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.

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.

Autochthonous pig breeds provide products of differentiated quality, among which quality control is difficult to perform and insufficient for current market requirements. The present research evaluates the predictive ability of near-infrared (NIR) spectroscopy, combined with chemometric methods as a rapid and affordable tool to assure traceability and quality control. Thus, NIR technology was assessed for intact and minced muscle Longissimus thoracis et lumborum samples collected from 12 European autochthonous pig breeds for the quantification of lipid content and fatty acid composition. Different tests were performed using different numbers of samples for calibration and validation. The best predictive ability was found using minced presentation and set with 80% of the samples for the calibration and the remaining 20% for the external validation test for the following traits: lipid content and saturated and polyunsaturated fatty acids, which attained both the highest determination coefficients (0.89, 0.61, and 0.65, respectively) and the lowest root mean square errors in external validation (0.62, 1.82, and 1.36, respectively). Lower predictive ability was observed for intact muscles. These results could contribute to improve the management of autochthonous breeds and to ensure quality of their products by traditional meat industry chains.

Near infrared spectroscopy (NIRS) can be useful in order to determine meat quality traits as a rapid and nondestructive technique. The aim of the present study is to assess the accuracy of NIRS technique to determine meat quality traits on Longissimus thoracis et lumborum muscle of open-air free-range Iberian pig (n = 287) both in intact and minced samples. Traits were measured by instrumental-chemical techniques: colour (L*, a* and b*), myoglobin content, centrifuge force water loss and texture: shear force and texture profile analysis (TPA: hardness, cohesiveness, springiness, chewiness). Calibration models between instrumental-chemical measures and NIRS spectral data were developed employing partial least square regressions (PLS). The samples were split in two random datasets (80 % in training set, 20 % in external validation set). An internal full cross-validation method was applied. Results were evaluated in terms of coefficient of determination (R 2 ), root-mean-square error (RMSE), residual prediction deviation (RPD) and range error ratio (RER). Full spectral range was used to develop mathematical equations. As regard external validation procedure, the highest coefficients of determination (R 2 p) in intact loin samples were achieved for hardness, redness (a*) and yellowness (b*) (0.70.7. The models developed suggest the ability of NIRS for quantitative prediction of shear force and for a rough classification of colour parameters L*, a*, b* in minced samples as well as hardness in intact samples.

This study aimed to compare the grazing behaviour of two pig genetic types, Cinta Senese (CS) and its crossbreed with Large White (LW x CS), farmed in natural rearing system in Tuscany, as influenced by diurnal time slot and by the season of the year. In situ direct observations on two herds of grazing pigs were conducted during daylight hours for five consecutive days and repeated bimonthly for a period of one year. The observations were grouped into three diurnal time slots and the relative frequencies of the main activities were obtained. Data were subjected to ANOVA with genetic type, diurnal time slot and month as fixed effects. The results, valid for the genotypes and the specific rearing system considered, highlighted that pigs displayed species-specific foraging behaviours for a long time and showed very low levels of other behaviours as aggressive or stereotypes. Both genotypes spent about 72% of the daylight hours for feeding, mainly dedicated to grass feeding. Nevertheless, CS pigs devoted less time to grazing pasture respect to LWxCS. Throughout the months, grazing was preferred to rooting, especially when herbaceous resources were more available. The proportion of diurnal time dedicated to feeding by pigs was reduced with the hot season, but CS seems more affected compared to LWxCS. HIGHLIGHTS In extensive rearing systems, Cinta Senese pig and its crossbreeds employed most of the diurnal time in grass feeding. Cinta Senese pigs devoted less time to grazing pasture respect to LWxCS, especially during hot months and the central hours of the day.