On Wednesday, 10 September 2025, Andrea Rakel Sigurðardóttir will defend her doctoral thesis in Health Sciences at the Faculty of Food Science and Nutrition at the University of Iceland. The thesis is entitled: Automatic Multispectral and Imaging Methods for Quality Monitoring of Seafood.
The opponents are Dr. Frosti Pálsson, scientist at deCODE Genetics, and Dr. Silje Ottestad, specialist at Maritech in Norway.
The supervisor and advisor was María Guðjónsdóttir, Professor. Also serving on the doctoral committee were Hafsteinn Einarsson, Associate Professor, Hildur Inga Sveinsdóttir, Lecturer and Project Manager at Matís, and Nette Schultz, PhD, Chief Innovation Officer (CINO) at Videometer A/S in Denmark.
Ólafur Ögmundarson, Associate Professor and Head of the Faculty of Food Science and Nutrition, will chair the ceremony, which will take place in the Ceremonial Hall of the University of Iceland and begins at 13:00.
Abstract
Sustainable utilisation of marine resources is a prerequisite for ensuring the long-term supply of seafood and for protecting marine ecosystems. At the same time, quality and safety monitoring throughout the entire value chain is a complex task, as seafood products are sensitive, biological diversity is great and they are susceptible to environmental influences and handling. Conventional methods such as sensory evaluation, visual assessment and chemical analyses are well established and useful, but they are often time-consuming, destructive to samples or based on subjective assessment.
Increasing demands for traceability, transparency, product quality and more efficient processing call for new solutions that are objective, rapid and non-destructive to samples.
This project explored the potential of imaging technology, particularly multispectral imaging, in combination with chemometrics, machine learning and deep neural networks, to automate quality and monitoring tasks within the fishing industry. The project consists of four scientific papers addressing different application possibilities of these technological solutions: age determination of fish otoliths, nematode detection in whitefish, freshness assessment of whole cod, and quality assessment of brown algae.
The results show that the integration of imaging and data-driven models offers potential for automation and for improving quality and monitoring processes in the fishing industry. It is clear that the technology offers great potential for further application throughout the seafood value chain, thereby opening opportunities for further research and development.