The use of principal component analysis (PCA) to characterize beef

Area of Science:

• Animal Science
• Food Science
• Biometrics

Background:

• Understanding meat quality variations is crucial for the beef industry.
• Breed and genetic factors significantly influence meat chemical, physical, and sensory properties.

Purpose of the Study:

• To investigate the relationships between various meat quality parameters in different ethnic groups of young bulls.
• To utilize Principal Component Analysis (PCA) for a comprehensive assessment of meat quality.

Main Methods:

• Principal Component Analysis (PCA) was applied to 18 chemical, physical, and sensory variables.
• Data were collected from 79 young bulls across six ethnic groups, including hypertrophied and normal Piemontese, Friesian, and Belgian Blue breeds, as well as crossbreds.
• The first three principal components (PCs) explained 63% of the total variability.

Main Results:

• PC1 was strongly influenced by sensory characteristics, protein content, shear force, and cooking losses.
• PC2 was defined by hydroxyproline and ether extract content, hue, and lightness.
• PCA successfully grouped meats from hypertrophied animals (Piemontese, Belgian Blue) separately from normal Piemontese and Friesian, with crossbreds falling in between, though intra-group variability was observed.

Conclusions:

• PCA is a powerful tool for synthesizing complex meat quality data into a clear judgment.
• Distinct meat quality profiles exist between hypertrophied and normal cattle breeds.
• Genetic background significantly impacts the chemical and sensory attributes of beef longissimus thoracis et lumborum.