Using near infrared spectroscopy to predict metabolizable energy of corn for pigs
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Scientia Agrícola (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162018000600486 |
Resumo: | ABSTRACT: 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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Scientia Agrícola (Online) |
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|
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Using near infrared spectroscopy to predict metabolizable energy of corn for pigschemical compositionprediction equationsvalidationswineABSTRACT: 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.Escola Superior de Agricultura "Luiz de Queiroz"2018-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162018000600486Scientia Agricola v.75 n.6 2018reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/1678-992x-2016-0509info:eu-repo/semantics/openAccessFerreira,Silvia LetíciaVasconcellos,Ricardo SouzaRossi,Robson MarceloPaula,Vinicius Ricardo Cambito deFachinello,Marcelise ReginaHuepa,Laura Marcela DíazPozza,Paulo Cesareng2018-06-20T00:00:00Zoai:scielo:S0103-90162018000600486Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2018-06-20T00:00Scientia 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 |
ABSTRACT: 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 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162018000600486 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162018000600486 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-992x-2016-0509 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
dc.source.none.fl_str_mv |
Scientia Agricola v.75 n.6 2018 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 |
_version_ |
1748936464768434176 |