Supervised machine learning for nutrient and contaminant profiling and benefit-risk assessment of Arctic seafood

Supervised machine learning for nutrient and contaminant profiling and benefit-risk assessment of Arctic seafood

Arctic seafood is a crucial source of nutrients due to its richness in proteins, omega-3 polyunsaturated fatty acids, vitamins and minerals. However, contaminants including Hg and persistent organic pollutants (POPs) in fish may pose a health concern for consumers. This study aimed to profile concen...

Full description

Saved in:
Bibliographic Details
Main Authors: Quang Tri Ho, Ole Jakob Nøstbakken, Monica Sanden, Lene Secher Myrmel, Martin Wiech, Annette Bernhard, Bente M. Nilsen, Amund Maage, Lisbeth Dahl
Format: Article
Language:English
Published: Elsevier 2025-08-01
Series:Environment International
Subjects:
Nutrients
EPA+DHA
Iodine
POPs
Dioxins and PCBs
Hg
Online Access:http://www.sciencedirect.com/science/article/pii/S0160412025004507
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://www.sciencedirect.com/science/article/pii/S0160412025004507

Similar Items