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Registro Completo |
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Biblioteca(s): |
Embrapa Mandioca e Fruticultura. |
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Data corrente: |
08/12/2022 |
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Data da última atualização: |
08/12/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CARVALHO, R. R. B. de; CORTES, D. F. M.; SOUSA, M. B. e; OLIVEIRA, L. A. de; OLIVEIRA, E. J. de. |
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Afiliação: |
RAVENA ROCHA BESSA DE CARVALHO, UNIVERSIDADE FEDERAL DO RECÔNCAVO DA BAHIA; DIEGO FERNANDO MARMOLEJO CORTES; MASSAINE BANDEIRA E SOUSA; LUCIANA ALVES DE OLIVEIRA, CNPMF; EDER JORGE DE OLIVEIRA, CNPMF. |
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Título: |
Image-based phenotyping of cassava roots for diversity studies and carotenoids prediction. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
PLoS One, v.17, n.1, e0263326, January, 2022. |
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ISSN: |
1932-6203 |
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DOI: |
https://doi.org/10.1371/journal.pone.0263326 |
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Idioma: |
Inglês |
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Conteúdo: |
Phenotyping to quantify the total carotenoids content (TCC) is sensitive, time-consuming, tedious, and costly. The development of high-throughput phenotyping tools is essential for screening hundreds of cassava genotypes in a short period of time in the biofortification program. This study aimed to (i) use digital images to extract information on the pulp color of cassava roots and estimate correlations with TCC, and (ii) select predictive models for TCC using colorimetric indices. Red, green and blue images were captured in root samples from 228 biofortified genotypes and the difference in color was analyzed using L*, a*, b*, hue and chroma indices from the International Commission on Illumination (CIELAB) color system and lightness. Colorimetric data were used for principal component analysis (PCA), correlation and for developing prediction models for TCC based on regression and machine learning. A high positive correlation between TCC and the variables b* (r = 0.90) and chroma (r = 0.89) was identified, while the other correlations were median and negative, and the L* parameter did not present a significant correlation with TCC. In general, the accuracy of most prediction models (with all variables and only the most important ones) was high (R2 ranging from 0.81 to 0.94). However, the artificial neural network prediction model presented the best predictive ability (R2 = 0.94), associated with the smallest error in the TCC estimates (root-mean-square error of 0.24). The structure of the studied population revealed five groups and high genetic variability based on PCA regarding colorimetric indices and TCC. Our results demonstrated that the use of data obtained from digital image analysis is an economical, fast, and effective alternative for the development of TCC phenotyping tools in cassava roots with high predictive ability. MenosPhenotyping to quantify the total carotenoids content (TCC) is sensitive, time-consuming, tedious, and costly. The development of high-throughput phenotyping tools is essential for screening hundreds of cassava genotypes in a short period of time in the biofortification program. This study aimed to (i) use digital images to extract information on the pulp color of cassava roots and estimate correlations with TCC, and (ii) select predictive models for TCC using colorimetric indices. Red, green and blue images were captured in root samples from 228 biofortified genotypes and the difference in color was analyzed using L*, a*, b*, hue and chroma indices from the International Commission on Illumination (CIELAB) color system and lightness. Colorimetric data were used for principal component analysis (PCA), correlation and for developing prediction models for TCC based on regression and machine learning. A high positive correlation between TCC and the variables b* (r = 0.90) and chroma (r = 0.89) was identified, while the other correlations were median and negative, and the L* parameter did not present a significant correlation with TCC. In general, the accuracy of most prediction models (with all variables and only the most important ones) was high (R2 ranging from 0.81 to 0.94). However, the artificial neural network prediction model presented the best predictive ability (R2 = 0.94), associated with the smallest error in the TCC estimates (root-mean-square error of 0.24). The st... Mostrar Tudo |
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Thesagro: |
Mandioca. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1149383/1/journal.pone.0263326.pdf
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Marc: |
LEADER 02501naa a2200205 a 4500
001 2149383
005 2022-12-08
008 2022 bl uuuu u00u1 u #d
022 $a1932-6203
024 7 $ahttps://doi.org/10.1371/journal.pone.0263326$2DOI
100 1 $aCARVALHO, R. R. B. de
245 $aImage-based phenotyping of cassava roots for diversity studies and carotenoids prediction.$h[electronic resource]
260 $c2022
520 $aPhenotyping to quantify the total carotenoids content (TCC) is sensitive, time-consuming, tedious, and costly. The development of high-throughput phenotyping tools is essential for screening hundreds of cassava genotypes in a short period of time in the biofortification program. This study aimed to (i) use digital images to extract information on the pulp color of cassava roots and estimate correlations with TCC, and (ii) select predictive models for TCC using colorimetric indices. Red, green and blue images were captured in root samples from 228 biofortified genotypes and the difference in color was analyzed using L*, a*, b*, hue and chroma indices from the International Commission on Illumination (CIELAB) color system and lightness. Colorimetric data were used for principal component analysis (PCA), correlation and for developing prediction models for TCC based on regression and machine learning. A high positive correlation between TCC and the variables b* (r = 0.90) and chroma (r = 0.89) was identified, while the other correlations were median and negative, and the L* parameter did not present a significant correlation with TCC. In general, the accuracy of most prediction models (with all variables and only the most important ones) was high (R2 ranging from 0.81 to 0.94). However, the artificial neural network prediction model presented the best predictive ability (R2 = 0.94), associated with the smallest error in the TCC estimates (root-mean-square error of 0.24). The structure of the studied population revealed five groups and high genetic variability based on PCA regarding colorimetric indices and TCC. Our results demonstrated that the use of data obtained from digital image analysis is an economical, fast, and effective alternative for the development of TCC phenotyping tools in cassava roots with high predictive ability.
650 $aMandioca
700 1 $aCORTES, D. F. M.
700 1 $aSOUSA, M. B. e
700 1 $aOLIVEIRA, L. A. de
700 1 $aOLIVEIRA, E. J. de
773 $tPLoS One$gv.17, n.1, e0263326, January, 2022.
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Registro original: |
Embrapa Mandioca e Fruticultura (CNPMF) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Caprinos e Ovinos. Para informações adicionais entre em contato com cnpc.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Caprinos e Ovinos. |
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Data corrente: |
11/12/2008 |
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Data da última atualização: |
25/08/2021 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
ANDRIOLI, A.; SOUZA, K. C. de; SANTOS, D. O. |
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Afiliação: |
ALICE ANDRIOLI, CNPC; KELMA COSTA DE SOUZA, Graduanda de Zootecnia Universidade Estadual Vale do Acaraú; DIONES OLIVEIRA SANTOS, CNPC. |
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Título: |
Programas de transferência de embriões em cabras da raça Moxotó criadas no Semi-árido cearense. |
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Ano de publicação: |
2008 |
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Fonte/Imprenta: |
In: SIMPÓSIO BRASILEIRO DE RECURSOS GENÉTICOS, 2., 2008, Brasília, DF. Anais... Brasília, DF: Embrapa Recursos Genéticos e Biotecnologia, 2008. p. 539. Resumo. |
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Idioma: |
Português |
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Palavras-Chave: |
Brasil; Ceará; Moxotó; Raça nativa. |
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Thesagro: |
Banco de Germoplasma; Caprino; Genética Animal; Transferência de Embrião. |
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Thesaurus NAL: |
Brazil; Conservation banking; Embryo transfer; Germplasm; Goats; Semen banks. |
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Categoria do assunto: |
H Saúde e Patologia |
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Marc: |
LEADER 00944nam a2200289 a 4500
001 1534067
005 2021-08-25
008 2008 bl uuuu u00u1 u #d
100 1 $aANDRIOLI, A.
245 $aProgramas de transferência de embriões em cabras da raça Moxotó criadas no Semi-árido cearense.$h[electronic resource]
260 $aIn: SIMPÓSIO BRASILEIRO DE RECURSOS GENÉTICOS, 2., 2008, Brasília, DF. Anais... Brasília, DF: Embrapa Recursos Genéticos e Biotecnologia, 2008. p. 539. Resumo.$c2008
650 $aBrazil
650 $aConservation banking
650 $aEmbryo transfer
650 $aGermplasm
650 $aGoats
650 $aSemen banks
650 $aBanco de Germoplasma
650 $aCaprino
650 $aGenética Animal
650 $aTransferência de Embrião
653 $aBrasil
653 $aCeará
653 $aMoxotó
653 $aRaça nativa
700 1 $aSOUZA, K. C. de
700 1 $aSANTOS, D. O.
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Embrapa Caprinos e Ovinos (CNPC) |
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