Early warning predictions of contaminants in animal feed using machine learning.

ABSTRACT: Contaminated feed can lead to negative health effects in both animals and humans, as well as result in economic losses. In order to support risk-based monitoring of animal feed, it is essential that the potential presence of food safety hazards can be predicted in an accurate and timely manner. This study used machine learning to predict the presence of four contaminant groups in animal feed: mycotoxins, heavy metals, dioxins and pesticides. Emphasis was placed on a holistic design, using a broad range of external drivers that may affect the presence of the contaminants as model input. Data included historical feed safety monitoring data related to a wide range of different animal feeds, countries, and years (2010-2023) as well as a variety of socioeconomic and weather indicators related t

SUBMITTER: van den Bulk LM 

PROVIDER: S-EPMC12749966 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature