Prediction of Nutritional Content in Peruvian Lunch Meals by Large Language Models: A One-Shot Evaluation

BACKGROUND Various 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 across geographical regions. We assessed whether large language models (LLMs) could predict the nutritional content of Peruvian lunch meals.

Using a dataset of 510 unique lunch images extracted from a nationally representative Peruvian cookbook, we compared nutrient values from recipe data (ground truth) against predictions generated by three 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 only 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 ground truth.

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.

LLMs can generate estimates of nutrient content from narrative descriptions of Peruvian lunch meals, but current performance levels fall short of the accuracy needed for clinical or consumer-facing applications. Competing Interest Statement The authors have declared no competing interest. Funding Statement None. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability The original recipes are at available at: https://www.gob.pe/institucion/ins/colecciones/19559-recetarios-saludables-por-regiones The original photographs were obtained through a Freedom of Information request to CENAN. Consequently, we are unable to further distribute these photographs. Interested parties are encouraged to make a similar request to CENAN.