RESUMO
In this study, we genetically characterized the Uruguayan pig breed Pampa Rocha. Genetic variability was assessed by analyzing a panel of 25 microsatellite markers from a sample of 39 individuals. Pampa Rocha pigs showed high genetic variability with observed and expected heterozygosities of 0.583 and 0.603, respectively. The mean number of alleles was 5.72. Twenty-four markers were polymorphic, with 95.8% of them in Hardy Weinberg equilibrium. The level of endogamy was low (FIS = 0.0475). A factorial analysis of correspondence was used to assess the genetic differences between Pampa Rocha and other pig breeds; genetic distances were calculated, and a tree was designed to reflect the distance matrix. Individuals were also allocated into clusters. This analysis showed that the Pampa Rocha breed was separated from the other breeds along the first and second axes. The neighbour-joining tree generated by the genetic distances DA showed clustering of Pampa Rocha with the Meishan breed. The allocation of individuals to clusters showed a clear separation of Pampa Rocha pigs. These results provide insights into the genetic variability of Pampa Rocha pigs and indicate that this breed is a well-defined genetic entity.
RESUMO
Visible (Vis) and near-infrared reflectance (NIR) spectroscopy combined with chemometrics was explored as a tool to trace muscles from autochthonous and crossbreed pigs from Uruguay. Muscles were sourced from two breeds, namely, the Pampa-Rocha (PR) and the Pampa-Rocha x Duroc (PRxD) crossbreed. Minced muscles were scanned in the Vis and NIR regions (400-2,500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), discriminant partial least square regression (DPLS), linear discriminant analysis (LDA) based on PCA scores and soft independent modelling of class analogy (SIMCA) were used to identify the origin of the muscles based on Vis and NIR data. Full cross validation was used as validation method when classification models were developed. DPLS correctly classified 87% of PR and 78% of PRxD muscle samples. LDA calibration models correctly classified 87 and 67% of muscles as PR and PRxD, respectively. SIMCA correctly classified 100% of PR muscles. The results demonstrated the usefulness of Vis and NIR spectra combined with chemometrics as rapid method for authentication and identification of muscles according to the breed of pig.
