| |
|
|
| Registros recuperados : 18 | |
| 1. |  | AGUIAR, A. F. O.; BACK, A. J.; RECIO, M. A. L.; CORSEUIL, C. W. Maximum flow study by the hydrogram method for a watershed in the south of Santa Catarina, Brazil. Revista de Ciências Ambientais, Canoas, v. 15, n. 2, p. 1-18, 2021.| Biblioteca(s): Epagri-Sede. |
|   |
| 2. | | AGUIAR, A. F. O.; CORSEUIL, C. W.; BACK, A. J.; LOBO, M. A. R.; GIEHL, M. R. Ajuste da equação Intensidade-Duração-Frequência de São Bonifácio, Santa Catarina. In: SIMPÓSIO BRASILEIRO DE RECURSOS HÍDRICOS, 23., 2019, Foz do Iguaçú. Anais... Porto Alegre: ABRH, 2019. p. 1-10.| Biblioteca(s): Epagri-Sede. |
| |
| 3. | | AMARAL, L. K.; CADORIN, S. B.; BACK, A. J.; SZYMANSKI,, F. D.; CORSEUIL, C. W. Perdas de solos em bacia hidrográfica de região montanhosa no Sul de Santa Catarina. In: CONGRESSO INTERNACIONAL DE ENGENHARIA AMBIENTAL, 10., REUNIÃO DE ESTUDOS AMBIENTAIS, 10., 2020, Porto Alegre. Resumos... Porto Alegre: UFRGS, 2020. p. 32-33.| Biblioteca(s): Epagri-Sede. |
| |
| 4. | | AMARAL, L. K.; OLIVEIRA, A. J. M.; CORSEUIL, C. W.; BACK, A. J. TENDÊNCIAS CLIMÁTICAS NOS DADOS DE CHUVA DE ITATI, RS. In: CONGRESSO BRASILEIRO DE ENGENHARIA AGRÍCOLA, 49., 2020, Foz do Iguaçu, PR. Anais... Jaboticabal: SBEA, 2020. p. 1-4.| Biblioteca(s): Epagri-Sede. |
| |
| 5. | | AMARALI, L. K.; CADORIN, S. B.; BACK, A. J.; SZYMANSK, F. D.; CORSEUIL, C. W. Estimation of soil loss by the USLE model in a mountain basin in the south of Santa Catarina state, Brazil. Revista Eletrônica em Gestão, Educação e Tecnologia Ambiental, Santa Maria, v. 24, n. e20, p. 1-23, 2020.| Biblioteca(s): Epagri-Sede. |
| |
| 8. | | BACK, A. J.; SOUZA, G. S.; GALATTO, S. L.; CORSEUIL, C. W.; POLETO, C. Erosivity index for Brasil based on Climatological Normals from 1991 to 2020. Holos, Natal, RN, v. 39, n. 3, p. 1-17, 2023.| Biblioteca(s): Epagri-Sede. |
| |
| 12. | | BACK, A. J.; ZAMBRANO, G. J. D.; CORSEUIL, C. W. Streamflow permanence curve of the river Timbó, Santa Catarina, Brazil. Acta Brasiliensis, Campina Grande, v. 3, n. 2, p. 56-62, 2019.| Biblioteca(s): Epagri-Sede. |
| |
| 13. | | BELLETTINI, A. L.; MATUGUMA, R. E.; BACK, A. J.; AGUIAR, A. F. O.; CORSEUIL, C. W. Potencial hidrelétrico com base na vazão mensal e diária para bacia do Rio Capivari, Sul de Santa Catarina, Brasil. In: SIMPÓSIO BRASILEIRO DE RECURSOS HÍDRICOS, 23., 2019, Foz do Iguaçú. Anais... Porto Alegre: ABRH, 2019. p. 1-10.| Biblioteca(s): Epagri-Sede. |
| |
| 18. | | PIMENTEL, L. O.; GIEHL, M. R.; BACK, A. J.; SILVA, B. B.; MARIANO, B. P.; CORSEUIL, C. W. Fatores climáticos e hidrológicos na avaliação da trilha piscinas do Malacara, extremo sul de Santa Catarina. In: SIMPÓSIO BRASILEIRO DE RECURSOS HÍDRICOS, 25., 2023, Aracajú, SE. Anais... Porto Alegre, RS: ABRH, 2023. p. 1-10| Biblioteca(s): Epagri-Sede. |
| |
| Registros recuperados : 18 | |
|
|
|
Registro Completo
|
Biblioteca(s): |
Epagri-Sede. |
|
Data corrente: |
25/10/2017 |
|
Data da última atualização: |
25/10/2017 |
|
Tipo da produção científica: |
Artigo em Anais de Congresso / Nota Técnica |
|
Autoria: |
MORESCO, R.; AFONSO, T.; UARROTA, V. G.; NAVARRO, B. B.; NUNES, E. C.; ROCHA, M.; MARASCHIN, M. |
|
Título: |
Classification tools for carotenoid content estimation in Manihot esculenta via metabolomics and machine learning. |
|
Ano de publicação: |
2017 |
|
Fonte/Imprenta: |
In: INTERNATIONAL CONFERENCE ON PRATICAL APPLICATIONS OF COMPUTATIONAL BIOLOGY & BIOINFORMATICS, 11., 2017, Espanha. Proceedings... Espanha: Springer, 2017. p. 280-288. |
|
Idioma: |
Inglês |
|
Conteúdo: |
Cassava genotypes (Manihot esculenta Crantz) with high pro-vitamin A activity have been identified as a strategy to reduce the prevalence of deficiency of this vitamin. The color variability of cassava roots, which can vary from white to red, is related to the presence of several carotenoid pigments. The present study has shown how CIELAB color measurement on cassava roots tissue can be used as a non-destructive and very fast technique to quantify the levels of carotenoids in cassava root samples, avoiding the use of more expensive analytical techniques for compound quantification, such as UV-visible spectrophotometry and the HPLC. For this, we used machine learning techniques, associating the colorimetric data (CIELAB) with the data obtained by UV-vis and HPLC, to obtain models of prediction of carotenoids for this type of biomass. Best values of R2 (above 90%) were observed for the predictive variable TCC determined by UV-vis spectrophotometry. When we tested the machine learning models using the CIELAB values as inputs, for the total carotenoids contents quantified by HPLC, the Partial Least Squares (PLS), Support Vector Machines, and Elastic Net models presented the best values of R2 (above 40%) and Root-Mean-Square Error (RMSE). For the carotenoid quantification by UV-vis spectrophotometry, R2 (around 60%) and RMSE values (around 6.5) are more satisfactory. Ridge regression and Elastic Network showed the best results. It can be concluded that the use colorimetric technique (CIELAB) associated with UV-vis/HPLC and statistical techniques of prognostic analysis through machine learning can predict the content of total carotenoids in these samples, with good precision and accuracy. MenosCassava genotypes (Manihot esculenta Crantz) with high pro-vitamin A activity have been identified as a strategy to reduce the prevalence of deficiency of this vitamin. The color variability of cassava roots, which can vary from white to red, is related to the presence of several carotenoid pigments. The present study has shown how CIELAB color measurement on cassava roots tissue can be used as a non-destructive and very fast technique to quantify the levels of carotenoids in cassava root samples, avoiding the use of more expensive analytical techniques for compound quantification, such as UV-visible spectrophotometry and the HPLC. For this, we used machine learning techniques, associating the colorimetric data (CIELAB) with the data obtained by UV-vis and HPLC, to obtain models of prediction of carotenoids for this type of biomass. Best values of R2 (above 90%) were observed for the predictive variable TCC determined by UV-vis spectrophotometry. When we tested the machine learning models using the CIELAB values as inputs, for the total carotenoids contents quantified by HPLC, the Partial Least Squares (PLS), Support Vector Machines, and Elastic Net models presented the best values of R2 (above 40%) and Root-Mean-Square Error (RMSE). For the carotenoid quantification by UV-vis spectrophotometry, R2 (around 60%) and RMSE values (around 6.5) are more satisfactory. Ridge regression and Elastic Network showed the best results. It can be concluded that the use colorimetric techni... Mostrar Tudo |
|
Palavras-Chave: |
Carotenoids; Cassava genotypes; Chemometrics; Descriptive models; HPLC; Machine learning; UV-vis. |
|
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
|
|
|
|
Marc: |
LEADER 02615naa a2200277 a 4500
001 1126773
005 2017-10-25
008 2017 bl uuuu u00u1 u #d
100 1 $aMORESCO, R.
245 $aClassification tools for carotenoid content estimation in Manihot esculenta via metabolomics and machine learning.$h[electronic resource]
260 $c2017
520 $aCassava genotypes (Manihot esculenta Crantz) with high pro-vitamin A activity have been identified as a strategy to reduce the prevalence of deficiency of this vitamin. The color variability of cassava roots, which can vary from white to red, is related to the presence of several carotenoid pigments. The present study has shown how CIELAB color measurement on cassava roots tissue can be used as a non-destructive and very fast technique to quantify the levels of carotenoids in cassava root samples, avoiding the use of more expensive analytical techniques for compound quantification, such as UV-visible spectrophotometry and the HPLC. For this, we used machine learning techniques, associating the colorimetric data (CIELAB) with the data obtained by UV-vis and HPLC, to obtain models of prediction of carotenoids for this type of biomass. Best values of R2 (above 90%) were observed for the predictive variable TCC determined by UV-vis spectrophotometry. When we tested the machine learning models using the CIELAB values as inputs, for the total carotenoids contents quantified by HPLC, the Partial Least Squares (PLS), Support Vector Machines, and Elastic Net models presented the best values of R2 (above 40%) and Root-Mean-Square Error (RMSE). For the carotenoid quantification by UV-vis spectrophotometry, R2 (around 60%) and RMSE values (around 6.5) are more satisfactory. Ridge regression and Elastic Network showed the best results. It can be concluded that the use colorimetric technique (CIELAB) associated with UV-vis/HPLC and statistical techniques of prognostic analysis through machine learning can predict the content of total carotenoids in these samples, with good precision and accuracy.
653 $aCarotenoids
653 $aCassava genotypes
653 $aChemometrics
653 $aDescriptive models
653 $aHPLC
653 $aMachine learning
653 $aUV-vis
700 1 $aAFONSO, T.
700 1 $aUARROTA, V. G.
700 1 $aNAVARRO, B. B.
700 1 $aNUNES, E. C.
700 1 $aROCHA, M.
700 1 $aMARASCHIN, M.
773 $tIn: INTERNATIONAL CONFERENCE ON PRATICAL APPLICATIONS OF COMPUTATIONAL BIOLOGY & BIOINFORMATICS, 11., 2017, Espanha. Proceedings... Espanha: Springer, 2017. p. 280-288.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Epagri-Sede (Epagri-Sede) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Expressão de busca inválida. Verifique!!! |
|
|
