Using near infrared spectroscopy to predict metabolizable energy of corn for pigs

Detalhes bibliográficos
Autor(a) principal: Ferreira, Silvia Letícia
Data de Publicação: 2018
Outros Autores: Vasconcellos, Ricardo Souza, Rossi, Robson Marcelo, Paula, Vinicius Ricardo Cambito de, Fachinello, Marcelise Regina, Huepa, Laura Marcela Díaz, Pozza, Paulo Cesar
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Scientia Agrícola (Online)
Texto Completo: https://www.revistas.usp.br/sa/article/view/147488
Resumo: The chemical composition of corn is variable and the knowledge of its chemical and energetic composition is required for an accurate formulation of the diet. This study aimed to determine the chemical composition, that is, dry matter (DM), mineral matter (MM), neutral detergent fiber (NDF), acid detergent fiber (ADF), ether extract (EE), crude protein (CP), gross energy (GE) and energetic values of different varieties (batches) of corn and validate mathematical models to predict the metabolizable energy values (ME) of corn for pigs using near infrared spectroscopy (NIRS). Corn samples were scanned in the spectrum range between 1,100 and 2,500 nm, the model parameters were estimated by the modified partial least squares (MPLS) method. Ten prediction equations were inserted into the NIRS and used to estimate the ME values. The first degree linear regression models of the estimated ME values in function of the observed ME values were adjusted. The existence of a linear ratio was evaluated by detecting the significance to posterior estimates of the straight line parameters. The values of digestible energy and ME ranged from 3,400 to 3,752 and 3,244 to 3,611 kcal kg−1, respectively. The prediction equations, ME1 = 4334 – 8.1MM + 4.1EE – 3.7NDF; ME2 = 4,194 – 9.2MM + 1.0CP + 4.1EE – 3.5NDF; and ME7 = 16.13 – 9.5NDF + 16EE + (23CP × NDF) – (138MM × NDF) were the most adequate to predict the ME values of corn by using NIRS.
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spelling Using near infrared spectroscopy to predict metabolizable energy of corn for pigschemical compositionprediction equationsvalidationswine The chemical composition of corn is variable and the knowledge of its chemical and energetic composition is required for an accurate formulation of the diet. This study aimed to determine the chemical composition, that is, dry matter (DM), mineral matter (MM), neutral detergent fiber (NDF), acid detergent fiber (ADF), ether extract (EE), crude protein (CP), gross energy (GE) and energetic values of different varieties (batches) of corn and validate mathematical models to predict the metabolizable energy values (ME) of corn for pigs using near infrared spectroscopy (NIRS). Corn samples were scanned in the spectrum range between 1,100 and 2,500 nm, the model parameters were estimated by the modified partial least squares (MPLS) method. Ten prediction equations were inserted into the NIRS and used to estimate the ME values. The first degree linear regression models of the estimated ME values in function of the observed ME values were adjusted. The existence of a linear ratio was evaluated by detecting the significance to posterior estimates of the straight line parameters. The values of digestible energy and ME ranged from 3,400 to 3,752 and 3,244 to 3,611 kcal kg−1, respectively. The prediction equations, ME1 = 4334 – 8.1MM + 4.1EE – 3.7NDF; ME2 = 4,194 – 9.2MM + 1.0CP + 4.1EE – 3.5NDF; and ME7 = 16.13 – 9.5NDF + 16EE + (23CP × NDF) – (138MM × NDF) were the most adequate to predict the ME values of corn by using NIRS.Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz2018-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistas.usp.br/sa/article/view/14748810.1590/1678-992x-2016-0509Scientia Agricola; v. 75 n. 6 (2018); 486-493Scientia Agricola; Vol. 75 Núm. 6 (2018); 486-493Scientia Agricola; Vol. 75 No. 6 (2018); 486-4931678-992X0103-9016reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USPenghttps://www.revistas.usp.br/sa/article/view/147488/141004Copyright (c) 2018 Scientia Agricolainfo:eu-repo/semantics/openAccessFerreira, Silvia LetíciaVasconcellos, Ricardo SouzaRossi, Robson MarceloPaula, Vinicius Ricardo Cambito deFachinello, Marcelise ReginaHuepa, Laura Marcela DíazPozza, Paulo Cesar2018-06-25T17:28:21Zoai:revistas.usp.br:article/147488Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2018-06-25T17:28:21Scientia Agrícola (Online) - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Using near infrared spectroscopy to predict metabolizable energy of corn for pigs
