infused with flavors that reflect the nuances of a romantic Japanese television program. We analyzed conversations from TV programs and lyrics from songs featuring fruits and sweets to recommend ingredients that express romantic feelings. Based on these recommendations, the bread developers then considered the flavoring of the bread and developed new bread varieties. The research included a tasting evaluation involving 31 participants and interviews with the product developers. Findings indicate a notable correlation between tastes generated by AI and human preferences. This study validates the concept of using AI in food innovation and highlights the broad potential for developing unique consumer experiences that focus on emotional engagement through AI and human collaboration.