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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
21/02/2014 |
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Actualizado : |
28/10/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
FASSIO, A.; COZZOLINO, D. |
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Afiliación : |
ALBERTO SANTIAGO FASSIO ARAUJO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; DANIEL COZZOLINO GÓMEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Non-destructive prediction of chemical composition in sunflower seeds by near infrared spectroscopy. |
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Fecha de publicación : |
2004 |
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Fuente / Imprenta : |
Industrial Crops and Products, 2004, 20 (3): 321-329. |
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DOI : |
10.1016/j.indcrop.2003.11.004 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 25 April 2003 / Accepted 14 November 2003. |
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Contenido : |
Abstract:
Near infrared reflectance spectroscopy (NIRS) was explored as a technique to predict moisture (M), oil and crude protein
(CP) content on intact sunflower seeds (Helianthus annuus L.). Three hundred samples were scanned intact in a monochromator
instrument NIRS 6500 (NIRSystems, Silver Spring, MD, USA). Calibration equations were developed using modified partial
least square regression (MPLS) with internal cross validation. Samples were split in two sets, one set used as calibration
(n = 250) where the remaining samples (n = 50) were used as validation set. Two mathematical treatments (first and second
derivative), none (log 1/R) and standard normal variate and detrend (SNVD) as scatter corrections were explored. The coefficient
of determination in calibration (R2
cal) and the standard error in cross validation (SECV) were 0.95 (SECV: 3.3) for M; 0.96
(SECV: 13.1) for CP and 0.90 (SECV: 22.3) for oil in g kg?1 on a dry weight basis (second derivative, 400?2500 nm). Prediction
models accounted for less than 65, 70 and 72% of the total variation for oil, M and CP, respectively. However, it was concluded
that NIRS is a suitable technique to be used as a tool for rapid pre-screening of quality characteristics on breeding programs. |
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Palabras claves : |
CRUDE PROTEIN; MOISTURE; NEAR INFRARED REFLECTANCE SPECTROSCOPY; OIL; SEEDS. |
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Thesagro : |
GIRASOL; HELIANTHUS ANNUUS L; NIRS; PROTEINA CRUDA; SEMILLAS. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 02084naa a2200277 a 4500
001 1049578
005 2019-10-28
008 2004 bl uuuu u00u1 u #d
024 7 $a10.1016/j.indcrop.2003.11.004$2DOI
100 1 $aFASSIO, A.
245 $aNon-destructive prediction of chemical composition in sunflower seeds by near infrared spectroscopy.
260 $c2004
500 $aArticle history: Received 25 April 2003 / Accepted 14 November 2003.
520 $aAbstract: Near infrared reflectance spectroscopy (NIRS) was explored as a technique to predict moisture (M), oil and crude protein (CP) content on intact sunflower seeds (Helianthus annuus L.). Three hundred samples were scanned intact in a monochromator instrument NIRS 6500 (NIRSystems, Silver Spring, MD, USA). Calibration equations were developed using modified partial least square regression (MPLS) with internal cross validation. Samples were split in two sets, one set used as calibration (n = 250) where the remaining samples (n = 50) were used as validation set. Two mathematical treatments (first and second derivative), none (log 1/R) and standard normal variate and detrend (SNVD) as scatter corrections were explored. The coefficient of determination in calibration (R2 cal) and the standard error in cross validation (SECV) were 0.95 (SECV: 3.3) for M; 0.96 (SECV: 13.1) for CP and 0.90 (SECV: 22.3) for oil in g kg?1 on a dry weight basis (second derivative, 400?2500 nm). Prediction models accounted for less than 65, 70 and 72% of the total variation for oil, M and CP, respectively. However, it was concluded that NIRS is a suitable technique to be used as a tool for rapid pre-screening of quality characteristics on breeding programs.
650 $aGIRASOL
650 $aHELIANTHUS ANNUUS L
650 $aNIRS
650 $aPROTEINA CRUDA
650 $aSEMILLAS
653 $aCRUDE PROTEIN
653 $aMOISTURE
653 $aNEAR INFRARED REFLECTANCE SPECTROSCOPY
653 $aOIL
653 $aSEEDS
700 1 $aCOZZOLINO, D.
773 $tIndustrial Crops and Products, 2004, 20 (3): 321-329.
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