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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
21/02/2014 |
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Actualizado : |
25/11/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
COZZOLINO, D.; MURRAY, I.; SCAIFE, J.R. |
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Afiliación : |
DANIEL COZZOLINO GÓMEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Animal Biology, SAC Aberdeen, Scottish Agricultural College, Aberdeen, Scotland, UK.; JEREMY R SCAIFE, Department of Agriculture, MacRobert Building, University of Aberdeen, Aberdeen, Scotland, UK. |
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Título : |
Near infrared reflectance spectroscopy in the prediction of chemical characteristics of minced raw fish. |
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Fecha de publicación : |
2002 |
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Fuente / Imprenta : |
Aquaculture Nutrition, 2002, Volume 8, Issue 1, Pages 1-6. |
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DOI : |
10.1046/j.1365-2095.2002.00176.x |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 10 July 2000/Accepted 12 December 2000. |
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Contenido : |
Abstract:Near infrared re¯ectance spectroscopy (NIRS) was applied to predict chemical composition in minced raw ®sh samples
used to make ®shmeal. The coe?cients of determination (R2 calibration) and standard error in cross validation (SECV) were 0.99 (3.86) and 0.96 (8.01) in g kg±1 for moisture and oil, respectively. Total volatile nitrogen (TVN) gave R2 and SECV of 0.96 (3.51) in mg g±1. Temperature also was predicted by NIRS, yielding R2 calibration: 0.98 and SECV:calibration 1.07 °C. We conclude that NIRS can be used successfully to assess the chemical composition and storage conditions in minced raw ®sh used by the ®shmeal industry. |
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Palabras claves : |
CARCASS COMPOSITION; FAT; FILLETS; MACKEREL TRACHURUS MURPHY; MEAL PRODUCTION; MUSCLE; PROTEINS; RAINBOW-TROUT; SALMON; TRANSMISSION SPECTROSCOPY. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 01551naa a2200289 a 4500
001 1047873
005 2019-11-25
008 2002 bl uuuu u00u1 u #d
024 7 $a10.1046/j.1365-2095.2002.00176.x$2DOI
100 1 $aCOZZOLINO, D.
245 $aNear infrared reflectance spectroscopy in the prediction of chemical characteristics of minced raw fish.$h[electronic resource]
260 $c2002
500 $aArticle history: Received 10 July 2000/Accepted 12 December 2000.
520 $aAbstract:Near infrared re¯ectance spectroscopy (NIRS) was applied to predict chemical composition in minced raw ®sh samples used to make ®shmeal. The coe?cients of determination (R2 calibration) and standard error in cross validation (SECV) were 0.99 (3.86) and 0.96 (8.01) in g kg±1 for moisture and oil, respectively. Total volatile nitrogen (TVN) gave R2 and SECV of 0.96 (3.51) in mg g±1. Temperature also was predicted by NIRS, yielding R2 calibration: 0.98 and SECV:calibration 1.07 °C. We conclude that NIRS can be used successfully to assess the chemical composition and storage conditions in minced raw ®sh used by the ®shmeal industry.
653 $aCARCASS COMPOSITION
653 $aFAT
653 $aFILLETS
653 $aMACKEREL TRACHURUS MURPHY
653 $aMEAL PRODUCTION
653 $aMUSCLE
653 $aPROTEINS
653 $aRAINBOW-TROUT
653 $aSALMON
653 $aTRANSMISSION SPECTROSCOPY
700 1 $aMURRAY, I.
700 1 $aSCAIFE, J.R.
773 $tAquaculture Nutrition, 2002, Volume 8, Issue 1, Pages 1-6.
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INIA La Estanzuela (LE) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
26/11/2015 |
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Actualizado : |
15/10/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
CARDOSO, F. F.; GOMES, C.C.G.; SOLLERO, B. P.; OLIVEIRA, M. M.; ROSO, V. M.; PICCOLI, M. L.; HIGA, R. H.; YOKOO, M. J.; CAETANO, A. R.; AGUILAR, I. |
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Afiliación : |
F. F. CARDOSO, EMBRAPA (Empresa Brasileira de Pesquisa Agropecuária); Universidad Federal de Pelotas; C.C.G. GOMES, EMBRAPA (Empresa Brasileira de Pesquisa Agropecuária); B. P. SOLLERO, EMBRAPA (Empresa Brasileira de Pesquisa Agropecuária); M.M. OLIVEIRA, EMBRAPA (Empresa Brasileira de Pesquisa Agropecuária); V.M. ROSO, Gensys Associated Consulants; M.L. PICCOLI, EMBRAPA (Empresa Brasileira de Pesquisa Agropecuária); R.H. HIGA, Gensys Associated Consulants; Universidad Federal de Rio Grande Do Sul (UFRGS); M.J. YOKKO, EMBRAPA (Empresa Brasileira de Pesquisa Agropecuária); A.R. CAETANO, EMBRAPA (Empresa Brasileira de Pesquisa Agropecuária); IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Genomic prediction for tick resistance in Braford and Hereford cattle. |
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Fecha de publicación : |
2015 |
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Fuente / Imprenta : |
Journal of Animal Science, 2015. v. 95, p. 2693-2705. Published June 25, 2015 |
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DOI : |
10.2527/jas2014-8832 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received December 19, 2014 / Accepted April 6, 2015. |
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Contenido : |
ABSTRACT.
One of the main animal health problems in tropical and subtropical cattle production is the bovine tick, which causes decreased performance, hide devaluation, increased production costs with acaricide treatments, and transmission of infectious diseases. This study investigated the utility of genomic prediction as a tool to select Braford (BO) and Hereford (HH) cattle resistant to ticks. The accuracy and bias of different methods for direct and blended genomic prediction was assessed using 10,673 tick counts obtained from 3,435 BO and
928 HH cattle belonging to the Delta G Connection breeding program. A subset of 2,803 BO and 652 HH samples were genotyped and 41,045 markers remained after quality control. Log transformed records were adjusted by a pedigree repeatability model to estimate variance components, genetic parameters, and breeding values (EBV) and subsequently used to obtain deregressed EBV. Estimated heritability and repeatability for tick counts were 0.19 ± 0.03 and 0.29 ± 0.01, respectively. Data were split into 5 subsets using k-means and random clustering for cross-validation of genomic predictions. Depending on the method, direct genomic value (DGV) prediction accuracies ranged from 0.35 with Bayes least absolute shrinkage and selection operator (LASSO) to 0.39 with BayesB for k-means clustering and between 0.42 with BayesLASSO and 0.45 with BayesC for random clustering. All genomic methods were superior to pedigree BLUP (PBLUP) accuracies of 0.26 for k-means and 0.29 for random groups, with highest accuracy gains obtained with BayesB (39%) for k-means and BayesC (55%) for random groups. Blending of historical phenotypic and pedigree information by different methods further increased DGV
accuracies by values between 0.03 and 0.05 for direct prediction methods. However, highest accuracy was observed with single-step genomic BLUP with values of 0.48 for k-means and 0.56, which represent, respectively, 84 and 93% improvement over PBLUP. Observed random clustering cross-validation breedspecific accuracies ranged between 0.29 and 0.36 for HH and between 0.55 and 0.61 for BO, depending on the blending method. These moderately high values for BO demonstrate that genomic predictions could be used as a practical tool to improve genetic resistance to ticks and in the development of resistant lines of this breed. For HH, accuracies are still in the low to moderate side and this breed training population needs to be increased before genomic selection could be reliably applied to improve tick resistance.
© 2015 American Society of Animal Science. All rights reserved. MenosABSTRACT.
