DETERMINATION OF BRACHIARIA SPP. FORAGE QUALITY BY NEAR-INFRARED SPECTROSCOPY AND PARTIAL LEAST SQUARES REGRESSION

Characterizing the chemical properties of forage is critical for the production of improved pastures and livestock development. Conventional analysis methods are very time- and material-consuming, whereas near-infrared spectroscopy (NIRS) and chemometric analyses allow a fast simultaneous determinat...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	MONRROY,MARIEL, GUTIÉRREZ,DEHYLIS, MIRANDA,MARISSA, HERNÁNDEZ,KARLA, RENÁN GARCÍA,JOSÉ
Lenguaje:	English
Publicado:	Sociedad Chilena de Química 2017
Materias:	forage NIRS partial least squares chemical properties
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072017000200010
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:scielo:S0717-97072017000200010
record_format	dspace
spelling	oai:scielo:S0717-970720170002000102017-06-30DETERMINATION OF BRACHIARIA SPP. FORAGE QUALITY BY NEAR-INFRARED SPECTROSCOPY AND PARTIAL LEAST SQUARES REGRESSIONMONRROY,MARIELGUTIÉRREZ,DEHYLISMIRANDA,MARISSAHERNÁNDEZ,KARLARENÁN GARCÍA,JOSÉ forage NIRS partial least squares chemical properties Characterizing the chemical properties of forage is critical for the production of improved pastures and livestock development. Conventional analysis methods are very time- and material-consuming, whereas near-infrared spectroscopy (NIRS) and chemometric analyses allow a fast simultaneous determination of various chemical or physical properties without the use of solvents or large sample amounts. The present research involved the development of models based on NIRS and partial least squares regression (PLS) to estimate the neutral detergent fiber (NDF), acid detergent fiber (ADF), cellulose, and crude protein (CP) contents in Brachiaria spp. forage samples. The models were constructed using spectral data in the range of 800 to 1850 nm. Different preprocessing methods were applied, such as standard normal variate and first-/second-derivative transformations. The obtained calibration models were internally cross-validated, displaying validation errors similar to those obtained for conventional methods. The predictive abilities of the developed models were evaluated for external set samples. NDF, ADF, cellulose, and CP contents were estimated with relative errors of prediction (REPs) of 1.8, 2.6, 4.1, and 8.5%, respectively. NIRS predictions are a useful and profitable tool for fast multi-sample chemical property analysis that is required for the assessment of forage quality. The obtained models are suitable for estimating the key chemical characteristics of forage quality. This research contributes a new approach to determining the quality of Brachiaria spp. forage and provides a new technological tool for the improvement of this crop.info:eu-repo/semantics/openAccessSociedad Chilena de QuímicaJournal of the Chilean Chemical Society v.62 n.2 20172017-06-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072017000200010en10.4067/S0717-97072017000200010
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	forage NIRS partial least squares chemical properties
spellingShingle	forage NIRS partial least squares chemical properties MONRROY,MARIEL GUTIÉRREZ,DEHYLIS MIRANDA,MARISSA HERNÁNDEZ,KARLA RENÁN GARCÍA,JOSÉ DETERMINATION OF BRACHIARIA SPP. FORAGE QUALITY BY NEAR-INFRARED SPECTROSCOPY AND PARTIAL LEAST SQUARES REGRESSION
description	Characterizing the chemical properties of forage is critical for the production of improved pastures and livestock development. Conventional analysis methods are very time- and material-consuming, whereas near-infrared spectroscopy (NIRS) and chemometric analyses allow a fast simultaneous determination of various chemical or physical properties without the use of solvents or large sample amounts. The present research involved the development of models based on NIRS and partial least squares regression (PLS) to estimate the neutral detergent fiber (NDF), acid detergent fiber (ADF), cellulose, and crude protein (CP) contents in Brachiaria spp. forage samples. The models were constructed using spectral data in the range of 800 to 1850 nm. Different preprocessing methods were applied, such as standard normal variate and first-/second-derivative transformations. The obtained calibration models were internally cross-validated, displaying validation errors similar to those obtained for conventional methods. The predictive abilities of the developed models were evaluated for external set samples. NDF, ADF, cellulose, and CP contents were estimated with relative errors of prediction (REPs) of 1.8, 2.6, 4.1, and 8.5%, respectively. NIRS predictions are a useful and profitable tool for fast multi-sample chemical property analysis that is required for the assessment of forage quality. The obtained models are suitable for estimating the key chemical characteristics of forage quality. This research contributes a new approach to determining the quality of Brachiaria spp. forage and provides a new technological tool for the improvement of this crop.
author	MONRROY,MARIEL GUTIÉRREZ,DEHYLIS MIRANDA,MARISSA HERNÁNDEZ,KARLA RENÁN GARCÍA,JOSÉ
author_facet	MONRROY,MARIEL GUTIÉRREZ,DEHYLIS MIRANDA,MARISSA HERNÁNDEZ,KARLA RENÁN GARCÍA,JOSÉ
author_sort	MONRROY,MARIEL
title	DETERMINATION OF BRACHIARIA SPP. FORAGE QUALITY BY NEAR-INFRARED SPECTROSCOPY AND PARTIAL LEAST SQUARES REGRESSION
title_short	DETERMINATION OF BRACHIARIA SPP. FORAGE QUALITY BY NEAR-INFRARED SPECTROSCOPY AND PARTIAL LEAST SQUARES REGRESSION
title_full	DETERMINATION OF BRACHIARIA SPP. FORAGE QUALITY BY NEAR-INFRARED SPECTROSCOPY AND PARTIAL LEAST SQUARES REGRESSION
title_fullStr	DETERMINATION OF BRACHIARIA SPP. FORAGE QUALITY BY NEAR-INFRARED SPECTROSCOPY AND PARTIAL LEAST SQUARES REGRESSION
title_full_unstemmed	DETERMINATION OF BRACHIARIA SPP. FORAGE QUALITY BY NEAR-INFRARED SPECTROSCOPY AND PARTIAL LEAST SQUARES REGRESSION
title_sort	determination of brachiaria spp. forage quality by near-infrared spectroscopy and partial least squares regression
publisher	Sociedad Chilena de Química
publishDate	2017
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072017000200010
work_keys_str_mv	AT monrroymariel determinationofbrachiariasppforagequalitybynearinfraredspectroscopyandpartialleastsquaresregression AT gutierrezdehylis determinationofbrachiariasppforagequalitybynearinfraredspectroscopyandpartialleastsquaresregression AT mirandamarissa determinationofbrachiariasppforagequalitybynearinfraredspectroscopyandpartialleastsquaresregression AT hernandezkarla determinationofbrachiariasppforagequalitybynearinfraredspectroscopyandpartialleastsquaresregression AT renangarciajose determinationofbrachiariasppforagequalitybynearinfraredspectroscopyandpartialleastsquaresregression
_version_	1714200910585921536

DETERMINATION OF BRACHIARIA SPP. FORAGE QUALITY BY NEAR-INFRARED SPECTROSCOPY AND PARTIAL LEAST SQUARES REGRESSION

Ejemplares similares