Analysis of defects in coffee beans compared to biplots for simultaneous tables
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista ciência agronômica (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902018000100062 |
Resumo: | ABSTRACT The demand for high quality coffee has become a consolidated criterion to achieve the best prices. Currently, cooperatives evaluate the coffee beans mainly through the particle size and the number of defects in the sample. This evaluation type generates counting data that originates contingency tables from different periods or groups involving the same variables in the row and column and there may be interest in knowing if two tables are related and how much are related. These are the so-called combined tables. Statistical analysis techniques normally employed do not include categorical data in the combined tables. The aim of this study was to evaluate the incidence of different types of defects in samples of large flat coffee beans in two different harvests through the construction of biplots. The decomposition theory in single simultaneous values of double entry contingency tables was used. The results of defect counting in beans of 24 coffee samples from southern Minas Gerais, Brazil, were evaluated in the 2014 and 2015 harvests. Moreover, the association among defect types, considered within different total defect proportions in the sample, was verified based on the percentage in 17/18 sieves. It was also evaluated the relative sums of squares from the similarity and dissimilarity among the harvests. It is concluded that the simultaneous analysis technique allows better visualizing the common behavior and alterations among different harvests, distinguishing the defect types associated with each harvest and among different proportions of large flat beans. |
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Analysis of defects in coffee beans compared to biplots for simultaneous tablesCoffea arabica L.Coffee qualityParticle sizeSVDSievesABSTRACT The demand for high quality coffee has become a consolidated criterion to achieve the best prices. Currently, cooperatives evaluate the coffee beans mainly through the particle size and the number of defects in the sample. This evaluation type generates counting data that originates contingency tables from different periods or groups involving the same variables in the row and column and there may be interest in knowing if two tables are related and how much are related. These are the so-called combined tables. Statistical analysis techniques normally employed do not include categorical data in the combined tables. The aim of this study was to evaluate the incidence of different types of defects in samples of large flat coffee beans in two different harvests through the construction of biplots. The decomposition theory in single simultaneous values of double entry contingency tables was used. The results of defect counting in beans of 24 coffee samples from southern Minas Gerais, Brazil, were evaluated in the 2014 and 2015 harvests. Moreover, the association among defect types, considered within different total defect proportions in the sample, was verified based on the percentage in 17/18 sieves. It was also evaluated the relative sums of squares from the similarity and dissimilarity among the harvests. It is concluded that the simultaneous analysis technique allows better visualizing the common behavior and alterations among different harvests, distinguishing the defect types associated with each harvest and among different proportions of large flat beans.Universidade Federal do Ceará2018-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902018000100062Revista Ciência Agronômica v.49 n.1 2018reponame:Revista ciência agronômica (Online)instname:Universidade Federal do Ceará (UFC)instacron:UFC10.5935/1806-6690.20180007info:eu-repo/semantics/openAccessBrighenti,Carla Regina GuimarãesCirillo,Marcelo Angeloeng2018-01-30T00:00:00Zoai:scielo:S1806-66902018000100062Revistahttp://www.ccarevista.ufc.br/PUBhttps://old.scielo.br/oai/scielo-oai.php||alekdutra@ufc.br|| ccarev@ufc.br1806-66900045-6888opendoar:2018-01-30T00:00Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC)false |
dc.title.none.fl_str_mv |
Analysis of defects in coffee beans compared to biplots for simultaneous tables |
title |
Analysis of defects in coffee beans compared to biplots for simultaneous tables |
spellingShingle |
Analysis of defects in coffee beans compared to biplots for simultaneous tables Brighenti,Carla Regina Guimarães Coffea arabica L. Coffee quality Particle size SVD Sieves |
title_short |
Analysis of defects in coffee beans compared to biplots for simultaneous tables |
title_full |
Analysis of defects in coffee beans compared to biplots for simultaneous tables |
title_fullStr |
Analysis of defects in coffee beans compared to biplots for simultaneous tables |
title_full_unstemmed |
Analysis of defects in coffee beans compared to biplots for simultaneous tables |
title_sort |
Analysis of defects in coffee beans compared to biplots for simultaneous tables |
author |
Brighenti,Carla Regina Guimarães |
author_facet |
Brighenti,Carla Regina Guimarães Cirillo,Marcelo Angelo |
author_role |
author |
author2 |
Cirillo,Marcelo Angelo |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Brighenti,Carla Regina Guimarães Cirillo,Marcelo Angelo |
dc.subject.por.fl_str_mv |
Coffea arabica L. Coffee quality Particle size SVD Sieves |
topic |
Coffea arabica L. Coffee quality Particle size SVD Sieves |
description |
ABSTRACT The demand for high quality coffee has become a consolidated criterion to achieve the best prices. Currently, cooperatives evaluate the coffee beans mainly through the particle size and the number of defects in the sample. This evaluation type generates counting data that originates contingency tables from different periods or groups involving the same variables in the row and column and there may be interest in knowing if two tables are related and how much are related. These are the so-called combined tables. Statistical analysis techniques normally employed do not include categorical data in the combined tables. The aim of this study was to evaluate the incidence of different types of defects in samples of large flat coffee beans in two different harvests through the construction of biplots. The decomposition theory in single simultaneous values of double entry contingency tables was used. The results of defect counting in beans of 24 coffee samples from southern Minas Gerais, Brazil, were evaluated in the 2014 and 2015 harvests. Moreover, the association among defect types, considered within different total defect proportions in the sample, was verified based on the percentage in 17/18 sieves. It was also evaluated the relative sums of squares from the similarity and dissimilarity among the harvests. It is concluded that the simultaneous analysis technique allows better visualizing the common behavior and alterations among different harvests, distinguishing the defect types associated with each harvest and among different proportions of large flat beans. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-03-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902018000100062 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902018000100062 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.5935/1806-6690.20180007 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Universidade Federal do Ceará |
publisher.none.fl_str_mv |
Universidade Federal do Ceará |
dc.source.none.fl_str_mv |
Revista Ciência Agronômica v.49 n.1 2018 reponame:Revista ciência agronômica (Online) instname:Universidade Federal do Ceará (UFC) instacron:UFC |
instname_str |
Universidade Federal do Ceará (UFC) |
instacron_str |
UFC |
institution |
UFC |
reponame_str |
Revista ciência agronômica (Online) |
collection |
Revista ciência agronômica (Online) |
repository.name.fl_str_mv |
Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC) |
repository.mail.fl_str_mv |
||alekdutra@ufc.br|| ccarev@ufc.br |
_version_ |
1750297488976248832 |