One of the main animal health problems in tropical and subtropical cattle production is the bovine tick, which causes decreased performance, hide devaluation, increased production costs with acaricide treatments, and transmission of infectious diseases. This study investigated the utility of genomic prediction as a tool to select Braford (BO) and Hereford (HH) cattle resistant to ticks. The accuracy and bias of different methods for direct and blended genomic prediction was assessed using 10,673 tick counts obtained from 3,435 BO and
928 HH cattle belonging to the Delta G Connection breeding program. A subset of 2,803 BO and 652 HH samples were genotyped and 41,045 markers remained after quality control. Log transformed records were adjusted by a pedigree repeatability model to estimate variance components, genetic parameters, and breeding values (EBV) and subsequently used to obtain deregressed EBV. Estimated heritability and repeatability for tick counts were 0.19 ± 0.03 and 0.29 ± 0.01, respectively. Data were split into 5 subsets using k-means and random clustering for cross-validation of genomic predictions. Depending on the method, direct genomic value (DGV) prediction accuracies ranged from 0.35 with Bayes least absolute shrinkage and selection operator (LASSO) to 0.39 with BayesB for k-means clustering and between 0.42 with BayesLASSO and 0.45 with BayesC for random clustering. All genomic methods were superior to pedigree BLUP (PBLUP) accuracies of 0.26 f... Presentar Todo |
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Palabras claves : |
BEEF CATLLE; GENOMIC SELECTION; HEALTH; TICK RESISTANCE. |
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Thesagro : |
GANADO DE CARNE; MEJORAMIENTO GENETICO ANIMAL; SALUD. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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Marc : |
LEADER 03639naa a2200337 a 4500
001 1054002
005 2019-10-15
008 2015 bl uuuu u00u1 u #d
024 7 $a10.2527/jas2014-8832$2DOI
100 1 $aCARDOSO, F. F.
245 $aGenomic prediction for tick resistance in Braford and Hereford cattle.$h[electronic resource]
260 $c2015
500 $aArticle history: Received December 19, 2014 / Accepted April 6, 2015.
520 $aABSTRACT. One of the main animal health problems in tropical and subtropical cattle production is the bovine tick, which causes decreased performance, hide devaluation, increased production costs with acaricide treatments, and transmission of infectious diseases. This study investigated the utility of genomic prediction as a tool to select Braford (BO) and Hereford (HH) cattle resistant to ticks. The accuracy and bias of different methods for direct and blended genomic prediction was assessed using 10,673 tick counts obtained from 3,435 BO and 928 HH cattle belonging to the Delta G Connection breeding program. A subset of 2,803 BO and 652 HH samples were genotyped and 41,045 markers remained after quality control. Log transformed records were adjusted by a pedigree repeatability model to estimate variance components, genetic parameters, and breeding values (EBV) and subsequently used to obtain deregressed EBV. Estimated heritability and repeatability for tick counts were 0.19 ± 0.03 and 0.29 ± 0.01, respectively. Data were split into 5 subsets using k-means and random clustering for cross-validation of genomic predictions. Depending on the method, direct genomic value (DGV) prediction accuracies ranged from 0.35 with Bayes least absolute shrinkage and selection operator (LASSO) to 0.39 with BayesB for k-means clustering and between 0.42 with BayesLASSO and 0.45 with BayesC for random clustering. All genomic methods were superior to pedigree BLUP (PBLUP) accuracies of 0.26 for k-means and 0.29 for random groups, with highest accuracy gains obtained with BayesB (39%) for k-means and BayesC (55%) for random groups. Blending of historical phenotypic and pedigree information by different methods further increased DGV accuracies by values between 0.03 and 0.05 for direct prediction methods. However, highest accuracy was observed with single-step genomic BLUP with values of 0.48 for k-means and 0.56, which represent, respectively, 84 and 93% improvement over PBLUP. Observed random clustering cross-validation breedspecific accuracies ranged between 0.29 and 0.36 for HH and between 0.55 and 0.61 for BO, depending on the blending method. These moderately high values for BO demonstrate that genomic predictions could be used as a practical tool to improve genetic resistance to ticks and in the development of resistant lines of this breed. For HH, accuracies are still in the low to moderate side and this breed training population needs to be increased before genomic selection could be reliably applied to improve tick resistance. © 2015 American Society of Animal Science. All rights reserved.
650 $aGANADO DE CARNE
650 $aMEJORAMIENTO GENETICO ANIMAL
650 $aSALUD
653 $aBEEF CATLLE
653 $aGENOMIC SELECTION
653 $aHEALTH
653 $aTICK RESISTANCE
700 1 $aGOMES, C.C.G.
700 1 $aSOLLERO, B. P.
700 1 $aOLIVEIRA, M. M.
700 1 $aROSO, V. M.
700 1 $aPICCOLI, M. L.
700 1 $aHIGA, R. H.
700 1 $aYOKOO, M. J.
700 1 $aCAETANO, A. R.
700 1 $aAGUILAR, I.
773 $tJournal of Animal Science, 2015.$gv. 95, p. 2693-2705. Published June 25, 2015
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