Potential use of near-infrared spectroscopy to predict fatty acid profile of meat from different european autochthonous pig breeds
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/20.500.11960/3148 |
Resumo: | Autochthonous pig breeds provide products of differentiated quality, among which quality control is difficult to perform and insufficient for current market requirements. The present research evaluates the predictive ability of near‐infrared (NIR) spectroscopy, combined with chemometric methods as a rapid and affordable tool to assure traceability and quality control. Thus, NIR technology was assessed for intact and minced muscle Longissimus thoracis et lumborum samples collected from 12 European autochthonous pig breeds for the quantification of lipid content and fatty acid composition. Different tests were performed using different numbers of samples for calibration and validation. The best predictive ability was found using minced presentation and set with 80% of the samples for the calibration and the remaining 20% for the external validation test for the following traits: lipid content and saturated and polyunsaturated fatty acids, which attained both the highest determination coefficients (0.89, 0.61, and 0.65, respectively) and the lowest root mean square errors in external validation (0.62, 1.82, and 1.36, respectively). Lower predictive ability was observed for intact muscles. These results could contribute to improve the management of autochthonous breeds and to ensure quality of their products by traditional meat industry chains. |
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Potential use of near-infrared spectroscopy to predict fatty acid profile of meat from different european autochthonous pig breedsAutochthonous pig breedsUntapped pig breedsSustainable animal productionNearinfrared (NIR) technologyIntactMincedFresh loinAutochthonous pig breeds provide products of differentiated quality, among which quality control is difficult to perform and insufficient for current market requirements. The present research evaluates the predictive ability of near‐infrared (NIR) spectroscopy, combined with chemometric methods as a rapid and affordable tool to assure traceability and quality control. Thus, NIR technology was assessed for intact and minced muscle Longissimus thoracis et lumborum samples collected from 12 European autochthonous pig breeds for the quantification of lipid content and fatty acid composition. Different tests were performed using different numbers of samples for calibration and validation. The best predictive ability was found using minced presentation and set with 80% of the samples for the calibration and the remaining 20% for the external validation test for the following traits: lipid content and saturated and polyunsaturated fatty acids, which attained both the highest determination coefficients (0.89, 0.61, and 0.65, respectively) and the lowest root mean square errors in external validation (0.62, 1.82, and 1.36, respectively). Lower predictive ability was observed for intact muscles. These results could contribute to improve the management of autochthonous breeds and to ensure quality of their products by traditional meat industry chains.2023-01-25T15:13:10Z2020-08-21T00:00:00Z2020-08-212022-10-02T08:51:22Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/20.500.11960/3148eng2076-341710.3390/app10175801Ortiz, AlbertoParrini, SilviaTejerina, DavidAraújo, José PedroCandek-Potokar, MarjetaCrovetti, AlessandroGarcia-Casco, Juan MariaGonzález, JoelHernández‐García, Francisco IgnacioKarolyi, DanijelMargeta, VladimirMartins, José ManuelNieto, RosaPetig, MatthiasRazmaite, VioletaSirtori, FrancescoLebret, BénédicteBozzi, Riccardoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-04-11T08:09:37Zoai:repositorio.ipvc.pt:20.500.11960/3148Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-04-11T08:09:37Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Potential use of near-infrared spectroscopy to predict fatty acid profile of meat from different european autochthonous pig breeds |
title |
Potential use of near-infrared spectroscopy to predict fatty acid profile of meat from different european autochthonous pig breeds |
spellingShingle |
Potential use of near-infrared spectroscopy to predict fatty acid profile of meat from different european autochthonous pig breeds Ortiz, Alberto Autochthonous pig breeds Untapped pig breeds Sustainable animal production Nearinfrared (NIR) technology Intact Minced Fresh loin |
title_short |
Potential use of near-infrared spectroscopy to predict fatty acid profile of meat from different european autochthonous pig breeds |
title_full |
Potential use of near-infrared spectroscopy to predict fatty acid profile of meat from different european autochthonous pig breeds |
title_fullStr |
Potential use of near-infrared spectroscopy to predict fatty acid profile of meat from different european autochthonous pig breeds |
title_full_unstemmed |
Potential use of near-infrared spectroscopy to predict fatty acid profile of meat from different european autochthonous pig breeds |
title_sort |
Potential use of near-infrared spectroscopy to predict fatty acid profile of meat from different european autochthonous pig breeds |
author |
Ortiz, Alberto |
author_facet |
Ortiz, Alberto Parrini, Silvia Tejerina, David Araújo, José Pedro Candek-Potokar, Marjeta Crovetti, Alessandro Garcia-Casco, Juan Maria González, Joel Hernández‐García, Francisco Ignacio Karolyi, Danijel Margeta, Vladimir Martins, José Manuel Nieto, Rosa Petig, Matthias Razmaite, Violeta Sirtori, Francesco Lebret, Bénédicte Bozzi, Riccardo |
author_role |
author |
author2 |
Parrini, Silvia Tejerina, David Araújo, José Pedro Candek-Potokar, Marjeta Crovetti, Alessandro Garcia-Casco, Juan Maria González, Joel Hernández‐García, Francisco Ignacio Karolyi, Danijel Margeta, Vladimir Martins, José Manuel Nieto, Rosa Petig, Matthias Razmaite, Violeta Sirtori, Francesco Lebret, Bénédicte Bozzi, Riccardo |
author2_role |
author author author author author author author author author author author author author author author author author |
dc.contributor.author.fl_str_mv |
Ortiz, Alberto Parrini, Silvia Tejerina, David Araújo, José Pedro Candek-Potokar, Marjeta Crovetti, Alessandro Garcia-Casco, Juan Maria González, Joel Hernández‐García, Francisco Ignacio Karolyi, Danijel Margeta, Vladimir Martins, José Manuel Nieto, Rosa Petig, Matthias Razmaite, Violeta Sirtori, Francesco Lebret, Bénédicte Bozzi, Riccardo |
dc.subject.por.fl_str_mv |
Autochthonous pig breeds Untapped pig breeds Sustainable animal production Nearinfrared (NIR) technology Intact Minced Fresh loin |
topic |
Autochthonous pig breeds Untapped pig breeds Sustainable animal production Nearinfrared (NIR) technology Intact Minced Fresh loin |
description |
Autochthonous pig breeds provide products of differentiated quality, among which quality control is difficult to perform and insufficient for current market requirements. The present research evaluates the predictive ability of near‐infrared (NIR) spectroscopy, combined with chemometric methods as a rapid and affordable tool to assure traceability and quality control. Thus, NIR technology was assessed for intact and minced muscle Longissimus thoracis et lumborum samples collected from 12 European autochthonous pig breeds for the quantification of lipid content and fatty acid composition. Different tests were performed using different numbers of samples for calibration and validation. The best predictive ability was found using minced presentation and set with 80% of the samples for the calibration and the remaining 20% for the external validation test for the following traits: lipid content and saturated and polyunsaturated fatty acids, which attained both the highest determination coefficients (0.89, 0.61, and 0.65, respectively) and the lowest root mean square errors in external validation (0.62, 1.82, and 1.36, respectively). Lower predictive ability was observed for intact muscles. These results could contribute to improve the management of autochthonous breeds and to ensure quality of their products by traditional meat industry chains. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-08-21T00:00:00Z 2020-08-21 2022-10-02T08:51:22Z 2023-01-25T15:13:10Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/20.500.11960/3148 |
url |
http://hdl.handle.net/20.500.11960/3148 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2076-3417 10.3390/app10175801 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
mluisa.alvim@gmail.com |
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1817543258828439552 |