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 | Acesso ao texto completo restrito à biblioteca da Embrapa Amazônia Oriental. Para informações adicionais entre em contato com cpatu.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Amazônia Oriental. |
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Data corrente: |
25/06/2019 |
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Data da última atualização: |
05/02/2026 |
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Autoria: |
KAUFMANN, K. C.; FAVERO, F. de F.; VASCONCELOS, M. A. M. de; GODOY, H. T.; SAMPAIO, K. A.; BARBIN, D. F. |
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Afiliação: |
KARINE CRISTINE KAUFMANN, UNIVERSIDADE ESTADUAL DE CAMPINAS; FLÁVIA DE FAVERI FAVERO, UNIVERSIDADE ESTADUAL DE CAMPINAS; MARCUS ARTHUR MARCAL DE VASCONCELOS, CPATU; HELENA TEIXEIRA GODOY, UNIVERSIDADE ESTADUAL DE CAMPINAS; KLICIA ARAUJO SAMPAIO, UNIVERSIDADE ESTADUAL DE CAMPINAS; DOUGLAS FERNANDES BARBIN, UNIVERSIDADE ESTADUAL DE CAMPINAS. |
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Título: |
Portable NIR spectrometer for prediction of palm oil acidity. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Journal of Food Science, v. 84, n. 3, p. 406-411, 2019. |
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DOI: |
10.1111/1750-3841.14467 |
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Idioma: |
Inglês |
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Conteúdo: |
Palm oil is widely used in the food industry, and its quality is associated with the free fatty acids (FFA) content. Determination of FFA in oil is time-consuming, requires chemicals and generates residues. There is trend of applying process analytical technologies (PAT) for fast and nondestructive determination of oil parameters. Portable near-infrared (NIR) spectrometers are cheaper than bench top equipment, and have been used for several tasks in the food processing industry, as it provides fast and reliable data for inline measurements. This study investigated the use of NIR spectra using a portable equipment, combined with both unsupervised and supervised multivariate analyses for identification of palm oil samples with different levels of FFA. Soft independent modeling of class analogy , k-Nearest Neighbors, and linear discriminant analysis models were able to correctly identify 100% of the studied samples with selected wavelengths from NIR spectra. Calibration models were performed for acidity prediction, achieving R2 = 0.97, with root mean square error of prediction = 4.37 for partial least squares model using most relevant wavelengths. These results demonstrate the feasibility of applying a low-cost portable NIR spectrophotometer to predict quality parameters of palm oil. |
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Thesagro: |
Composição Química; Dendê; Elaeis Oleifera; Espectrômetro; Óleo Vegetal. |
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Categoria do assunto: |
W Química e Física |
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Marc: |
LEADER 02029naa a2200253 a 4500
001 2110029
005 2026-02-05
008 2019 bl uuuu u00u1 u #d
024 7 $a10.1111/1750-3841.14467$2DOI
100 1 $aKAUFMANN, K. C.
245 $aPortable NIR spectrometer for prediction of palm oil acidity.$h[electronic resource]
260 $c2019
520 $aPalm oil is widely used in the food industry, and its quality is associated with the free fatty acids (FFA) content. Determination of FFA in oil is time-consuming, requires chemicals and generates residues. There is trend of applying process analytical technologies (PAT) for fast and nondestructive determination of oil parameters. Portable near-infrared (NIR) spectrometers are cheaper than bench top equipment, and have been used for several tasks in the food processing industry, as it provides fast and reliable data for inline measurements. This study investigated the use of NIR spectra using a portable equipment, combined with both unsupervised and supervised multivariate analyses for identification of palm oil samples with different levels of FFA. Soft independent modeling of class analogy , k-Nearest Neighbors, and linear discriminant analysis models were able to correctly identify 100% of the studied samples with selected wavelengths from NIR spectra. Calibration models were performed for acidity prediction, achieving R2 = 0.97, with root mean square error of prediction = 4.37 for partial least squares model using most relevant wavelengths. These results demonstrate the feasibility of applying a low-cost portable NIR spectrophotometer to predict quality parameters of palm oil.
650 $aComposição Química
650 $aDendê
650 $aElaeis Oleifera
650 $aEspectrômetro
650 $aÓleo Vegetal
700 1 $aFAVERO, F. de F.
700 1 $aVASCONCELOS, M. A. M. de
700 1 $aGODOY, H. T.
700 1 $aSAMPAIO, K. A.
700 1 $aBARBIN, D. F.
773 $tJournal of Food Science$gv. 84, n. 3, p. 406-411, 2019.
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Embrapa Amazônia Oriental (CPATU) |
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