{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T10:53:31Z","timestamp":1764240811008,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T00:00:00Z","timestamp":1726617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ERA-NET BlueBio Cofund","award":["2014156-0611","ANR20090415"],"award-info":[{"award-number":["2014156-0611","ANR20090415"]}]},{"name":"Icelandic Food Research fund","award":["2014156-0611","ANR20090415"],"award-info":[{"award-number":["2014156-0611","ANR20090415"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Foods"],"abstract":"<jats:p>Nematodes pose significant challenges for the fish processing industry, particularly in white fish. Despite technological advances, the industry still depends on manual labor for the detection and extraction of nematodes. This study addresses the initial steps of automatic nematode detection and differentiation from other common defects in fish fillets, such as skin remnants and blood spots. VideometerLab 4, an advanced Multispectral Imaging (MSI) System, was used to acquire 270 images of 50 Atlantic cod fillets under controlled conditions. In total, 173 nematodes were labeled using the Segment Anything Model (SAM), which is trained to automatically segment objects of interest from only few representative pixels. With the acquired dataset, we study the potential of identifying nematodes through their spectral signature. We incorporated normalized Canonical Discriminant Analysis (nCDA) to develop segmentation models trained to distinguish between different components within the fish fillets. By incorporating multiple segmentation models, we aimed to achieve a satisfactory balance between false negatives and false positives. This resulted in 88% precision and 79% recall for our annotated test data. This approach could improve process control by accurately identifying fillets with nematodes. Using MSI minimizes unnecessary inspection of fillets in good condition and concurrently boosts product safety and quality.<\/jats:p>","DOI":"10.3390\/foods13182952","type":"journal-article","created":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T09:43:13Z","timestamp":1726652593000},"page":"2952","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Sequence Segmentation of Nematodes in Atlantic Cod with Multispectral Imaging Data"],"prefix":"10.3390","volume":"13","author":[{"given":"Andrea Rakel","family":"Sigur\u00f0ard\u00f3ttir","sequence":"first","affiliation":[{"name":"Faculty of Food Science and Nutrition, University of Iceland, S\u00e6mundargata 12, 102 Reykjav\u00edk, Iceland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1815-123X","authenticated-orcid":false,"given":"Hildur Inga","family":"Sveinsd\u00f3ttir","sequence":"additional","affiliation":[{"name":"Faculty of Food Science and Nutrition, University of Iceland, S\u00e6mundargata 12, 102 Reykjav\u00edk, Iceland"},{"name":"Mat\u00eds, Food and Biotech R&D, V\u00ednlandslei\u00f0 12, 113 Reykjav\u00edk, Iceland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nette","family":"Schultz","sequence":"additional","affiliation":[{"name":"Videometer A\/S, 2730 Herlev, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5072-3678","authenticated-orcid":false,"given":"Hafsteinn","family":"Einarsson","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, University of Iceland, Bjargargata 1, 102 Reykjav\u00edk, Iceland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7577-1190","authenticated-orcid":false,"given":"Mar\u00eda","family":"Gudj\u00f3nsd\u00f3ttir","sequence":"additional","affiliation":[{"name":"Faculty of Food Science and Nutrition, University of Iceland, S\u00e6mundargata 12, 102 Reykjav\u00edk, Iceland"},{"name":"Mat\u00eds, Food and Biotech R&D, V\u00ednlandslei\u00f0 12, 113 Reykjav\u00edk, Iceland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1093\/af\/vfac020","article-title":"Automation in the fish industry","volume":"12","year":"2022","journal-title":"Anim. 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