Near Infrared Spectroscopy technology for prediction of chemical composition of natural fresh pastures

This study evaluates the potential of Fourier-Transform Near Infrared Spectroscopy (FT-NIRS) to estimate the chemical composition of fresh natural pastures of Tuscany without previous drying and grinding. Chemical composition of herbage samples is determined by applying usual chemistry. FT-NIRS calibration and cross-validation were developed applying spectra pre-treatment and two statistical models: partial least square regression and principal component regression. The results are evaluated in terms of coefficients of determination (R2), root mean square error (RMSE) and residual prediction deviation (RPD). Calibration results, using partial least square models, obtained a R2 in calibration greater than 0.95 for dry matter and crude protein, intermediate values (>0.75) for the fibre fraction and lower results for ash and crude fat (<0.75). The chemometric analysis shows lower results using principal component regression than partial least square models, although dry matter and acid detergent fibre obtained relatively high R2 in calibration (0.876 and 0.863, respectively). Crossvalidation achieved both lower R2 and higher errors than calibration. Despite the wide variability of the data set, the results suggest that coupling FT-NIRS with partial least squares analysis allows us to estimate some chemical parameters of natural pastures, while the use of principal component regression models needs further evaluation.