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Registro completo
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Biblioteca (s) : |
INIA Tacuarembó. |
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Fecha : |
28/01/2021 |
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Actualizado : |
28/01/2021 |
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Tipo de producción científica : |
Cartillas |
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Autor : |
INIA (INSTITUTO NACIONAL DE INVESTIGACIÓN AGROPECUARIA); PROGRAMA NACIONAL PRODUCCIÓN DE CARNE Y LANA |
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Afiliación : |
PROGRAMA NACIONAL DE INVESTIGACIÓN PRODUCCIÓN DE CARNE Y LANA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Suplementación por auto suministro sobre campo natural. [folleto] |
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Fecha de publicación : |
2017 |
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Fuente / Imprenta : |
Montevideo (UY): INIA, 2017. |
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Idioma : |
Español |
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Palabras claves : |
PRODUCCIÓN ANIMAL. |
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Asunto categoría : |
L01 Ganadería |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/14963/1/3-Folleto-Suplementacion-auto-suministro-sobre-CN.jpg
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Marc : |
LEADER 00442nam a2200121 a 4500
001 1061698
005 2021-01-28
008 2017 bl uuuu u0uu1 u #d
100 1 $aINIA (INSTITUTO NACIONAL DE INVESTIGACIÓN AGROPECUARIA)
245 $aSuplementación por auto suministro sobre campo natural. [folleto]$h[electronic resource]
260 $aMontevideo (UY): INIA$c2017
653 $aPRODUCCIÓN ANIMAL
700 1 $aPROGRAMA NACIONAL PRODUCCIÓN DE CARNE Y LANA
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Registro original : |
INIA Tacuarembó (TBO) |
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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
22/11/2016 |
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Actualizado : |
22/11/2016 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
A - 1 |
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Autor : |
COZZOLINO, D.; FASSIO, A.; RESTAINO, E.; FERNANDEZ, E.; LA MANNA, A. |
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Afiliación : |
DANIEL COZZOLINO GÓMEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ALBERTO SANTIAGO FASSIO ARAUJO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ERNESTO ANGEL RESTAINO GALUP, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ENRIQUE GENARO FERNANDEZ RODRIGUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ALEJANDRO FRANCISCO LA MANNA ALONSO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Verification of silage type using near-infrared spectroscopy combined with multivariate analysis. |
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Fecha de publicación : |
2008 |
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Fuente / Imprenta : |
Journal of Agricultural and Food Chemistry, 2008, v. 56, no.1, p.79-83. |
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DOI : |
10.1021/jf072566d |
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Idioma : |
Inglés |
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Notas : |
Received 28 August 2007 // Date accepted 8 November 2007 // Published online 27 November 2007 // Published in print 1 January 2008. |
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Contenido : |
ABSTRACT.
The ability to authenticate the feed given to animals has become a major challenge in animal production, where the diet fed to the animal is one of the most important production factors affecting the composition of milk and meat from cattle, sheep, and goats. Hence, there is currently an increased consumer demand for information on herbivore production factors and particularly the animal diet. The aim of this study was to evaluate the reliability and accuracy of near-infrared (NIR) reflectance spectroscopy as a tool to verify and authenticate the type of silage used as fed for ruminants. Grain silage (GrS, n ) 94), grass and legume silage (GLegS, n ) 121), and sunflower silage (SunS, n ) 50) samples were collected from commercial farms and analyzed in the visible and NIR regions (400-2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA),
partial least-squares discriminant analysis (PLS1-DA), and linear discriminant analysis (LDA) models ere used as methods to verify the different silage types. The classification models based on the NIR data correctly classified more than 90% of the silage samples according to their type. The results from this study showed that NIR spectra combined with multivariate analysis could be used as a tool to objectively authenticate silage samples used as a feed for ruminants.
© 2008 American Chemical Society |
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Palabras claves : |
IDENTIFICATION; LINEAR DISCRIMINANT ANALYSIS; NEAR-INFRARED SPECTROSCOPY; PARTIAL LEAST-SQUARES DISCRIMINANT ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; SILAGE. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 02371naa a2200265 a 4500
001 1056114
005 2016-11-22
008 2008 bl uuuu u00u1 u #d
024 7 $a10.1021/jf072566d$2DOI
100 1 $aCOZZOLINO, D.
245 $aVerification of silage type using near-infrared spectroscopy combined with multivariate analysis.$h[electronic resource]
260 $c2008
500 $aReceived 28 August 2007 // Date accepted 8 November 2007 // Published online 27 November 2007 // Published in print 1 January 2008.
520 $aABSTRACT. The ability to authenticate the feed given to animals has become a major challenge in animal production, where the diet fed to the animal is one of the most important production factors affecting the composition of milk and meat from cattle, sheep, and goats. Hence, there is currently an increased consumer demand for information on herbivore production factors and particularly the animal diet. The aim of this study was to evaluate the reliability and accuracy of near-infrared (NIR) reflectance spectroscopy as a tool to verify and authenticate the type of silage used as fed for ruminants. Grain silage (GrS, n ) 94), grass and legume silage (GLegS, n ) 121), and sunflower silage (SunS, n ) 50) samples were collected from commercial farms and analyzed in the visible and NIR regions (400-2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), partial least-squares discriminant analysis (PLS1-DA), and linear discriminant analysis (LDA) models ere used as methods to verify the different silage types. The classification models based on the NIR data correctly classified more than 90% of the silage samples according to their type. The results from this study showed that NIR spectra combined with multivariate analysis could be used as a tool to objectively authenticate silage samples used as a feed for ruminants. © 2008 American Chemical Society
653 $aIDENTIFICATION
653 $aLINEAR DISCRIMINANT ANALYSIS
653 $aNEAR-INFRARED SPECTROSCOPY
653 $aPARTIAL LEAST-SQUARES DISCRIMINANT ANALYSIS
653 $aPRINCIPAL COMPONENT ANALYSIS
653 $aSILAGE
700 1 $aFASSIO, A.
700 1 $aRESTAINO, E.
700 1 $aFERNANDEZ, E.
700 1 $aLA MANNA, A.
773 $tJournal of Agricultural and Food Chemistry, 2008$gv. 56, no.1, p.79-83.
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INIA Las Brujas (LB) |
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