Tailoring the Nutritional Composition of Italian Foods to the US Nutrition5k Dataset for Food Image Recognition: Challenges and a Comparative Analysis.

ABSTRACT:

Background

Training of machine learning algorithms on dish images collected in other countries requires possible sources of systematic discrepancies, including country-specific food composition databases (FCDBs), to be tackled. The US Nutrition5k project provides for ~5000 dish images and related dish- and ingredient-level information on mass, energy, and macronutrients from the US FCDB. The aim of this study is to (1) identify challenges/solutions in linking the nutritional composition of Italian foods with food images from Nutrition5k and (2) assess potential differences in nutrient content estimated across the Italian and US FCDBs and their determinants.

Methods

After food matching, expert data curation, and handling of missing values, dish-level ingredients from Nutritio

SUBMITTER: Bianco R 

PROVIDER: S-EPMC11479105 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature