Determining classes of food items for health requirements and nutrition guidelines using Gaussian mixture models.

ABSTRACT:

Introduction

The identification of classes of nutritionally similar food items is important for creating food exchange lists to meet health requirements and for informing nutrition guidelines and campaigns. Cluster analysis methods can assign food items into classes based on the similarity in their nutrient contents. Finite mixture models use probabilistic classification with the advantage of taking into account the uncertainty of class thresholds.

Methods

This paper uses univariate Gaussian mixture models to determine the probabilistic classification of food items in the South African Food Composition Database (SAFCDB) based on nutrient content.

Results

Classifying food items by animal protein, fatty acid, available carbohydrate, total fibre, sodium, iron, vitamin A,

SUBMITTER: Balakrishna Y 

PROVIDER: S-EPMC10611470 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature