Brown sugar characterization based on sucrose, Cu, Ca, Na, Fe and Mg contents
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | The Journal of Engineering and Exact Sciences |
Texto Completo: | https://periodicos.ufv.br/jcec/article/view/12796 |
Resumo: | Samples of light brown sugar (DPC) and dark sugar (DPE) were analyzed, determining their characteristics, based on metal concentration and sucrose data, through of the principal component analysis (PCA). For this purpose, a matrix with 97 sugar samples and 6 variables (sucrose, Cu, Ca, Na, Fe and Mg) was obtained, self-scaling the data with zero mean and unit variance. The principal component analysis showed the common and discrepant characteristics between the different brown sugars. In the data arrangement in the PCA, it is possible to observe that the first two principal components explain practically 70% of the total variance of the data. Observing the separation between the light brown sugar group (DPC) and the dark brown sugar group (DPE), in the first principal component while in the second principal component the separation of the samples from the light brown sugar group (DPC) is evident of six dark brown sugar (DPE) samples, which showed similar characteristics to the light brown sugar group (DPC) samples in first principal component. These characteristics of the samples are evidenced probably due to the cooking process in which cleaning is carried out, to remove the foams formed during the heating of the broth, in order to ensure a lighter product. In this operation, as evidenced by the analyzes, calcium and iron are removed, two variables, in addition to sucrose, which are more significant in the classification of dark brown sugar (DPE). |
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Brown sugar characterization based on sucrose, Cu, Ca, Na, Fe and Mg contentsCaracterização de açúcar mascavo baseado nos teores de sacarose, Cu, Ca, Na, Fe e Mg Açúcar mascavo. Análise das componentes principais. Espectrofotometria por absorção atômica.Brown sugar. Principal component analysis. Atomic absorption spectrometry.Samples of light brown sugar (DPC) and dark sugar (DPE) were analyzed, determining their characteristics, based on metal concentration and sucrose data, through of the principal component analysis (PCA). For this purpose, a matrix with 97 sugar samples and 6 variables (sucrose, Cu, Ca, Na, Fe and Mg) was obtained, self-scaling the data with zero mean and unit variance. The principal component analysis showed the common and discrepant characteristics between the different brown sugars. In the data arrangement in the PCA, it is possible to observe that the first two principal components explain practically 70% of the total variance of the data. Observing the separation between the light brown sugar group (DPC) and the dark brown sugar group (DPE), in the first principal component while in the second principal component the separation of the samples from the light brown sugar group (DPC) is evident of six dark brown sugar (DPE) samples, which showed similar characteristics to the light brown sugar group (DPC) samples in first principal component. These characteristics of the samples are evidenced probably due to the cooking process in which cleaning is carried out, to remove the foams formed during the heating of the broth, in order to ensure a lighter product. In this operation, as evidenced by the analyzes, calcium and iron are removed, two variables, in addition to sucrose, which are more significant in the classification of dark brown sugar (DPE).Foram analisadas amostras de açúcar mascavo claro (DPC) e escuro (DPE), determinando-se as características destas, baseado nos dados de concentração de metais e de sacarose, através da análise das componentes principais (PCA). Para tanto, uma matriz com 97 amostras de açucares e 6 variáveis (Sacarose, Cu, Ca, Na, Fe e Mg) foi obtida, auto escalonando-se os dados com média zero e variância unitária. A análise das componentes principais evidenciou as características comuns e discrepantes entre os diferentes açúcares mascavos. Na disposição dos dados na PCA é possível observar que as duas primeiras componentes principais explicam praticamente 70 % da variância total dos dados. Observando-se a separação entre o grupo de açúcar mascavo claro (DPC) e o grupo de açúcar mascavo escuro (DPE), na primeira componente principal enquanto na segunda componente principal fica evidenciado a separação das amostras do grupo de açúcar mascavo claro (DPC) de seis amostras de açúcar mascavo escuro (DPE), que apresentaram características semelhantes às amostras do grupo de açúcar mascavo claro (DPC) na CP1. Estas características das amostras ficam evidenciadas provavelmente devido ao processo de cozimento no qual é realizado uma limpeza, para remover as espumas formadas durante o aquecimento do caldo, de modo a assegurar um produto mais claro. Nesta operação, como evidenciado pelas análises, são retirados cálcio e ferro, duas variáveis, além da sacarose, mais significativas na classificação do açúcar mascaro escuro (DPE).Universidade Federal de Viçosa - UFV2021-07-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.ufv.br/jcec/article/view/1279610.18540/jcecvl7iss3pp12796-01-09eThe Journal of Engineering and Exact Sciences; Vol. 7 No. 3 (2021); 12796-01-09eThe Journal of Engineering and Exact Sciences; Vol. 7 Núm. 3 (2021); 12796-01-09eThe Journal of Engineering and Exact Sciences; v. 7 n. 3 (2021); 12796-01-09e2527-1075reponame:The Journal of Engineering and Exact Sciencesinstname:Universidade Federal de Viçosa (UFV)instacron:UFVporhttps://periodicos.ufv.br/jcec/article/view/12796/6758Copyright (c) 2021 The Journal of Engineering and Exact Scienceshttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessNatalino, RicardoReis, Efraim LázaroReis, CesarFidêncio, Paulo HenriqueMayrink, Maria Isabel Cristina Batista 2021-08-16T18:50:11Zoai:ojs.periodicos.ufv.br:article/12796Revistahttp://www.seer.ufv.br/seer/rbeq2/index.php/req2/oai2527-10752527-1075opendoar:2021-08-16T18:50:11The Journal of Engineering and Exact Sciences - Universidade Federal de Viçosa (UFV)false |
