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https://hdl.handle.net/1822/49955
Título: | UV-Vis and CIELAB based chemometric characterization of manihot esculenta carotenoid contents |
Autor(es): | Afonso, T. Rodolfo, Moresco Uarrota, Virgilio G. Navarro, Bruno Bachiega Nunes, Eduardo da C. Marcelo, Maraschin Rocha, Miguel |
Palavras-chave: | Carotenoids Cassava genotypes Chemometrics CIELAB Machine learning |
Data: | 2017 |
Editora: | De Gruyter Open |
Revista: | Journal of Integrative Bioinformatics |
Citação: | 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 |
Resumo(s): | 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. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/49955 |
DOI: | 10.1515/jib-2017-0056 |
ISSN: | 1613-4516 |
e-ISSN: | 1613-4516 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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document_47425_1.pdf | 1,87 MB | Adobe PDF | Ver/Abrir |