Evaluating LLM Accuracy in Predicting Peruvian Meal Nutrition.
TL;DR
LLMs can generate estimates of nutrient content from narrative descriptions of Peruvian meals, but current performance levels fall short of the precision required for clinical deployment or commercial consumer-facing applications.
Abstract
Background
Artificial intelligence applications have been developed to predict the nutrient content of meals. However, none have been evaluated in the context of Peruvian cuisine, characterized by diverse ingredients and recipes. We assessed whether large language models (LLMs) could predict the nutritional content of Peruvian meals.
Methods
Using a dataset of 510 unique lunch images extracted from a Peruvian cookbook, we compared nutrient values from recipe data against predictions generated by LLMs (Gemma-3 4B, 12B, and 27B). The LLMs were given the meal name and a photograph and prompted to produce narrative descriptions of the meal. Using the descriptions, the same LLMs were prompted to estimate six nutrients: energy (kcal/serving), protein (g/serving), carbohydrates (g/serving), iron (mg/serving), vitamin A (μg/serving), and zinc (mg/serving). Agreement proportions and errors metrics were calculated against the values from the recipe book.
Results
The 27B LLM achieved the highest agreement proportions across most nutrients-calories (45%), carbohydrates (31%), iron (15%), vitamin A (19%), and zinc (31%)-while the 12B model performed best for protein (70% agreement). The 27B model yielded the lowest mean absolute error (MAE) for calories (108 kcal), carbohydrates (26 g), iron (4 mg), and zinc (1 mg). The 12B LLM had the lowest MAE for protein (6 g) and vitamin A (667 μg). The 4B LLM showed the poorest performance across metrics.
Conclusions
LLMs can generate estimates of nutrient content from narrative descriptions of Peruvian meals, but current performance levels fall short of the precision required for clinical deployment or commercial consumer-facing applications.