Use of FT-NIRS for determination of chemical components and nutritional value of natural pasture

This study evaluates the potential of Fourier transformation near-infrared reflectance spectroscopy to estimate the nutritional value and the chemical composition of natural pastures. Variability from all samples of pastures available is considered in order to assess the applicability of the calibration models in the future predictions. Chemical components (dry matter, crude protein, ash, ether extract, crude fibre, fibrous fractions) of grass samples were determined by applying official methods, and milk and meat forage units were calculated. Calibration and validation models were developed between chemical–nutritional parameters and NIRS spectral data using partial least square regression (PLS). The capacity of methods has been achieved using two validation approaches: the first using an independent dataset for prediction and the second by cross-validation process. The results are evaluated in term of coefficient of determination, root-mean-square error and residual prediction deviation. Despite the wide variability of the data set, the results of FT-NIRS have been able to estimate the chemical composition of natural and naturalised pasture with good accuracy and precision, while for nutritional value parameters, a further evaluation may be useful.