Meat quality traits of Nellore bulls according to different degrees of backfat thickness: A multivariate approach

• Autor(a) principal: Baldassini, W. A.
• Data de Publicação: 2017
• Outros Autores: Chardulo, L. A.L. [UNESP], Silva, J. A.V. [UNESP], Malheiros, J. M. [UNESP], Dias, V. A.D. [UNESP], Espigolan, R. [UNESP], Baldi, F. S. [UNESP], Albuquerque, L. G. [UNESP], Fernandes, T. T., Padilha, P. M. [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1071/AN15120; http://hdl.handle.net/11449/178561
• topic: beef cattle; carcass; meat aspect; multivariate analysis; Zebu genotype.

Subcutaneous fat deposition measured as backfat thickness (BFT) increases protection for the bovine carcass during cooling, conferring to BFT an important characteristic for the meat industry. To study the influence of BFT on meat quality traits of Nellore bulls (Bos indicus), data from 1652 animals aged 20-24 months in feedlot finishing were used. The principal component analysis (PCA) was performed to characterise meat quality variables in longissimus thoracis muscle. Measurements comprised the rib eye area, BFT, marbling, shear force, myofibril fragmentation index, cooking losses, intramuscular lipid content and colour (lightness, yellowness, redness, chromaticity and hue). Considering BFT as a separation criterion, the K-means cluster analysis was applied to classify beef samples. The first four PC explained roughly 66% of total variability and meat colour (yellowness and chromaticity) was more effective to define the first PC. Tenderness or toughness (shear force and cooking losses) and fatness (BFT and intramuscular lipid content) were more effective to define the second and third PC, respectively. Three BFT groups were formed and projected in the gradient defined by PC 2 and PC 3. BFT means in the clusters were 10.82 ± 3.19 (I), 5.03 ± 1.01 (II) and 2.54 ± 0.63 (III) mm with 185, 947 and 520 animals in each group, respectively. The projection of I, II and III in the gradient allowed to distinguish fatness between beef samples and tenderness between I and III. Additionally, 57.32% of animals (Group II) were placed between the two previous groups. Beef samples with higher values of shear force and cooking losses (tough meat) showed lower BFT and myofibril fragmentation index values, possibly due to fibre shortening. PCA and K-means cluster analysis presented as interesting multivariate techniques to identify Nellore bulls regarding meat quality as some of the traits used in the study are difficult to measure. The three-cluster solution represented the main biological type of Nellore bulls finished on feedlot in Brazil showing that only 11.2% of beef samples (Cluster I) can be considered tender. This information can be useful for breeding programs of Nellore bulls. In this study, Cluster I shows optimal beef quality (SF ≤ 4.52 ± 1.17 kg) with better marbling level and less cooking losses. Nellore cattle producers should target BFT at least 5.00 mm to prevent fibre shortening. However, the only condition which provides optimal beef tenderness (i.e. shear force values lower than 4.9 kg) was found in Cluster I. The BFT does not seem to be a suitable characteristic for the selection of animals to improve tenderness due the weak relationship between BFT and shear force.