Prediction models of the nutritional quality of fresh and dry Brachiaria brizantha cv. Piatã grass by near infrared spectroscopy

This study aimed to generate prediction models to estimate the chemical composition of fresh and dry Brachiaria brizantha cv. Piatã grass using near infrared spectroscopy (NIRS). Chemical analyses of 249 samples were performed to determine oven-dried sample (ODS), dry matter (DM), crude protein (CP)...

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Bibliographic Details
Published in:	Journal of Applied Animal Research Vol. 51; no. 1; pp. 193 - 203
Main Authors:	Andrade Ribeiro, Mariellen Cristine, Loures Guerra, Geisi, Cano Serafim, Camila, Nóbrega de Carvalho, Larissa, Galbeiro, Sandra, Vendrame, Pedro Rodolfo Siqueira, Monteiro do Carmo, João Pedro, Rodrigues Franconere, Erica Regina, Ferracini, Jéssica Geralda, do Prado, Ivanor Nunes, Prado Calixto, Odimári Pricila, Mizubuti, Ivone Yurika
Format:	Journal Article
Language:	English
Published:	Abingdon Taylor & Francis 31-12-2023 Taylor & Francis Ltd Taylor & Francis Group
Subjects:	Brachiaria brizantha Bromatological estimates Cellulose Dry matter Infrared spectroscopy NIRS Nutritional composition Particle size Prediction models Spectrum analysis
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Summary:	This study aimed to generate prediction models to estimate the chemical composition of fresh and dry Brachiaria brizantha cv. Piatã grass using near infrared spectroscopy (NIRS). Chemical analyses of 249 samples were performed to determine oven-dried sample (ODS), dry matter (DM), crude protein (CP), neutral detergent fibre (NDF), acid detergent fibre (ADF), acid detergent lignin (ADL), cellulose (CEL) and total digestible nutrients (TDN). The samples were scanned in an NIRS spectrometer and different percentages were used to compose and develop the models (100% fresh; 100% dry; 25% fresh:75% dry; 50% fresh:50% dry and 75% fresh:25% dry). The purpose of these mixed models is to know if it is possible to obtain reliable predictions from fresh samples in a database that contains dry samples. The calibration models were developed using modified partial least squares (MPLS) and evaluated by statistical parameters, including coefficient of determination (R²) and residual predictive deviation (RPD). The model with 100% dry samples obtained the best results in R² and RPD validations, for CP (0.94; 3.98), NDF (0.92; 3,49) and TDN (0.90; 3.12). The 100% fresh samples produced the best R² results in ODS (0.83), CP (0.85), ADF (0.84) and ADL (0.83). A screening model was validated to predict the characteristics and components of the fresh samples. The model using 100% dry grass was suitable for predicting all the variables, except ODS, DM and CEL. Highlights Prediction models can be used for assessment of fresh forage, allowing producers to make quicker decisions, thereby saving time and money.
ISSN:	0971-2119 0974-1844
DOI:	10.1080/09712119.2023.2172022