Computer vision system approach in colour measurements of foods: Part II. validation of methodology with real foods
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Food Science and Technology (Campinas) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612016000300499 |
Resumo: | Abstract The colour of food is one of the most important factors affecting consumers’ purchasing decision. Although there are many colour spaces, the most widely used colour space in the food industry is L*a*b* colour space. Conventionally, the colour of foods is analysed with a colorimeter that measures small and non-representative areas of the food and the measurements usually vary depending on the point where the measurement is taken. This leads to the development of alternative colour analysis techniques. In this work, a simple and alternative method to measure the colour of foods known as “computer vision system” is presented and justified. With the aid of the computer vision system, foods that are homogenous and uniform in colour and shape could be classified with regard to their colours in a fast, inexpensive and simple way. This system could also be used to distinguish the defectives from the non-defectives. Quality parameters of meat and dairy products could be monitored without any physical contact, which causes contamination during sampling. |
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Computer vision system approach in colour measurements of foods: Part II. validation of methodology with real foodscomputer vision systemfoodRGBL*a*b*Abstract The colour of food is one of the most important factors affecting consumers’ purchasing decision. Although there are many colour spaces, the most widely used colour space in the food industry is L*a*b* colour space. Conventionally, the colour of foods is analysed with a colorimeter that measures small and non-representative areas of the food and the measurements usually vary depending on the point where the measurement is taken. This leads to the development of alternative colour analysis techniques. In this work, a simple and alternative method to measure the colour of foods known as “computer vision system” is presented and justified. With the aid of the computer vision system, foods that are homogenous and uniform in colour and shape could be classified with regard to their colours in a fast, inexpensive and simple way. This system could also be used to distinguish the defectives from the non-defectives. Quality parameters of meat and dairy products could be monitored without any physical contact, which causes contamination during sampling.Sociedade Brasileira de Ciência e Tecnologia de Alimentos2016-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612016000300499Food Science and Technology v.36 n.3 2016reponame:Food Science and Technology (Campinas)instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)instacron:SBCTA10.1590/1678-457X.02616info:eu-repo/semantics/openAccessTARLAK,FatihOZDEMİR,MuratMELİKOGLU,Mehmeteng2016-09-29T00:00:00Zoai:scielo:S0101-20612016000300499Revistahttp://www.scielo.br/ctaONGhttps://old.scielo.br/oai/scielo-oai.php||revista@sbcta.org.br1678-457X0101-2061opendoar:2016-09-29T00:00Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)false |
dc.title.none.fl_str_mv |
Computer vision system approach in colour measurements of foods: Part II. validation of methodology with real foods |
title |
Computer vision system approach in colour measurements of foods: Part II. validation of methodology with real foods |
spellingShingle |
Computer vision system approach in colour measurements of foods: Part II. validation of methodology with real foods TARLAK,Fatih computer vision system food RGB L*a*b* |
title_short |
Computer vision system approach in colour measurements of foods: Part II. validation of methodology with real foods |
title_full |
Computer vision system approach in colour measurements of foods: Part II. validation of methodology with real foods |
title_fullStr |
Computer vision system approach in colour measurements of foods: Part II. validation of methodology with real foods |
title_full_unstemmed |
Computer vision system approach in colour measurements of foods: Part II. validation of methodology with real foods |
title_sort |
Computer vision system approach in colour measurements of foods: Part II. validation of methodology with real foods |
author |
TARLAK,Fatih |
author_facet |
TARLAK,Fatih OZDEMİR,Murat MELİKOGLU,Mehmet |
author_role |
author |
author2 |
OZDEMİR,Murat MELİKOGLU,Mehmet |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
TARLAK,Fatih OZDEMİR,Murat MELİKOGLU,Mehmet |
dc.subject.por.fl_str_mv |
computer vision system food RGB L*a*b* |
topic |
computer vision system food RGB L*a*b* |
description |
Abstract The colour of food is one of the most important factors affecting consumers’ purchasing decision. Although there are many colour spaces, the most widely used colour space in the food industry is L*a*b* colour space. Conventionally, the colour of foods is analysed with a colorimeter that measures small and non-representative areas of the food and the measurements usually vary depending on the point where the measurement is taken. This leads to the development of alternative colour analysis techniques. In this work, a simple and alternative method to measure the colour of foods known as “computer vision system” is presented and justified. With the aid of the computer vision system, foods that are homogenous and uniform in colour and shape could be classified with regard to their colours in a fast, inexpensive and simple way. This system could also be used to distinguish the defectives from the non-defectives. Quality parameters of meat and dairy products could be monitored without any physical contact, which causes contamination during sampling. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-09-01 |
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=S0101-20612016000300499 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612016000300499 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-457X.02616 |
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 |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos |
publisher.none.fl_str_mv |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos |
dc.source.none.fl_str_mv |
Food Science and Technology v.36 n.3 2016 reponame:Food Science and Technology (Campinas) instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) instacron:SBCTA |
instname_str |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) |
instacron_str |
SBCTA |
institution |
SBCTA |
reponame_str |
Food Science and Technology (Campinas) |
collection |
Food Science and Technology (Campinas) |
repository.name.fl_str_mv |
Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) |
repository.mail.fl_str_mv |
||revista@sbcta.org.br |
_version_ |
1752126320769236992 |