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 crossvalidation process. The results are evaluated in term of coefficient of determination, root-meansquare 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.
A total of 78 female and male Nero Siciliano pigs were used. Forty-one pigs were reared following the traditional management system, 37 pigs were reared in pens with a small outdoor paddock and fed to appetite using commercial rations according to the growing period. Both male and female pigs were castrated. All pigs were weighed and measured periodically. Body measurements included height at withers, chest girth, body length, width at shoulders and at rump. Age and body weight at slaughter ranged respectively from 371 to 572 days and from 79 to 113 kg. The carcasses were weighed and dissected into lean, fat and bone cuts. In the early and final stages indoor-pigs grew faster than those reared outdoors. Trends in body length were similar for the two rearing systems, for width at shoulders and rump, chest girth and height at withers, indoor pigs showed higher values than the outdoor ones. Carcasses of similar weight were longer in outdoor than in indoor pigs but the latter showed greater subcutaneous fat thickness. Outdoor pigs had the lowest dressing percentage and the highest percentage of lean cuts, such as shoulder and ham, but not of neck and loin. Sex did not significantly affect the analysed characteristics. #
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 (R 2), root mean square error (RMSE) and residual prediction deviation (RPD). Calibration results, using partial least square models, obtained a R 2 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 R 2 in calibration (0.876 and 0.863, respectively). Crossvalidation achieved both lower R 2 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.
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