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 | Acesso ao texto completo restrito à biblioteca da Embrapa Milho e Sorgo. Para informações adicionais entre em contato com cnpms.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
20/07/2016 |
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Data da última atualização: |
24/02/2017 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
GUIMARAES, C. C.; ASSIS, C.; SIMEONE, M. L. F.; SENA, M. M. |
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Afiliação: |
MARIA LUCIA FERREIRA SIMEONE, CNPMS. |
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Título: |
Use of near-infrared spectroscopy, partial least-squares, and ordered predictors selection to predict four quality parameters of sweet sorghum juice used to produce bioethanol. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
Energy & Fuels, Washington, v. 30, p. 4137- 4144, 2016. |
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DOI: |
10.1021/acs.energyfuels.6b00408 |
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Idioma: |
Inglês |
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Conteúdo: |
Sweet sorghum juice is gaining importance as a raw material for the first-generation ethanol production in the period between harvests of sugar cane. Breeding programs are seeking to improve sorghum quality to increase productivity, what has generated an excessive number of samples to be analyzed. Thus, the aim of this paper was to develop rapid and low-cost methods based on partial least-squares (PLS) and near-infrared spectroscopy (NIRS) for the determination of four quality chemical parameters of sweet sorghum. Spectra were recorded with a transflectance accessory, and robust models were built with 500 samples obtained from more than 200 hybrids and inbred strains. Optimization by variable selection was carried out with ordered predictors selection (OPS), providing simpler, more interpretable and predictive multivariate calibration models. The methods were developed in the working ranges of 5.5−18.1° Brix, 1.2−5.2%, 0.3−13.0%, and 9.8−83.0% for degrees Brix, reducing sugars, polarizable sugars, and apparent purity, respectively. Root-mean-square errors of prediction (RMSEP) of 0.3° Brix, 0.3%, 0.6%, and 5.3% were obtained for these four parameters, respectively. Finally, a complete multivariate analytical validation was carried out, and the methods were considered linear, accurate, sensitive, and without bias. |
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Thesagro: |
Análise de Laboratório; Etanol; Química; Sorgo açucareiro. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02105naa a2200217 a 4500
001 2049288
005 2017-02-24
008 2016 bl uuuu u00u1 u #d
024 7 $a10.1021/acs.energyfuels.6b00408$2DOI
100 1 $aGUIMARAES, C. C.
245 $aUse of near-infrared spectroscopy, partial least-squares, and ordered predictors selection to predict four quality parameters of sweet sorghum juice used to produce bioethanol.$h[electronic resource]
260 $c2016
520 $aSweet sorghum juice is gaining importance as a raw material for the first-generation ethanol production in the period between harvests of sugar cane. Breeding programs are seeking to improve sorghum quality to increase productivity, what has generated an excessive number of samples to be analyzed. Thus, the aim of this paper was to develop rapid and low-cost methods based on partial least-squares (PLS) and near-infrared spectroscopy (NIRS) for the determination of four quality chemical parameters of sweet sorghum. Spectra were recorded with a transflectance accessory, and robust models were built with 500 samples obtained from more than 200 hybrids and inbred strains. Optimization by variable selection was carried out with ordered predictors selection (OPS), providing simpler, more interpretable and predictive multivariate calibration models. The methods were developed in the working ranges of 5.5−18.1° Brix, 1.2−5.2%, 0.3−13.0%, and 9.8−83.0% for degrees Brix, reducing sugars, polarizable sugars, and apparent purity, respectively. Root-mean-square errors of prediction (RMSEP) of 0.3° Brix, 0.3%, 0.6%, and 5.3% were obtained for these four parameters, respectively. Finally, a complete multivariate analytical validation was carried out, and the methods were considered linear, accurate, sensitive, and without bias.
650 $aAnálise de Laboratório
650 $aEtanol
650 $aQuímica
650 $aSorgo açucareiro
700 1 $aASSIS, C.
700 1 $aSIMEONE, M. L. F.
700 1 $aSENA, M. M.
773 $tEnergy & Fuels, Washington$gv. 30, p. 4137- 4144, 2016.
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