Early identification of mushy Halibut syndrome with hyperspectral image analysis
Mushy Halibut Syndrome (MHS) is a condition that appears in Greenland halibut and manifests itself as abnormally opaque, flaccid and jelly-like flesh. Fish affected by this syndrome show poor meat quality, which results in negative consequences for the fish industry. The research community has not c...
Published in: | LWT |
---|---|
Main Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/11250/3050552 https://doi.org/10.1016/j.lwt.2023.114559 |
id |
ftnofima:oai:nofima.brage.unit.no:11250/3050552 |
---|---|
record_format |
openpolar |
spelling |
ftnofima:oai:nofima.brage.unit.no:11250/3050552 2023-05-15T16:29:29+02:00 Early identification of mushy Halibut syndrome with hyperspectral image analysis Ortega, Samuel Lindberg, Stein-Kato Olsen, Stein Harris Anderssen, Kathryn Elizabeth Heia, Karsten 2023 application/pdf https://hdl.handle.net/11250/3050552 https://doi.org/10.1016/j.lwt.2023.114559 eng eng Norges forskningsråd: 309259 Norges forskningsråd: 294805 Lebensmittel-Wissenschaft + Technologie. 2023, 176 . urn:issn:0023-6438 https://hdl.handle.net/11250/3050552 https://doi.org/10.1016/j.lwt.2023.114559 cristin:2124735 11 176 Lebensmittel-Wissenschaft + Technologie Peer reviewed Journal article 2023 ftnofima https://doi.org/10.1016/j.lwt.2023.114559 2023-02-15T23:47:26Z Mushy Halibut Syndrome (MHS) is a condition that appears in Greenland halibut and manifests itself as abnormally opaque, flaccid and jelly-like flesh. Fish affected by this syndrome show poor meat quality, which results in negative consequences for the fish industry. The research community has not carefully investigated this condition, nor novel technologies for MHS detection have been proposed. In this research work, we propose using hyperspectral imaging to detect MHS. After collecting a dataset of hyperspectral images of halibut affected by MHS, two different goals were targeted. Firstly, the estimation of the chemical composition of the samples (specifically fat and water content) from their spectral data by using constrained spectral unmixing. Secondly, supervised classification using partial least squares discriminant analysis (PLS-DA) was evaluated to identify specimens affected by MHS. The outcomes of our study suggest that the prediction of fat from the spectral data is possible, but the prediction of the water content was not found to be accurate. However, the detection of MHS using PLS-DA was precise for hyperspectral images from both fillets and whole fish, with lower bounds of 75% and 83% for precision and recall, respectively. Our findings suggest hyperspectral imaging as a suitable technology for the early screening of MHS. Early identification of mushy Halibut syndrome with hyperspectral image analysis publishedVersion Article in Journal/Newspaper Greenland Nofima Knowledge Archive (Brage) Greenland LWT 176 114559 |
institution |
Open Polar |
collection |
Nofima Knowledge Archive (Brage) |
op_collection_id |
ftnofima |
language |
English |
description |
Mushy Halibut Syndrome (MHS) is a condition that appears in Greenland halibut and manifests itself as abnormally opaque, flaccid and jelly-like flesh. Fish affected by this syndrome show poor meat quality, which results in negative consequences for the fish industry. The research community has not carefully investigated this condition, nor novel technologies for MHS detection have been proposed. In this research work, we propose using hyperspectral imaging to detect MHS. After collecting a dataset of hyperspectral images of halibut affected by MHS, two different goals were targeted. Firstly, the estimation of the chemical composition of the samples (specifically fat and water content) from their spectral data by using constrained spectral unmixing. Secondly, supervised classification using partial least squares discriminant analysis (PLS-DA) was evaluated to identify specimens affected by MHS. The outcomes of our study suggest that the prediction of fat from the spectral data is possible, but the prediction of the water content was not found to be accurate. However, the detection of MHS using PLS-DA was precise for hyperspectral images from both fillets and whole fish, with lower bounds of 75% and 83% for precision and recall, respectively. Our findings suggest hyperspectral imaging as a suitable technology for the early screening of MHS. Early identification of mushy Halibut syndrome with hyperspectral image analysis publishedVersion |
format |
Article in Journal/Newspaper |
author |
Ortega, Samuel Lindberg, Stein-Kato Olsen, Stein Harris Anderssen, Kathryn Elizabeth Heia, Karsten |
spellingShingle |
Ortega, Samuel Lindberg, Stein-Kato Olsen, Stein Harris Anderssen, Kathryn Elizabeth Heia, Karsten Early identification of mushy Halibut syndrome with hyperspectral image analysis |
author_facet |
Ortega, Samuel Lindberg, Stein-Kato Olsen, Stein Harris Anderssen, Kathryn Elizabeth Heia, Karsten |
author_sort |
Ortega, Samuel |
title |
Early identification of mushy Halibut syndrome with hyperspectral image analysis |
title_short |
Early identification of mushy Halibut syndrome with hyperspectral image analysis |
title_full |
Early identification of mushy Halibut syndrome with hyperspectral image analysis |
title_fullStr |
Early identification of mushy Halibut syndrome with hyperspectral image analysis |
title_full_unstemmed |
Early identification of mushy Halibut syndrome with hyperspectral image analysis |
title_sort |
early identification of mushy halibut syndrome with hyperspectral image analysis |
publishDate |
2023 |
url |
https://hdl.handle.net/11250/3050552 https://doi.org/10.1016/j.lwt.2023.114559 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland |
genre_facet |
Greenland |
op_source |
11 176 Lebensmittel-Wissenschaft + Technologie |
op_relation |
Norges forskningsråd: 309259 Norges forskningsråd: 294805 Lebensmittel-Wissenschaft + Technologie. 2023, 176 . urn:issn:0023-6438 https://hdl.handle.net/11250/3050552 https://doi.org/10.1016/j.lwt.2023.114559 cristin:2124735 |
op_doi |
https://doi.org/10.1016/j.lwt.2023.114559 |
container_title |
LWT |
container_volume |
176 |
container_start_page |
114559 |
_version_ |
1766019191555489792 |