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Detalle de: Predictive ability of the feeding system in Iberian pig by means of several analytical methods

Title Predictive ability of the feeding system in Iberian pig by means of several analytical methods
Abstract

The reliability of several analytical methods proposed to predict the feeding system received by Iberian pigs during the fattening period has been contrasted. Samples of subcutaneous adipose tissue were analyzed from 38 batches of pigs fattened in three seasons of montanera (acorn and pastures feeding). They corresponded to the four categories described in the Quality Standard for Iberian pig products: 251 samples of bellota, 164 of recebo, 197 of cebo de campo and 137 of cebo. To perform the study, the following analytical methods were used: fatty acid quantification, NIR, alpha and gamma tocopherol, chemical sensor, triacilglycerides, stable isotopes and neophytadiene. The laboratories received the samples without any information about the fattening system to which they belonged and remitted their predictions with respect to the above categories. The results showed a high percentage of accurate predictions of the methods when the fattening type corresponds to the extreme categories (bellota and cebo), however, the methods had difficulty in discerning between samples from a mixed feeding regime with acorn and feed (recebo) and problems when pigs are fattened with compound feeds including vegetable fats with high levels of oleic acid. Although a simplification into only two categories (cebo/campo and recebo/bellota) results in a success rate higher than 90% for some methods, the combination of two or three techniques with problematic samples allows for differentiating among the four categories with the same accuracy.

Apa Reference García Casco, J.M., Muñoz, M. & González, E. (2013). Predictive ability of the feeding system in Iberian pig by means of several analytical methods. Grasas y Aceites, 64(2), 191-200. 10.3989/gya.130812
Doi 10.3989/gya.130812
Publication Date 30 Jun 2013
Publication Type Article
Researcher María Muñoz Muñoz
Ontology
Research Groups
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