title Using near infrared spectroscopy to predict metabolizable energy of corn for pigs
spellingShingle Using near infrared spectroscopy to predict metabolizable energy of corn for pigs
Ferreira, Silvia Letícia
chemical composition
prediction equations
validation
swine
title_short Using near infrared spectroscopy to predict metabolizable energy of corn for pigs
title_full Using near infrared spectroscopy to predict metabolizable energy of corn for pigs
title_fullStr Using near infrared spectroscopy to predict metabolizable energy of corn for pigs
title_full_unstemmed Using near infrared spectroscopy to predict metabolizable energy of corn for pigs
title_sort Using near infrared spectroscopy to predict metabolizable energy of corn for pigs
author Ferreira, Silvia Letícia
author_facet Ferreira, Silvia Letícia
Vasconcellos, Ricardo Souza
Rossi, Robson Marcelo
Paula, Vinicius Ricardo Cambito de
Fachinello, Marcelise Regina
Huepa, Laura Marcela Díaz
Pozza, Paulo Cesar
author_role author
author2 Vasconcellos, Ricardo Souza
Rossi, Robson Marcelo
Paula, Vinicius Ricardo Cambito de
Fachinello, Marcelise Regina
Huepa, Laura Marcela Díaz
Pozza, Paulo Cesar
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Ferreira, Silvia Letícia
Vasconcellos, Ricardo Souza
Rossi, Robson Marcelo
Paula, Vinicius Ricardo Cambito de
Fachinello, Marcelise Regina
Huepa, Laura Marcela Díaz
Pozza, Paulo Cesar
dc.subject.por.fl_str_mv chemical composition
prediction equations
validation
swine
topic chemical composition
prediction equations
validation
swine
description The chemical composition of corn is variable and the knowledge of its chemical and energetic composition is required for an accurate formulation of the diet. This study aimed to determine the chemical composition, that is, dry matter (DM), mineral matter (MM), neutral detergent fiber (NDF), acid detergent fiber (ADF), ether extract (EE), crude protein (CP), gross energy (GE) and energetic values of different varieties (batches) of corn and validate mathematical models to predict the metabolizable energy values (ME) of corn for pigs using near infrared spectroscopy (NIRS). Corn samples were scanned in the spectrum range between 1,100 and 2,500 nm, the model parameters were estimated by the modified partial least squares (MPLS) method. Ten prediction equations were inserted into the NIRS and used to estimate the ME values. The first degree linear regression models of the estimated ME values in function of the observed ME values were adjusted. The existence of a linear ratio was evaluated by detecting the significance to posterior estimates of the straight line parameters. The values of digestible energy and ME ranged from 3,400 to 3,752 and 3,244 to 3,611 kcal kg−1, respectively. The prediction equations, ME1 = 4334 – 8.1MM + 4.1EE – 3.7NDF; ME2 = 4,194 – 9.2MM + 1.0CP + 4.1EE – 3.5NDF; and ME7 = 16.13 – 9.5NDF + 16EE + (23CP × NDF) – (138MM × NDF) were the most adequate to predict the ME values of corn by using NIRS.
publishDate 2018
dc.date.none.fl_str_mv 2018-12-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://www.revistas.usp.br/sa/article/view/147488
10.1590/1678-992x-2016-0509
url https://www.revistas.usp.br/sa/article/view/147488
identifier_str_mv 10.1590/1678-992x-2016-0509
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.revistas.usp.br/sa/article/view/147488/141004
dc.rights.driver.fl_str_mv Copyright (c) 2018 Scientia Agricola
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2018 Scientia Agricola
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz
publisher.none.fl_str_mv Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz
dc.source.none.fl_str_mv Scientia Agricola; v. 75 n. 6 (2018); 486-493
Scientia Agricola; Vol. 75 Núm. 6 (2018); 486-493
Scientia Agricola; Vol. 75 No. 6 (2018); 486-493
1678-992X
0103-9016
reponame:Scientia Agrícola (Online)
instname:Universidade de São Paulo (USP)
instacron:USP
instname_str Universidade de São Paulo (USP)
instacron_str USP
institution USP
reponame_str Scientia Agrícola (Online)
collection Scientia Agrícola (Online)
repository.name.fl_str_mv Scientia Agrícola (Online) - Universidade de São Paulo (USP)
repository.mail.fl_str_mv scientia@usp.br||alleoni@usp.br
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