Classification modeling based on surface porosity for the grading of natural cork stoppers for quality wines
Autor(a) principal: | |
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Data de Publicação: | 2015 |
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/10400.5/14033 |
Resumo: | tThe natural cork stoppers are commercially graded into quality classes according with the homogeneity of theexternal surface. The underlying criteria for this classification are subjective without quantified criteria and standardsdefined by cork industry or consumers. Image analysis was applied to premium, good and standard quality classes tocharacterize the surface of the cork stoppers and stepwise discriminant analysis (SDA) was used to build predictiveclassification models. The final goal is to analyze the contribution of each porosity feature and propose an algorithmfor cork stoppers quality class classification. This study provides the knowledge based on a large sampling to anaccurate grading of natural cork stoppers.In average all the models presented accuracy in relation to the commercial classification over 68% with a highermismatch in the mid-quality range. Color showed an important discriminating power, increasing the accuracy in10%. The main discriminant features were porosity coefficient and color variables, calculated for the lateral surface. Aquality classification algorithm was presented based on a simplified model with an accuracy of 75%. The classificationbased on color vision systems can ensure improved quality class uniformity and a higher transparency in trade |
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Classification modeling based on surface porosity for the grading of natural cork stoppers for quality winesnatural cork stoppersquality classesimage analysisporositydiscriminant analysisclassification algorithmtThe natural cork stoppers are commercially graded into quality classes according with the homogeneity of theexternal surface. The underlying criteria for this classification are subjective without quantified criteria and standardsdefined by cork industry or consumers. Image analysis was applied to premium, good and standard quality classes tocharacterize the surface of the cork stoppers and stepwise discriminant analysis (SDA) was used to build predictiveclassification models. The final goal is to analyze the contribution of each porosity feature and propose an algorithmfor cork stoppers quality class classification. This study provides the knowledge based on a large sampling to anaccurate grading of natural cork stoppers.In average all the models presented accuracy in relation to the commercial classification over 68% with a highermismatch in the mid-quality range. Color showed an important discriminating power, increasing the accuracy in10%. The main discriminant features were porosity coefficient and color variables, calculated for the lateral surface. Aquality classification algorithm was presented based on a simplified model with an accuracy of 75%. The classificationbased on color vision systems can ensure improved quality class uniformity and a higher transparency in tradeElsevierRepositório da Universidade de LisboaOliveira, VandaKnapic, SofiaPereira, Helena2017-09-11T10:06:02Z20152015-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/14033engfood and bioproducts processing 93 (2015) 69–76http://dx.doi.org/10.1016/j.fbp.2013.11.004info: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:RCAAP2023-03-06T14:44:06Zoai:www.repository.utl.pt:10400.5/14033Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:59:55.632224Repositório 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 |
Classification modeling based on surface porosity for the grading of natural cork stoppers for quality wines |
title |
Classification modeling based on surface porosity for the grading of natural cork stoppers for quality wines |
spellingShingle |
Classification modeling based on surface porosity for the grading of natural cork stoppers for quality wines Oliveira, Vanda natural cork stoppers quality classes image analysis porosity discriminant analysis classification algorithm |
title_short |
Classification modeling based on surface porosity for the grading of natural cork stoppers for quality wines |
title_full |
Classification modeling based on surface porosity for the grading of natural cork stoppers for quality wines |
title_fullStr |
Classification modeling based on surface porosity for the grading of natural cork stoppers for quality wines |
title_full_unstemmed |
Classification modeling based on surface porosity for the grading of natural cork stoppers for quality wines |
title_sort |
Classification modeling based on surface porosity for the grading of natural cork stoppers for quality wines |
author |
Oliveira, Vanda |
author_facet |
Oliveira, Vanda Knapic, Sofia Pereira, Helena |
author_role |
author |
author2 |
Knapic, Sofia Pereira, Helena |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Oliveira, Vanda Knapic, Sofia Pereira, Helena |
dc.subject.por.fl_str_mv |
natural cork stoppers quality classes image analysis porosity discriminant analysis classification algorithm |
topic |
natural cork stoppers quality classes image analysis porosity discriminant analysis classification algorithm |
description |
tThe natural cork stoppers are commercially graded into quality classes according with the homogeneity of theexternal surface. The underlying criteria for this classification are subjective without quantified criteria and standardsdefined by cork industry or consumers. Image analysis was applied to premium, good and standard quality classes tocharacterize the surface of the cork stoppers and stepwise discriminant analysis (SDA) was used to build predictiveclassification models. The final goal is to analyze the contribution of each porosity feature and propose an algorithmfor cork stoppers quality class classification. This study provides the knowledge based on a large sampling to anaccurate grading of natural cork stoppers.In average all the models presented accuracy in relation to the commercial classification over 68% with a highermismatch in the mid-quality range. Color showed an important discriminating power, increasing the accuracy in10%. The main discriminant features were porosity coefficient and color variables, calculated for the lateral surface. Aquality classification algorithm was presented based on a simplified model with an accuracy of 75%. The classificationbased on color vision systems can ensure improved quality class uniformity and a higher transparency in trade |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015 2015-01-01T00:00:00Z 2017-09-11T10:06:02Z |
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/10400.5/14033 |
url |
http://hdl.handle.net/10400.5/14033 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
food and bioproducts processing 93 (2015) 69–76 http://dx.doi.org/10.1016/j.fbp.2013.11.004 |
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 |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
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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1817553289800056832 |