Predicting acorn-grass weight gain index using non-destructive Near Infrared Spectroscopy in order to classify Iberian pig carcasses according to feeding regime

摘要

Predicting Acorn-Grass Weight Gain Index using non-destructive Near Infrared Spectroscopy in order to classify Iberian pig carcasses according to feeding regime The classification of Iberian pig carcasses into different commercial categories according to feeding regime was evaluated by means of a non-destructive analysis of the subcutaneous adipose tissue using Near Infrared Spectroscopy (NIRS). A quantitative approach was used to predict the Acorn-Grass Weight Gain Index (AGWGI), and a set of criteria was established for commercial classification purposes. A total of 719 animals belonging to various batches, reflecting a wide range of feeding regimes, production systems and years, were analyzed with a view to developing and evaluating quantitative NIRS models. Results for the external validation of these models indicate that NIRS made clear differentiation of batches as a function of three feeding regimes possible with high accuracy (Acorn, Recebo and Feed), on the basis of the mean representative spectra of each batch. Moreover, individual analysis of the animals showed a broad consensus between field inspection information and the classification based on the AGWGI NIRS prediction, especially for extreme categories (Acorn and Feed).