Vol. 5 No. 1 (2025): Issue 5
Articles

Governing Food Nutrition Feature Analysis: A Gaussian Mixture Model-based Approach

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  • Julien Lefevre,
  • Camille Dubois,
  • Erik Nilsson,
  • Antoine Roche
Julien Lefevre
Research Institute for Sustainable Food Systems, Angers Institute of Agriculture, Angers, 49000, France
Camille Dubois
Nutritional Science and Health Research Center, Orleans University of Biological Studies, Orleans, 45000, France
Antoine Roche
Center for Advanced Nutritional Analysis, Reims Faculty of Applied Sciences, Reims, 51100, France
DOI: https://doi.org/10.71070/oaml.v5i1.134

Published 2025-01-27

Keywords

  • Nutrition Analysis,
  • Public Health,
  • Statistical Methods,
  • Gaussian Mixture Models,
  • High-Dimensional Data

How to Cite

Lefevre, J., Dubois, C., Nilsson, E., & Roche, A. (2025). Governing Food Nutrition Feature Analysis: A Gaussian Mixture Model-based Approach. Optimizations in Applied Machine Learning, 5(1). https://doi.org/10.71070/oaml.v5i1.134
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Food nutrition is a critical aspect of public health, with increasing attention being paid to the analysis and monitoring of its governing features. However, existing research lacks advanced analytical techniques to effectively capture the complex dynamics of food nutrition. This paper reviews the current state of food nutrition analysis and identifies the challenges faced, including the limitations of traditional statistical methods in handling the high-dimensional nature of nutrition data. To address these issues, we propose a novel approach based on Gaussian Mixture Models, which offer a more flexible and accurate representation of the underlying structure of food nutrition features. Our innovative method provides a promising avenue for improving the understanding and management of food nutrition, ultimately contributing to the enhancement of public health strategies.

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