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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
05/01/2024 |
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Data da última atualização: |
05/01/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 1 |
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Autoria: |
SCHOLZ, M. B. dos S.; KITZBERGER, C. S. G.; PEREIRA, L. F. P.; DAVRIEUX, F.; POT, D.; CHARMETANT, P.; LEROY, T. |
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Afiliação: |
MARIA BRÍGIDA DOS SANTOS SCHOLZ, INSTITUTO AGRONÔMICO DO PARANÁ; CÍNTIA SORANE GOOD KITZBERGER, INSTITUTO AGRONÔMICO DO PARANÁ; LUIZ FILIPE PROTASIO PEREIRA, CNPCa; FABRICE DAVRIEUX, CENTRO DE COOPERAÇÃO INTERNACIONAL EM PESQUISA AGRONÔMICA PARA O DESENVOLVIMENTO; DAVID POT, CENTRO DE COOPERAÇÃO INTERNACIONAL EM PESQUISA AGRONÔMICA PARA O DESENVOLVIMENTO; PIERRE CHARMETANT, CENTRO DE COOPERAÇÃO INTERNACIONAL EM PESQUISA AGRONÔMICA PARA O DESENVOLVIMENTO; THIERRY LEROY, CENTRO DE COOPERAÇÃO INTERNACIONAL EM PESQUISA AGRONÔMICA PARA O DESENVOLVIMENTO. |
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Título: |
Application of near infrared spectroscopy for green coffee biochemical phenotyping. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
Journal of Near Infrared Spectroscopy, v. 22, n. 6, p. 411-421, 2014. |
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Idioma: |
Inglês |
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Conteúdo: |
Accessions resulting from surveys in Ethiopia (the centre of origin of Arabica coffee) can be used as a source of genetic variability in breeding coffee plants. They may contain some genes of interest for coffee breeding, specifically in relation to beverage quality. Near infrared (NIR) spectroscopy was used to develop models for predicting the major coffee constituents related to quality beverage (proteins, caffeine, lipids, chlorogenic acids, phenolic compounds, total sugars and sucrose). We selected coffee samples listed in a database containing data of chemical contents from samples of traditional and modern cultivars and of Ethiopian accessions to construct models to predict these compounds. Spectra were collected between 1100 nm and 2500 nm, and mathematical pretreatments were applied. The number of samples for the calibration step for each compound was set so as to be representative of distribution values. Cross-validation was performed on the total set of samples to select the optimal number of terms for the prediction models of each component. The prediction models were developed employing a modified partial least-squares regression. The total set of samples for each component was divided randomly into two subsets: one for developing the prediction model and the other to evaluate the predicted values. The best prediction models obtained were for chlorogenic acids (r(2) = 0.94, RPD = 4.16), proteins (r(2) = 0.94, RPD = 4.09) and caffeine (r(2) = 0.92, RPD = 4.16). Models for lipids and phenolic compounds were not as accurate (r(2) = 0.87, RPD = 2.77 and r(2) = 0.86, RPD = 2.62, respectively), while models for sucrose (r(2) = 0.84, RPD = 2.44) and total sugars (r(2) = 0.85, RPD = 2.55) were even less accurate. All these models can be used for identifying coffee lines with more desirable traits in breeding programmes. The models were effective in discriminating Ethiopian coffee accessions from modern cultivars of coffee. Additionally, the NIR technique will make it possible to analyse a large number of samples in breeding programmes and may be used as a high-throughput analysis for green coffee phenotyping. MenosAccessions resulting from surveys in Ethiopia (the centre of origin of Arabica coffee) can be used as a source of genetic variability in breeding coffee plants. They may contain some genes of interest for coffee breeding, specifically in relation to beverage quality. Near infrared (NIR) spectroscopy was used to develop models for predicting the major coffee constituents related to quality beverage (proteins, caffeine, lipids, chlorogenic acids, phenolic compounds, total sugars and sucrose). We selected coffee samples listed in a database containing data of chemical contents from samples of traditional and modern cultivars and of Ethiopian accessions to construct models to predict these compounds. Spectra were collected between 1100 nm and 2500 nm, and mathematical pretreatments were applied. The number of samples for the calibration step for each compound was set so as to be representative of distribution values. Cross-validation was performed on the total set of samples to select the optimal number of terms for the prediction models of each component. The prediction models were developed employing a modified partial least-squares regression. The total set of samples for each component was divided randomly into two subsets: one for developing the prediction model and the other to evaluate the predicted values. The best prediction models obtained were for chlorogenic acids (r(2) = 0.94, RPD = 4.16), proteins (r(2) = 0.94, RPD = 4.09) and caffeine (r(2) = 0.92, RPD = 4.16). Mo... Mostrar Tudo |
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Palavras-Chave: |
Green coffee. |
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Thesaurus NAL: |
Biochemical compounds; Cultivars; High-throughput nucleotide sequencing; Infrared spectroscopy; Phenotype. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02948naa a2200265 a 4500
001 2160470
005 2024-01-05
008 2014 bl uuuu u00u1 u #d
100 1 $aSCHOLZ, M. B. dos S.
245 $aApplication of near infrared spectroscopy for green coffee biochemical phenotyping.$h[electronic resource]
260 $c2014
520 $aAccessions resulting from surveys in Ethiopia (the centre of origin of Arabica coffee) can be used as a source of genetic variability in breeding coffee plants. They may contain some genes of interest for coffee breeding, specifically in relation to beverage quality. Near infrared (NIR) spectroscopy was used to develop models for predicting the major coffee constituents related to quality beverage (proteins, caffeine, lipids, chlorogenic acids, phenolic compounds, total sugars and sucrose). We selected coffee samples listed in a database containing data of chemical contents from samples of traditional and modern cultivars and of Ethiopian accessions to construct models to predict these compounds. Spectra were collected between 1100 nm and 2500 nm, and mathematical pretreatments were applied. The number of samples for the calibration step for each compound was set so as to be representative of distribution values. Cross-validation was performed on the total set of samples to select the optimal number of terms for the prediction models of each component. The prediction models were developed employing a modified partial least-squares regression. The total set of samples for each component was divided randomly into two subsets: one for developing the prediction model and the other to evaluate the predicted values. The best prediction models obtained were for chlorogenic acids (r(2) = 0.94, RPD = 4.16), proteins (r(2) = 0.94, RPD = 4.09) and caffeine (r(2) = 0.92, RPD = 4.16). Models for lipids and phenolic compounds were not as accurate (r(2) = 0.87, RPD = 2.77 and r(2) = 0.86, RPD = 2.62, respectively), while models for sucrose (r(2) = 0.84, RPD = 2.44) and total sugars (r(2) = 0.85, RPD = 2.55) were even less accurate. All these models can be used for identifying coffee lines with more desirable traits in breeding programmes. The models were effective in discriminating Ethiopian coffee accessions from modern cultivars of coffee. Additionally, the NIR technique will make it possible to analyse a large number of samples in breeding programmes and may be used as a high-throughput analysis for green coffee phenotyping.
650 $aBiochemical compounds
650 $aCultivars
650 $aHigh-throughput nucleotide sequencing
650 $aInfrared spectroscopy
650 $aPhenotype
653 $aGreen coffee
700 1 $aKITZBERGER, C. S. G.
700 1 $aPEREIRA, L. F. P.
700 1 $aDAVRIEUX, F.
700 1 $aPOT, D.
700 1 $aCHARMETANT, P.
700 1 $aLEROY, T.
773 $tJournal of Near Infrared Spectroscopy$gv. 22, n. 6, p. 411-421, 2014.
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