Optimizing Ingredient Substitution Using Large Language Models to Enhance Phytochemical Content in Recipes
This work addresses the problem of promoting healthier eating practices through AI-driven recipe optimization for nutritionists and consumers, but it is incremental as it builds on existing datasets and methods.
This study tackled the problem of optimizing ingredient substitutions in recipes to enhance phytochemical content using large language models, resulting in improved Hit@1 accuracy from 34.53% to 38.03% on one dataset and from 40.24% to 54.46% on another, and generating 1,951 enriched pairings and 1,639 unique recipes.
In the emerging field of computational gastronomy, aligning culinary practices with scientifically supported nutritional goals is increasingly important. This study explores how large language models (LLMs) can be applied to optimize ingredient substitutions in recipes, specifically to enhance the phytochemical content of meals. Phytochemicals are bioactive compounds found in plants, which, based on preclinical studies, may offer potential health benefits. We fine-tuned models, including OpenAI's GPT-3.5, DaVinci, and Meta's TinyLlama, using an ingredient substitution dataset. These models were used to predict substitutions that enhance phytochemical content and create a corresponding enriched recipe dataset. Our approach improved Hit@1 accuracy on ingredient substitution tasks, from the baseline 34.53 plus-minus 0.10% to 38.03 plus-minus 0.28% on the original GISMo dataset, and from 40.24 plus-minus 0.36% to 54.46 plus-minus 0.29% on a refined version of the same dataset. These substitutions led to the creation of 1,951 phytochemically enriched ingredient pairings and 1,639 unique recipes. While this approach demonstrates potential in optimizing ingredient substitutions, caution must be taken when drawing conclusions about health benefits, as the claims are based on preclinical evidence. Future work should include clinical validation and broader datasets to further evaluate the nutritional impact of these substitutions. This research represents a step forward in using AI to promote healthier eating practices, providing potential pathways for integrating computational methods with nutritional science.