Predicting beef cuts composition, fatty acids and meat quality characteristics by spiral computed tomography

Area of Science:

• Animal Science
• Food Science
• Imaging Technology

Background:

• Accurate prediction of beef cut composition and quality is crucial for the meat industry.
• Traditional methods like dissection and chemical analysis are destructive, time-consuming, and costly.
• Non-invasive imaging techniques offer a potential alternative for rapid and cost-effective meat quality assessment.

Purpose of the Study:

• To investigate the potential of X-ray computed tomography (CT) combined with partial least square regression (PLSR) for predicting beef cut composition and quality traits.
• To evaluate the accuracy of CT-PLSR in predicting subcutaneous fat, intermuscular fat, total fat, muscle content, intramuscular fat (IMF), and fatty acid profiles in different beef breeds.
• To assess the feasibility of using CT for non-destructive prediction of technological and sensory meat quality traits.

Main Methods:

• Spiral CT scanning of sirloin cuts from Aberdeen Angus (AAx) and Limousin (LIMx) cattle (n=88 and n=106, respectively).
• Dissection and analysis of scanned cuts for technological and sensory parameters, IMF content, and fatty acid composition.
• Development and cross-validation of CT-PLSR models to predict various meat composition and quality attributes.

Main Results:

• CT-PLSR achieved high accuracy in predicting subcutaneous fat (R2=0.92-0.94), intermuscular fat (R2=0.81-0.86), total fat (R2=0.89-0.93), and muscle content (R2=0.97-0.99).
• Accurate predictions were obtained for fatty acid profiles (R2=0.61-0.75) and IMF content (R2=0.71-0.76) in both breeds.
• Low to very low accuracies (R2=0.01-0.26) were observed for technological and sensory traits, indicating limitations in predicting these specific parameters.

Conclusions:

• X-ray CT combined with PLSR is a highly accurate and non-destructive method for predicting beef cut composition (fat, muscle) and IMF content.
• This imaging approach provides a cost-effective alternative to traditional methods for assessing key meat quality components.
• Further research may be needed to improve CT's accuracy in predicting complex technological and sensory meat quality traits.