UV-Vis and CIELAB based chemometric characterization of manihot esculenta carotenoid contents
Autor(a) principal: | |
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Data de Publicação: | 2017 |
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/1822/49955 |
Resumo: | Vitamin A deficiency is a prevalent health problem in many areas of the world, where cassava genotypes with high pro-vitamin A content have been identified as a strategy to address this issue. In this study, we found a positive correlation between the color of the root pulp and the total carotenoid contents and, importantly, showed how CIELAB color measurements can be used as a non-destructive and fast technique to quantify the amount of carotenoids in cassava root samples, as opposed to traditional methods. We trained several machine learning models using UV-visible spectrophotometry data, CIELAB data and a low-level data fusion of the two. Best performance models were obtained for the total carotenoids contents calculated using the UV-visible dataset as input, with R2 values above 90 %. Using CIELAB and fusion data, values around 60 % and above 90 % were found. Importantly, these results demonstrated how data fusion can lead to a better model performance for prediction when comparing to the use of a single data source. Considering all these findings, the use of colorimetric data associated with UV-visible and HPLC data through statistical and machine learning methods is a reliable way of predicting the content of total carotenoids in cassava root samples. |
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UV-Vis and CIELAB based chemometric characterization of manihot esculenta carotenoid contentsCarotenoidsCassava genotypesChemometricsCIELABMachine learningScience & TechnologyVitamin A deficiency is a prevalent health problem in many areas of the world, where cassava genotypes with high pro-vitamin A content have been identified as a strategy to address this issue. In this study, we found a positive correlation between the color of the root pulp and the total carotenoid contents and, importantly, showed how CIELAB color measurements can be used as a non-destructive and fast technique to quantify the amount of carotenoids in cassava root samples, as opposed to traditional methods. We trained several machine learning models using UV-visible spectrophotometry data, CIELAB data and a low-level data fusion of the two. Best performance models were obtained for the total carotenoids contents calculated using the UV-visible dataset as input, with R2 values above 90 %. Using CIELAB and fusion data, values around 60 % and above 90 % were found. Importantly, these results demonstrated how data fusion can lead to a better model performance for prediction when comparing to the use of a single data source. Considering all these findings, the use of colorimetric data associated with UV-visible and HPLC data through statistical and machine learning methods is a reliable way of predicting the content of total carotenoids in cassava root samples.To CNPq (National Counsel of Technological and Scientific Development) for financial support (Process n 407323/2013-9), to CAPES (Coordination for the Improvement of Higher Education Personnel (CAPES), and EPAGRI(AgriculturalResearchandRuralExtensionCompanyofSantaCatarina).Theresearchfellowshipfrom CNPqonbehalfofM.Maraschinisacknowledged.TheworkispartiallyfundedbyProjectPropMine,funded bytheagreementbetweenPortugueseFCT(FoundationforScienceandTechnology)andBrazilianCNPq.info:eu-repo/semantics/publishedVersionDe Gruyter OpenUniversidade do MinhoAfonso, T.Rodolfo, MorescoUarrota, Virgilio G.Navarro, Bruno BachiegaNunes, Eduardo da C.Marcelo, MaraschinRocha, Miguel20172017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/49955engAfonso, T.; Rodolfo, Moresco; Uarrota, Virgilio G.; Navarro, Bruno Bachiega; Nunes, Eduardo da C.; Marcelo, Maraschin; Rocha, Miguel, UV-Vis and CIELAB based chemometric characterization of manihot esculenta carotenoid contents. Journal of Integrative Bioinformatics, 14(4, SI), 20171613-45161613-451610.1515/jib-2017-0056info: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:RCAAP2023-07-21T12:35:27Zoai:repositorium.sdum.uminho.pt:1822/49955Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:31:18.981Repositório 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 |
UV-Vis and CIELAB based chemometric characterization of manihot esculenta carotenoid contents |
title |
UV-Vis and CIELAB based chemometric characterization of manihot esculenta carotenoid contents |
spellingShingle |
UV-Vis and CIELAB based chemometric characterization of manihot esculenta carotenoid contents Afonso, T. Carotenoids Cassava genotypes Chemometrics CIELAB Machine learning Science & Technology |
title_short |
UV-Vis and CIELAB based chemometric characterization of manihot esculenta carotenoid contents |
title_full |
UV-Vis and CIELAB based chemometric characterization of manihot esculenta carotenoid contents |
title_fullStr |
UV-Vis and CIELAB based chemometric characterization of manihot esculenta carotenoid contents |
title_full_unstemmed |
UV-Vis and CIELAB based chemometric characterization of manihot esculenta carotenoid contents |
title_sort |
UV-Vis and CIELAB based chemometric characterization of manihot esculenta carotenoid contents |
author |
Afonso, T. |
author_facet |
Afonso, T. Rodolfo, Moresco Uarrota, Virgilio G. Navarro, Bruno Bachiega Nunes, Eduardo da C. Marcelo, Maraschin Rocha, Miguel |
author_role |
author |
author2 |
Rodolfo, Moresco Uarrota, Virgilio G. Navarro, Bruno Bachiega Nunes, Eduardo da C. Marcelo, Maraschin Rocha, Miguel |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Afonso, T. Rodolfo, Moresco Uarrota, Virgilio G. Navarro, Bruno Bachiega Nunes, Eduardo da C. Marcelo, Maraschin Rocha, Miguel |
dc.subject.por.fl_str_mv |
Carotenoids Cassava genotypes Chemometrics CIELAB Machine learning Science & Technology |
topic |
Carotenoids Cassava genotypes Chemometrics CIELAB Machine learning Science & Technology |
description |
Vitamin A deficiency is a prevalent health problem in many areas of the world, where cassava genotypes with high pro-vitamin A content have been identified as a strategy to address this issue. In this study, we found a positive correlation between the color of the root pulp and the total carotenoid contents and, importantly, showed how CIELAB color measurements can be used as a non-destructive and fast technique to quantify the amount of carotenoids in cassava root samples, as opposed to traditional methods. We trained several machine learning models using UV-visible spectrophotometry data, CIELAB data and a low-level data fusion of the two. Best performance models were obtained for the total carotenoids contents calculated using the UV-visible dataset as input, with R2 values above 90 %. Using CIELAB and fusion data, values around 60 % and above 90 % were found. Importantly, these results demonstrated how data fusion can lead to a better model performance for prediction when comparing to the use of a single data source. Considering all these findings, the use of colorimetric data associated with UV-visible and HPLC data through statistical and machine learning methods is a reliable way of predicting the content of total carotenoids in cassava root samples. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 2017-01-01T00:00:00Z |
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/1822/49955 |
url |
http://hdl.handle.net/1822/49955 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Afonso, T.; Rodolfo, Moresco; Uarrota, Virgilio G.; Navarro, Bruno Bachiega; Nunes, Eduardo da C.; Marcelo, Maraschin; Rocha, Miguel, UV-Vis and CIELAB based chemometric characterization of manihot esculenta carotenoid contents. Journal of Integrative Bioinformatics, 14(4, SI), 2017 1613-4516 1613-4516 10.1515/jib-2017-0056 |
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 |
De Gruyter Open |
publisher.none.fl_str_mv |
De Gruyter Open |
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 |
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1799132820612841472 |