Beef-on-dairy: Meat quality of veal and prediction of intramuscular fat using the Q-FOM™ Beef camera at the 5th-6th thoracic vertebra.
Meat Sci
; 213: 109503, 2024 Jul.
Article
in En
| MEDLINE
| ID: mdl-38579510
ABSTRACT
This study aims to describe the meat quality of young Holstein (HOL) beef-on-dairy heifers and bulls sired by Angus (ANG, n = 109), Charolais (CHA, n = 101) and Danish Blue (DBL, n = 127), and to investigate the performance of the handheld vision-based Q-FOM™ Beef camera in predicting the intramuscular fat concentration (IMF%) in M. longissimus thoracis from carcasses quartered at the 5th-6th thoracic vertebra. The results showed significant differences between crossbreeds and sexes on carcass characteristics and meat quality. DBL × HOL had the highest EUROP conformation scores, whereas ANG × HOL had darker meat with higher IMF% (3.52%) compared to CHA × HOL (2.99%) and DBL × HOL (2.51%). Bulls had higher EUROP conformation scores than heifers, and heifers had higher IMF% (3.70%) than bulls (2.31%). These findings indicate the potential for producing high-quality meat from beef-on-dairy heifers and ANG bulls. The IMF% prediction model for Q-FOM performed well with R2 = 0.91 and root mean squared error of cross validation, RMSECV = 1.33%. The performance of the prediction model on the beef-on-dairy veal subsample ranging from 0.9 to 7.4% IMF had lower accuracy (R2 = 0.48) and the prediction error (RMSEveal) was 1.00%. When grouping beef-on-dairy veal carcasses into three IMF% classes (2.5% IMF bins), 62.6% of the carcasses were accurately predicted. Furthermore, Q-FOM IMF% predictions and chemically determined IMF% were similar for each combination of sex and crossbreed, revealing a potential of Q-FOM IMF% predictions to be used in breeding, when aiming for higher meat quality.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Thoracic Vertebrae
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Adipose Tissue
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Muscle, