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A new methodological approach combining animal performances, nutritional value and sensory quality of meat

This work initially published in Meat Science (122, 163-172) in 2016 provides a new methodological approach to establish the links between different data sets, by using a variable clustering procedure instead of a classical individual clustering approach. Three data sets (animal performance, nutritional value of the meat, sensory quality traits of meat) were considered for a total of 97 variables for each of the 71 young bulls. Variables of each dataset were arranged into homogeneous synthetic variables. Then, the three pools of synthetic variables were analysed together to establish the links existing among the three data sets.
Classification showed no opposition between animal performances and nutritional value of meat, as it seemed possible to identify animals with a high butcher value and intramuscular fat relatively rich in polyunsaturated fatty acids. However, the classification indicated that animal performances were negatively correlated with sensory quality of meat. This method appeared to be a useful contribution to the management of animal breeding for an optimal trade-off between the three elements of the triptych (animal performances, nutritional value and sensory quality of meat).

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