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1.
Meat Sci ; 79(3): 458-62, 2008 Jul.
Article in English | MEDLINE | ID: mdl-22062906

ABSTRACT

This report describes the meat quality of two INTA hybrids (hybrid females) sired by Duroc (D) or Yorkshire (Y) boars and a third one from PIC (S), a cross of females C22 to 412 boars. Starting at 30kg live weight, 18 barrows and 18 gilts of each genotype were kept in identical conditions until slaughtered at 110kg. Longissimus dorsi muscles were analyzed. Means differed significantly (P<0.05) for drip loss (higher in S); tenderness (more tender in D), water holding capacity (higher in Y); cooking loss (higher in Y); colour parameter L(∗) (lower in D) and b(∗) (higher in S) and intramuscular fat content (higher in D). As a result of sensory analysis, it was found that D was the most tender and juicy. There were few sex effects and no genotype-sex interactions. Distinct differences in meat quality between hybrids do exist, with D superior, S the worst, and Y intermediate.

2.
Meat Sci ; 79(3): 611-3, 2008 Jul.
Article in English | MEDLINE | ID: mdl-22062924

ABSTRACT

Rapid evolution of pork production in Argentina requires new calibrations for predicting carcass lean meat percentage with the Fat-O-Meater (FOM) and Hennessy Grading Probe (HGP), first adopted in 1995. The second objective was to unify the lean percentage units with those applied by the European Union. Carcasses of 59 gilts and 56 barrows from different environments and breeds were tested. Carcass weights were from 65 to 117kg, and lean content was from 38% to 62%. Predicting lean content by multiple regression equations, the coefficients of determination R(2) were 0.801 and 0.794 for the FOM and HGP equations, and the residual standard deviations (RSD) were 2.40% and 2.45%, respectively. Both instruments had the same precision and were accurate enough to be adopted in national carcass grading classification. Hot carcass weight was not selected as a significant variable. The same prediction equations could be used for gilts and barrows. Quadratic terms did not improve predictions.

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