Predicting ruminal degradability and chemical composition of corn silage using near-infrared spectroscopy and multivariate regression
Pauliane Pucetti, Sebastião de Campos Valadares Filho, Jussara Valente Roque, Julia Travassos da Silva, Kellen Ribeiro de Oliveira, Flavia Adriane Sales Silva, Wilson Junior Cardoso, Fabyano Fonseca e Silva, Kendall Carl Swanson, Aziz ur Rahman Muhammad, Aziz ur Rahman Muhammad
TL;DR
This study shows that near-infrared spectroscopy can predict corn silage composition and some ruminal degradation parameters, offering a faster alternative to traditional methods.
Contribution
The paper introduces validated regression models using NIR spectroscopy for predicting corn silage properties and ruminal degradation.
Findings
NIR models accurately predicted corn silage composition except for organic matter.
Ruminal degradation parameters showed calibration correlation coefficients between 0.530 and 0.985.
NIR has potential as a rapid tool for predicting ruminal degradation in the field.
Abstract
The aim of this study was to develop and validate regression models to predict the chemical composition and ruminal degradation parameters of corn silage by near-infrared spectroscopy (NIR). Ninety-four samples were used to develop and validate the models to predict corn silage composition. A subset of 23 samples was used to develop and validate models to predict ruminal degradation parameters of corn silage. Wet chemistry methods were used to determine the composition values and ruminal degradation parameters of the corn silage samples. The dried and ground samples had their NIR spectra scanned using a poliSPECNIR 900–1700 model NIR sprectrophotometer (ITPhotonics S.r.l, Breganze, IT.). The models were developed using regression by partial least squares (PLS), and the ordered predictor selection (OPS) method was used. In general, the regression models obtained to predict the corn…
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Taxonomy
TopicsSpectroscopy and Chemometric Analyses · Ruminant Nutrition and Digestive Physiology · Meat and Animal Product Quality