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| 1. |  | FARIELLO, M.I.; ARBOLEYA, L.; BELZARENA, D.; DE LOS SANTOS, L.; ELENTER, J.; ETCHEBARNE, G.; HOUNIE, I.; CIAPPESONI, G.; NAVAJAS, E.; LECUMBERRY, F. Something old, something new, something borrowed : Evaluation of different neural network architectures for genomic prediction. [poster] En: Plant & Animal Genome Conference : PAG 30, San Diego, California, USA, 13-18 january 2023. Editorial: Plant and Animal Genome Conference (PAG). Este trabajo fue parcialmente financiado por la Universidad de la República y el proyecto ANII FDA 1_2018_1_154364. -- LICENCIA: Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0).| Biblioteca(s): INIA Las Brujas. |
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 | Acceso al texto completo restringido a Biblioteca INIA La Estanzuela. Por información adicional contacte bib_le@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha actual : |
21/02/2014 |
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Actualizado : |
30/09/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
A - 2 |
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Autor : |
COZZOLINO, D.; MORON, A. |
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Afiliación : |
DANIEL COZZOLINO GÓMEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; DAVID ALEJANDRO MORON YACOEL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Exploring the use of near infrared reflectance spectroscopy (NIRS) to predict trace minerals in legumes. |
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Fecha de publicación : |
2004 |
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Fuente / Imprenta : |
Animal Feed Science and Technology, Volume 111, Issues 1?4, 12 January 2004, Pages 161-173. |
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DOI : |
10.1016/j.anifeedsci.2003.08.001 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 28 January 2003 / Received in revised form 23 July 2003 / Accepted 5 August 2003. |
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Contenido : |
Abstract:
The use of near infrared reflectance spectroscopy (NIRS) was explored to predict trace mineral concentrations in two legumes. Samples (332), composite of white clover (n=97) and lucerne (n=235), from different locations in Uruguay representing a wide range of soil types, were analysed for sodium (Na), sulphur (S), copper (Cu), iron (Fe), manganese (Mn), zinc (Zn), and boron (B). The samples were scanned in reflectance in a monochromator instrument (400?2500 nm). Calibration models (n=262) were developed using modified partial least squares regression (MPLS) based on cross-validation and tested using a validation set (n=70). Two mathematical treatments of the spectra were compared (first and second derivative). The highest coefficients of determination in calibration (RCAL2) and the lowest standard errors of cross-validation (SECV) were obtained using second derivative. The RCAL2 and SECV were 0.83 (SECV: 0.8) for Na and 0.86 (SECV: 2.5) for S in g kg?1 DM; 0.80 (SECV: 4.4), 0.80 (SECV: 10.6), 0.78 (SECV: 22.9), 0.76 (SECV: 0.83) and 0.57 (SECV 25.7) for B, Zn, Mn, Cu and Fe in mg kg?1 DM on a dry weight, respectively. Sulphur (SEP: 5.5), sodium (SEP: 1.2) and boron (SEP 4.2) were well predicted by NIRS on a validation set. |
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Palabras claves : |
LEGUMES FORAGE QUALITY; NIRS; PARTIAL LEAST SQUARES; TRACE MINERALS. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 02004naa a2200205 a 4500
001 1049567
005 2019-09-30
008 2004 bl uuuu u00u1 u #d
024 7 $a10.1016/j.anifeedsci.2003.08.001$2DOI
100 1 $aCOZZOLINO, D.
245 $aExploring the use of near infrared reflectance spectroscopy (NIRS) to predict trace minerals in legumes.
260 $c2004
500 $aArticle history: Received 28 January 2003 / Received in revised form 23 July 2003 / Accepted 5 August 2003.
520 $aAbstract: The use of near infrared reflectance spectroscopy (NIRS) was explored to predict trace mineral concentrations in two legumes. Samples (332), composite of white clover (n=97) and lucerne (n=235), from different locations in Uruguay representing a wide range of soil types, were analysed for sodium (Na), sulphur (S), copper (Cu), iron (Fe), manganese (Mn), zinc (Zn), and boron (B). The samples were scanned in reflectance in a monochromator instrument (400?2500 nm). Calibration models (n=262) were developed using modified partial least squares regression (MPLS) based on cross-validation and tested using a validation set (n=70). Two mathematical treatments of the spectra were compared (first and second derivative). The highest coefficients of determination in calibration (RCAL2) and the lowest standard errors of cross-validation (SECV) were obtained using second derivative. The RCAL2 and SECV were 0.83 (SECV: 0.8) for Na and 0.86 (SECV: 2.5) for S in g kg?1 DM; 0.80 (SECV: 4.4), 0.80 (SECV: 10.6), 0.78 (SECV: 22.9), 0.76 (SECV: 0.83) and 0.57 (SECV 25.7) for B, Zn, Mn, Cu and Fe in mg kg?1 DM on a dry weight, respectively. Sulphur (SEP: 5.5), sodium (SEP: 1.2) and boron (SEP 4.2) were well predicted by NIRS on a validation set.
653 $aLEGUMES FORAGE QUALITY
653 $aNIRS
653 $aPARTIAL LEAST SQUARES
653 $aTRACE MINERALS
700 1 $aMORON, A.
773 $tAnimal Feed Science and Technology, Volume 111, Issues 1?4, 12 January 2004, Pages 161-173.
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