Predicting Unreported Micronutrients From Food Labels: Machine Learning Approach.

ABSTRACT:

Background

Micronutrient deficiencies represent a major global health issue, with over 2 billion individuals experiencing deficiencies in essential vitamins and minerals. Food labels provide consumers with information regarding the nutritional content of food items and have been identified as a potential tool for improving diets. However, due to governmental regulations and the physical limitations of the labels, food labels often lack comprehensive information about the vitamins and minerals present in foods. As a result, information about most of the micronutrients is absent from existing food labels.

Objective

This paper aims to examine the possibility of using machine learning algorithms to predict unreported micronutrients such as vitamin A (retinol), vitamin C, vitamin

SUBMITTER: Razavi R 

PROVIDER: S-EPMC10134025 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature