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Registro Completo |
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Biblioteca(s): |
Biblioteca Rui Tendinha. |
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Data corrente: |
04/07/2018 |
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Data da última atualização: |
12/04/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
FERRÃO, L. F. V.; FERRÃO, R. G.; FERRÃO, M. A. G.; FONSECA, A. F. A. da.; CARBONETTO, P.; STEPHENS, M.; GARCIA, A. A. F. |
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Afiliação: |
Luis Felipe Ventorim Ferrão, ESALQ; Romário Gava Ferrão, Incaper; Maria Amélia Gava Ferrão, Incaper/Embrapa Café; Aymbiré Francisco Almeida da Fonseca, Incaper/Embrapa Café; Peter Carbonetto, Research Computing Center, University of Chicago; Matthew Stephens, Research Computing Center, University of Chicago; Antonio Augusto Franco Garcia, ESALQ. |
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Título: |
Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Heredity, june 2018. |
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Idioma: |
Português |
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Conteúdo: |
Genomic selection has been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies describing how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee?production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our results support the potential of genomic selection to reshape traditional plant breeding schemes. In practice, we expect to increase the genetic gain per unit of time by reducing the length cycle of recurrent selection in coffee. MenosGenomic selection has been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies describing how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee?production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our... Mostrar Tudo |
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Palavras-Chave: |
Cafe conilon. |
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Thesaurus NAL: |
Coffea canephora; Genomic. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://biblioteca.incaper.es.gov.br/digital/bitstream/item/4674/1/s41437-018-0105-y.pdf
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Marc: |
LEADER 02393naa a2200229 a 4500
001 1020469
005 2024-04-12
008 2018 bl uuuu u00u1 u #d
100 1 $aFERRÃO, L. F. V.
245 $aAccurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models.$h[electronic resource]
260 $c2018
520 $aGenomic selection has been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies describing how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee?production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our results support the potential of genomic selection to reshape traditional plant breeding schemes. In practice, we expect to increase the genetic gain per unit of time by reducing the length cycle of recurrent selection in coffee.
650 $aCoffea canephora
650 $aGenomic
653 $aCafe conilon
700 1 $aFERRÃO, R. G.
700 1 $aFERRÃO, M. A. G.
700 1 $aFONSECA, A. F. A. da.
700 1 $aCARBONETTO, P.
700 1 $aSTEPHENS, M.
700 1 $aGARCIA, A. A. F.
773 $tHeredity, june 2018.
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Biblioteca Rui Tendinha (BRT) |
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 | Acesso ao texto completo restrito à biblioteca da Biblioteca Rui Tendinha. Para informações adicionais entre em contato com biblioteca@incaper.es.gov.br. |
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Registro Completo |
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Biblioteca(s): |
Biblioteca Rui Tendinha. |
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Data corrente: |
07/08/2023 |
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Data da última atualização: |
07/08/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
GOMES, W. dos S.; PEREIRA, L. L.; LUZ, J. M. R. da; OLIVEIRA, E. C. da S.; GUARÇONI, R. G.; MOREIRA, T. R.; FILETE, C. A.; MORELI, A. P.; PARTELLI, F. L. |
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Afiliação: |
Willian dos Santos Gomes; Lucas Louzada Pereira; José Maria Rodrigues da Luz; Emanuele Catarina da Silva Oliveira; Rogerio Carvalho Guarçoni, Incaper; Taís Rizzo Moreira; Cristhiane Altoé Filete; Aldemar Polonini Moreli; Fábio Luiz Partelli. |
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Título: |
Preliminary study of variation in quality of fermented Coffea canephora genotypes using sensory assessment and mid-infrared spectroscopy. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Eur Food Res Technol, 2023. |
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DOI: |
10.1007/s00217-023-04339-1 |
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Idioma: |
Inglês |
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Conteúdo: |
Coffee is one of the most widely consumed beverages in the world. The genetic variability of Coffea canephora has demonstrated significant differences in the chemical compositions of genotypes, resulting in different sensory profiles in the beverage. Fermentation can also affect the sensory quality of coffee beverage. Therefore, the objective of this study was to analyze the sensory profile and the chemical groups that contribute to the sensory qualities of the coffee beverage of C. canephora var. Conilon genotypes subjected to different fermentation processes. Fermentations were carried out with 4 L of cherry coffee or peeled cherry coffee for 36 h at 25 ?. In the induced fermentation, the initial Colony Forming Unit (CFU/mL) was of 107 for Saccharomyces cerevisiae, Klebsiella sp, and Lactobacillus brevis. There was no microbial inoculation in the washed fermentation and the natural process. The sensory quality and chemical groups of the coffee were determined using the Uganda Coffee Development Authority Sensory Analysis Protocol with 6 Q-Graders and mid-infrared spectroscopy, respectively. The sensory and spectrometry analyses were able to distinguish the genotypes, highlighting the separations of genotypes A1 and Verdim with the worst sensory results, and genotype 153 with the best result. Groups formation through the mean Euclidean distance reinforces the sensory differences between fermentations. Furthermore, the greatest chemical changes in coffee beans were promoted by fermentation induced by S. cerevisiae. Thus, the genotypes and the type of fermentation influence in the sensory quality demonstrating potential for optimizing fermentations to improve the sensory quality of conilon coffee. MenosCoffee is one of the most widely consumed beverages in the world. The genetic variability of Coffea canephora has demonstrated significant differences in the chemical compositions of genotypes, resulting in different sensory profiles in the beverage. Fermentation can also affect the sensory quality of coffee beverage. Therefore, the objective of this study was to analyze the sensory profile and the chemical groups that contribute to the sensory qualities of the coffee beverage of C. canephora var. Conilon genotypes subjected to different fermentation processes. Fermentations were carried out with 4 L of cherry coffee or peeled cherry coffee for 36 h at 25 ?. In the induced fermentation, the initial Colony Forming Unit (CFU/mL) was of 107 for Saccharomyces cerevisiae, Klebsiella sp, and Lactobacillus brevis. There was no microbial inoculation in the washed fermentation and the natural process. The sensory quality and chemical groups of the coffee were determined using the Uganda Coffee Development Authority Sensory Analysis Protocol with 6 Q-Graders and mid-infrared spectroscopy, respectively. The sensory and spectrometry analyses were able to distinguish the genotypes, highlighting the separations of genotypes A1 and Verdim with the worst sensory results, and genotype 153 with the best result. Groups formation through the mean Euclidean distance reinforces the sensory differences between fermentations. Furthermore, the greatest chemical changes in coffee beans were promoted ... Mostrar Tudo |
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Palavras-Chave: |
Café conilon. |
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Thesagro: |
Coffea Canephora; Qualidade. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02534naa a2200265 a 4500
001 1025022
005 2023-08-07
008 2023 bl uuuu u00u1 u #d
024 7 $a10.1007/s00217-023-04339-1$2DOI
100 1 $aGOMES, W. dos S.
245 $aPreliminary study of variation in quality of fermented Coffea canephora genotypes using sensory assessment and mid-infrared spectroscopy.$h[electronic resource]
260 $c2023
520 $aCoffee is one of the most widely consumed beverages in the world. The genetic variability of Coffea canephora has demonstrated significant differences in the chemical compositions of genotypes, resulting in different sensory profiles in the beverage. Fermentation can also affect the sensory quality of coffee beverage. Therefore, the objective of this study was to analyze the sensory profile and the chemical groups that contribute to the sensory qualities of the coffee beverage of C. canephora var. Conilon genotypes subjected to different fermentation processes. Fermentations were carried out with 4 L of cherry coffee or peeled cherry coffee for 36 h at 25 ?. In the induced fermentation, the initial Colony Forming Unit (CFU/mL) was of 107 for Saccharomyces cerevisiae, Klebsiella sp, and Lactobacillus brevis. There was no microbial inoculation in the washed fermentation and the natural process. The sensory quality and chemical groups of the coffee were determined using the Uganda Coffee Development Authority Sensory Analysis Protocol with 6 Q-Graders and mid-infrared spectroscopy, respectively. The sensory and spectrometry analyses were able to distinguish the genotypes, highlighting the separations of genotypes A1 and Verdim with the worst sensory results, and genotype 153 with the best result. Groups formation through the mean Euclidean distance reinforces the sensory differences between fermentations. Furthermore, the greatest chemical changes in coffee beans were promoted by fermentation induced by S. cerevisiae. Thus, the genotypes and the type of fermentation influence in the sensory quality demonstrating potential for optimizing fermentations to improve the sensory quality of conilon coffee.
650 $aCoffea Canephora
650 $aQualidade
653 $aCafé conilon
700 1 $aPEREIRA, L. L.
700 1 $aLUZ, J. M. R. da
700 1 $aOLIVEIRA, E. C. da S.
700 1 $aGUARÇONI, R. G.
700 1 $aMOREIRA, T. R.
700 1 $aFILETE, C. A.
700 1 $aMORELI, A. P.
700 1 $aPARTELLI, F. L.
773 $tEur Food Res Technol, 2023.
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