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Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue—A Salmon Case Study

The aim of the present study was to critically evaluate the potential of using NIR and Raman spectroscopy for prediction of fatty acid features and single fatty acids in salmon muscle. The study was based on 618 homogenized salmon muscle samples acquired from Atlantic salmon representing a one year-...

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Autores principales: Afseth, Nils Kristian, Dankel, Katinka, Andersen, Petter Vejle, Difford, Gareth Frank, Horn, Siri Storteig, Sonesson, Anna, Hillestad, Borghild, Wold, Jens Petter, Tengstrand, Erik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8997921/
https://www.ncbi.nlm.nih.gov/pubmed/35407049
http://dx.doi.org/10.3390/foods11070962
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author Afseth, Nils Kristian
Dankel, Katinka
Andersen, Petter Vejle
Difford, Gareth Frank
Horn, Siri Storteig
Sonesson, Anna
Hillestad, Borghild
Wold, Jens Petter
Tengstrand, Erik
author_facet Afseth, Nils Kristian
Dankel, Katinka
Andersen, Petter Vejle
Difford, Gareth Frank
Horn, Siri Storteig
Sonesson, Anna
Hillestad, Borghild
Wold, Jens Petter
Tengstrand, Erik
author_sort Afseth, Nils Kristian
collection PubMed
description The aim of the present study was to critically evaluate the potential of using NIR and Raman spectroscopy for prediction of fatty acid features and single fatty acids in salmon muscle. The study was based on 618 homogenized salmon muscle samples acquired from Atlantic salmon representing a one year-class nucleus, fed the same high fish oil feed. NIR and Raman spectra were used to make regression models for fatty acid features and single fatty acids measured by gas chromatography. The predictive performance of both NIR and Raman was good for most fatty acids, with R(2) above 0.6. Overall, Raman performed marginally better than NIR, and since the Raman models generally required fewer components than respective NIR models to reach high and optimal performance, Raman is likely more robust for measuring fatty acids compared to NIR. The fatty acids of the salmon samples co-varied to a large extent, a feature that was exacerbated by the overlapping peaks in NIR and Raman spectra. Thus, the fatty acid related variation of the spectroscopic data of the present study can be explained by only a few independent principal components. For the Raman spectra, this variation was dominated by functional groups originating from long-chain polyunsaturated FAs like EPA and DHA. By exploring the independent EPA and DHA Raman models, spectral signatures similar to the respective pure fatty acids could be seen. This proves the potential of Raman spectroscopy for single fatty acid prediction in muscle tissue.
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spelling pubmed-89979212022-04-12 Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue—A Salmon Case Study Afseth, Nils Kristian Dankel, Katinka Andersen, Petter Vejle Difford, Gareth Frank Horn, Siri Storteig Sonesson, Anna Hillestad, Borghild Wold, Jens Petter Tengstrand, Erik Foods Article The aim of the present study was to critically evaluate the potential of using NIR and Raman spectroscopy for prediction of fatty acid features and single fatty acids in salmon muscle. The study was based on 618 homogenized salmon muscle samples acquired from Atlantic salmon representing a one year-class nucleus, fed the same high fish oil feed. NIR and Raman spectra were used to make regression models for fatty acid features and single fatty acids measured by gas chromatography. The predictive performance of both NIR and Raman was good for most fatty acids, with R(2) above 0.6. Overall, Raman performed marginally better than NIR, and since the Raman models generally required fewer components than respective NIR models to reach high and optimal performance, Raman is likely more robust for measuring fatty acids compared to NIR. The fatty acids of the salmon samples co-varied to a large extent, a feature that was exacerbated by the overlapping peaks in NIR and Raman spectra. Thus, the fatty acid related variation of the spectroscopic data of the present study can be explained by only a few independent principal components. For the Raman spectra, this variation was dominated by functional groups originating from long-chain polyunsaturated FAs like EPA and DHA. By exploring the independent EPA and DHA Raman models, spectral signatures similar to the respective pure fatty acids could be seen. This proves the potential of Raman spectroscopy for single fatty acid prediction in muscle tissue. MDPI 2022-03-26 /pmc/articles/PMC8997921/ /pubmed/35407049 http://dx.doi.org/10.3390/foods11070962 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Afseth, Nils Kristian
Dankel, Katinka
Andersen, Petter Vejle
Difford, Gareth Frank
Horn, Siri Storteig
Sonesson, Anna
Hillestad, Borghild
Wold, Jens Petter
Tengstrand, Erik
Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue—A Salmon Case Study
title Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue—A Salmon Case Study
title_full Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue—A Salmon Case Study
title_fullStr Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue—A Salmon Case Study
title_full_unstemmed Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue—A Salmon Case Study
title_short Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue—A Salmon Case Study
title_sort raman and near infrared spectroscopy for quantification of fatty acids in muscle tissue—a salmon case study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8997921/
https://www.ncbi.nlm.nih.gov/pubmed/35407049
http://dx.doi.org/10.3390/foods11070962
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