Three muscles (Longissimus thoracis, Semimembranosus, Biceps femoris) of 40 young bulls from 3 breeds were used to quantify structural characteristics of bovine connective tissue by image analysis, with both macroscopic and microscopic approaches. Collagen and proteoglycan content was also investigated. Perimysium occupied a greater area (8 vs 6%), and was wider (42 vs 2 μm) and shorter per unit area (1.9 vs 30 mm mm(-2)) than endomysium. Perimysium and endomysium from Longissimus were thinner, less ramified than in Biceps. Longissimus showed less total collagen and cross-linking and more proteoglycans (P<0.0001) than Biceps muscle. Blond d'Aquitaine perimysium occupied less area, was more ramified and muscles contained less collagen, cross-linking and more proteoglycans than Angus (P<0.001). Limousin was intermediate. High proteoglycan content in muscle containing less total collagen suggested a complementarity between these molecules. They might influence mechanical properties of intramuscular connective tissue. This was especially true given that proteoglycans and total collagen were negatively and positively linked with structural parameters, respectively.

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The objective of this study was to determine the potential of multispectral imaging (MSI) data recorded in the visible and near infrared electromagnetic regions to predict the structural features of intramuscular connective tissue, the proportion of intramuscular fat (IMF), and some characteristic parameters of muscle fibers involved in beef sensory quality. In order to do this, samples from three muscles (Longissimus thoracis, Semimembranosus and Biceps femoris) of animals belonging to three breeds (Aberdeen Angus, Limousine, and Blonde d’Aquitaine) were used (120 samples). After the acquisition of images by MSI and segmentation of their morphological parameters, a back propagation artificial neural network (ANN) model coupled with partial least squares was applied to predict the muscular parameters cited above. The results presented a high accuracy and are promising (R2 test > 0.90) for practical applications. For example, considering the prediction of IMF, the regression model giving the best ANN model exhibited R2P = 0.99 and RMSEP = 0.103 g × 100 g−1 DM.