dc.title.none.fl_str_mv |
Brown sugar characterization based on sucrose, Cu, Ca, Na, Fe and Mg contents Caracterização de açúcar mascavo baseado nos teores de sacarose, Cu, Ca, Na, Fe e Mg |
title |
Brown sugar characterization based on sucrose, Cu, Ca, Na, Fe and Mg contents |
spellingShingle |
Brown sugar characterization based on sucrose, Cu, Ca, Na, Fe and Mg contents Natalino, Ricardo Açúcar mascavo. Análise das componentes principais. Espectrofotometria por absorção atômica. Brown sugar. Principal component analysis. Atomic absorption spectrometry. |
title_short |
Brown sugar characterization based on sucrose, Cu, Ca, Na, Fe and Mg contents |
title_full |
Brown sugar characterization based on sucrose, Cu, Ca, Na, Fe and Mg contents |
title_fullStr |
Brown sugar characterization based on sucrose, Cu, Ca, Na, Fe and Mg contents |
title_full_unstemmed |
Brown sugar characterization based on sucrose, Cu, Ca, Na, Fe and Mg contents |
title_sort |
Brown sugar characterization based on sucrose, Cu, Ca, Na, Fe and Mg contents |
author |
Natalino, Ricardo |
author_facet |
Natalino, Ricardo Reis, Efraim Lázaro Reis, Cesar Fidêncio, Paulo Henrique Mayrink, Maria Isabel Cristina Batista |
author_role |
author |
author2 |
Reis, Efraim Lázaro Reis, Cesar Fidêncio, Paulo Henrique Mayrink, Maria Isabel Cristina Batista |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Natalino, Ricardo Reis, Efraim Lázaro Reis, Cesar Fidêncio, Paulo Henrique Mayrink, Maria Isabel Cristina Batista |
dc.subject.por.fl_str_mv |
Açúcar mascavo. Análise das componentes principais. Espectrofotometria por absorção atômica. Brown sugar. Principal component analysis. Atomic absorption spectrometry. |
topic |
Açúcar mascavo. Análise das componentes principais. Espectrofotometria por absorção atômica. Brown sugar. Principal component analysis. Atomic absorption spectrometry. |
description |
Samples of light brown sugar (DPC) and dark sugar (DPE) were analyzed, determining their characteristics, based on metal concentration and sucrose data, through of the principal component analysis (PCA). For this purpose, a matrix with 97 sugar samples and 6 variables (sucrose, Cu, Ca, Na, Fe and Mg) was obtained, self-scaling the data with zero mean and unit variance. The principal component analysis showed the common and discrepant characteristics between the different brown sugars. In the data arrangement in the PCA, it is possible to observe that the first two principal components explain practically 70% of the total variance of the data. Observing the separation between the light brown sugar group (DPC) and the dark brown sugar group (DPE), in the first principal component while in the second principal component the separation of the samples from the light brown sugar group (DPC) is evident of six dark brown sugar (DPE) samples, which showed similar characteristics to the light brown sugar group (DPC) samples in first principal component. These characteristics of the samples are evidenced probably due to the cooking process in which cleaning is carried out, to remove the foams formed during the heating of the broth, in order to ensure a lighter product. In this operation, as evidenced by the analyzes, calcium and iron are removed, two variables, in addition to sucrose, which are more significant in the classification of dark brown sugar (DPE). |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-07-02 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.ufv.br/jcec/article/view/12796 10.18540/jcecvl7iss3pp12796-01-09e |
url |
https://periodicos.ufv.br/jcec/article/view/12796 |
identifier_str_mv |
10.18540/jcecvl7iss3pp12796-01-09e |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://periodicos.ufv.br/jcec/article/view/12796/6758 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2021 The Journal of Engineering and Exact Sciences https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2021 The Journal of Engineering and Exact Sciences https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Viçosa - UFV |
publisher.none.fl_str_mv |
Universidade Federal de Viçosa - UFV |
dc.source.none.fl_str_mv |
The Journal of Engineering and Exact Sciences; Vol. 7 No. 3 (2021); 12796-01-09e The Journal of Engineering and Exact Sciences; Vol. 7 Núm. 3 (2021); 12796-01-09e The Journal of Engineering and Exact Sciences; v. 7 n. 3 (2021); 12796-01-09e 2527-1075 reponame:The Journal of Engineering and Exact Sciences instname:Universidade Federal de Viçosa (UFV) instacron:UFV |
instname_str |
Universidade Federal de Viçosa (UFV) |
instacron_str |
UFV |
institution |
UFV |
reponame_str |
The Journal of Engineering and Exact Sciences |
collection |
The Journal of Engineering and Exact Sciences |
repository.name.fl_str_mv |
The Journal of Engineering and Exact Sciences - Universidade Federal de Viçosa (UFV) |
repository.mail.fl_str_mv |
|
_version_ |
1798313321886121984 |