SVM regression to assess meat characteristics of bisaro pig loins using nirs methodology
Autor(a) principal: | |
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Data de Publicação: | 2022 |
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/10198/25363 |
Resumo: | This study evaluates the ability of the near infrared reflectance spectroscopy (NIRS) to estimate the aW, protein, moisture, ash, fat, collagen, texture, pigments, and WHC in the Longissimus thoracis et lumborum (LTL) of Bisaro pig. Samples (n = 40) of the LTL muscle were minced and scanned in an FT-NIR MasterTM N500 (BuCHI) over a NIR spectral range of 4000-10,000 cm(-1) with a resolution of 4 cm(-1). The PLS and SVM regression models were developed using the spectra's math treatment, DV1, DV2, MSC, SNV, and SMT (n = 40). PLS models showed acceptable fits (estimation models with RMSE <= 0.5% and R-2 >= 0.95) except for the RT variable (RMSE of 0.891% and R-2 of 0.748). The SVM models presented better overall prediction results than those obtained by PLS, where only the variables pigments and WHC presented estimation models (respectively: RMSE of 0.069 and 0.472%; R-2 of 0.993 and 0.996; slope of 0.985 +/- 0.006 and 0.925 +/- 0.006). The results showed NIRs capacity to predict the meat quality traits of Bisaro pig breed in order to guarantee its characterization. |
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SVM regression to assess meat characteristics of bisaro pig loins using nirs methodologyNIRSVM modelMeat qualityBísaro pigLongissimus thoracis et lumborumThis study evaluates the ability of the near infrared reflectance spectroscopy (NIRS) to estimate the aW, protein, moisture, ash, fat, collagen, texture, pigments, and WHC in the Longissimus thoracis et lumborum (LTL) of Bisaro pig. Samples (n = 40) of the LTL muscle were minced and scanned in an FT-NIR MasterTM N500 (BuCHI) over a NIR spectral range of 4000-10,000 cm(-1) with a resolution of 4 cm(-1). The PLS and SVM regression models were developed using the spectra's math treatment, DV1, DV2, MSC, SNV, and SMT (n = 40). PLS models showed acceptable fits (estimation models with RMSE <= 0.5% and R-2 >= 0.95) except for the RT variable (RMSE of 0.891% and R-2 of 0.748). The SVM models presented better overall prediction results than those obtained by PLS, where only the variables pigments and WHC presented estimation models (respectively: RMSE of 0.069 and 0.472%; R-2 of 0.993 and 0.996; slope of 0.985 +/- 0.006 and 0.925 +/- 0.006). The results showed NIRs capacity to predict the meat quality traits of Bisaro pig breed in order to guarantee its characterization.MDPIBiblioteca Digital do IPBVasconcelos, LiaDias, L.G.Leite, AnaFerreira, Iasmin da SilvaPereira, EtelvinaSilva, SeverianoRodrigues, SandraTeixeira, Alfredo2022-04-05T15:43:30Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/25363engVasconcelos, Lia Inês Machado; Dias, L.G.; Leite, Ana; Ferreira, Iasmin da Silva; Pereira, Etelvina; Silva, Severiano; Rodrigues, Sandra; Teixeira, Alfredo (2023). SVM regression to assess meat characteristics of bisaro pig loins using nirs methodology. Foods. eISSN 2304-8158. 12:3, p. 1-1510.3390/foods120304702304-8158info: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:RCAAP2024-03-06T01:21:18Zoai:bibliotecadigital.ipb.pt:10198/25363Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:16:01.654419Repositório 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 |
SVM regression to assess meat characteristics of bisaro pig loins using nirs methodology |
title |
SVM regression to assess meat characteristics of bisaro pig loins using nirs methodology |
spellingShingle |
SVM regression to assess meat characteristics of bisaro pig loins using nirs methodology Vasconcelos, Lia NIR SVM model Meat quality Bísaro pig Longissimus thoracis et lumborum |
title_short |
SVM regression to assess meat characteristics of bisaro pig loins using nirs methodology |
title_full |
SVM regression to assess meat characteristics of bisaro pig loins using nirs methodology |
title_fullStr |
SVM regression to assess meat characteristics of bisaro pig loins using nirs methodology |
title_full_unstemmed |
SVM regression to assess meat characteristics of bisaro pig loins using nirs methodology |
title_sort |
SVM regression to assess meat characteristics of bisaro pig loins using nirs methodology |
author |
Vasconcelos, Lia |
author_facet |
Vasconcelos, Lia Dias, L.G. Leite, Ana Ferreira, Iasmin da Silva Pereira, Etelvina Silva, Severiano Rodrigues, Sandra Teixeira, Alfredo |
author_role |
author |
author2 |
Dias, L.G. Leite, Ana Ferreira, Iasmin da Silva Pereira, Etelvina Silva, Severiano Rodrigues, Sandra Teixeira, Alfredo |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Biblioteca Digital do IPB |
dc.contributor.author.fl_str_mv |
Vasconcelos, Lia Dias, L.G. Leite, Ana Ferreira, Iasmin da Silva Pereira, Etelvina Silva, Severiano Rodrigues, Sandra Teixeira, Alfredo |
dc.subject.por.fl_str_mv |
NIR SVM model Meat quality Bísaro pig Longissimus thoracis et lumborum |
topic |
NIR SVM model Meat quality Bísaro pig Longissimus thoracis et lumborum |
description |
This study evaluates the ability of the near infrared reflectance spectroscopy (NIRS) to estimate the aW, protein, moisture, ash, fat, collagen, texture, pigments, and WHC in the Longissimus thoracis et lumborum (LTL) of Bisaro pig. Samples (n = 40) of the LTL muscle were minced and scanned in an FT-NIR MasterTM N500 (BuCHI) over a NIR spectral range of 4000-10,000 cm(-1) with a resolution of 4 cm(-1). The PLS and SVM regression models were developed using the spectra's math treatment, DV1, DV2, MSC, SNV, and SMT (n = 40). PLS models showed acceptable fits (estimation models with RMSE <= 0.5% and R-2 >= 0.95) except for the RT variable (RMSE of 0.891% and R-2 of 0.748). The SVM models presented better overall prediction results than those obtained by PLS, where only the variables pigments and WHC presented estimation models (respectively: RMSE of 0.069 and 0.472%; R-2 of 0.993 and 0.996; slope of 0.985 +/- 0.006 and 0.925 +/- 0.006). The results showed NIRs capacity to predict the meat quality traits of Bisaro pig breed in order to guarantee its characterization. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-04-05T15:43:30Z 2023 2023-01-01T00:00:00Z |
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/10198/25363 |
url |
http://hdl.handle.net/10198/25363 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Vasconcelos, Lia Inês Machado; Dias, L.G.; Leite, Ana; Ferreira, Iasmin da Silva; Pereira, Etelvina; Silva, Severiano; Rodrigues, Sandra; Teixeira, Alfredo (2023). SVM regression to assess meat characteristics of bisaro pig loins using nirs methodology. Foods. eISSN 2304-8158. 12:3, p. 1-15 10.3390/foods12030470 2304-8158 |
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.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
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 |
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1799135444816887808 |