Gaussian Mixture Models for Identifying Ultra-Processed Foods

Strickland, Joshua; Dare, Murry; Wang, Wenjia

doi:10.1007/978-3-032-29292-6_9

Gaussian Mixture Models for Identifying Ultra-Processed Foods

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Strickland, Joshua, Dare, Murry and Wang, Wenjia (2026) Gaussian Mixture Models for Identifying Ultra-Processed Foods. In: 21st International Conference on Hybrid Artificial Intelligence Systems, 2026-06-18 - 2026-06-19.

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Abstract

Ultra-processed foods (UPFs) are industrial formulations of multiple ingredients to make a highly palatable food product and account for nearly half of the average British diet and most developed countries. UPFs are strongly associated with obesity, chronic disease, and increased mortality. However, the identification of UPFs is currently still heavily relied on by human experts, making consistent and large-scale classification very difficult and resource intensive. This research explores a semi-supervised pipeline in which available labels are leveraged during preprocessing and performance evaluation, but not used in unsupervised clustering tasks. We investigated and tested Gaussian Mixture Models (GMM) and few other commonly used clustering methods on real-world data, and then compared them with a baseline method k-means. This reflects the real-world scenario where food databases contain partially labelled data but full annotation remains impractical. These results demonstrate that unsupervised learning offers a viable and scalable option for automated food categorization where labelled data is scarce.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	nova classification method,ultra-processed food,unsupervised machine learning,theoretical computer science,general computer science,sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/2600/2614
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Health Computing
Related URLs:	https://www.scopus.com/pages/publication...
Depositing User:	LivePure Connector
Date Deposited:	16 Jul 2026 09:12
Last Modified:	20 Jul 2026 10:10
URI:	https://ueaeprints.uea.ac.uk/id/eprint/103872
DOI:	10.1007/978-3-032-29292-6_9

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