Use of near infrared spectroscopy and multivariate calibration to determine the nutritional composition of ryegrass
DOI:
https://doi.org/10.52945/rac.v37i1.1781Keywords:
Calibration models, Bromatology, Spectroscopy.Abstract
In pasture-based production systems, knowledge of the nutritional potential of forages is of great importance for decision-making regarding food supplementation. However, analyses to assess nutritional composition are expensive and time-consuming. Near-infrared spectroscopy (NIRS) is a rapid and economical method used to quantify the levels of organic compounds in feeds. In the present work, multivariate calibration models were developed to predict crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), and in vitro digestibility of organic matter (DIVMO) of ryegrass using the NIRS technique, to be used in laboratory routine. The number of samples used varied from 294 to 390, depending on the component analyzed. The models were selected using the root mean square error of prediction (RMSEP), bias, deviation performance ratio (RPD), the error interval ratio (RER), and coefficients of determination (R2) obtained in external validation. For PB, FDN, FDA and DIVMO, the selected models presented the following values, respectively: R2 = 0.98; 0.94; 0.96; 0.91; RMSEP = 0.96; 1.35; 1.03; 1.58; bias = 0.21; 0.51; 0.70; 0.06; RPD = 6.33; 5.02; 4.08; 3.80; and RER = 26.33; 14.37; 14.61; 11.46. According to the goodness-of-fit measures obtained, the models developed for CP, NDF, and FDA can be used in the laboratory routine to analyze the nutritional values of ryegrass. The model developed for DIVMO can be used to screen ryegrass in evaluation, selection, and improvement studies.
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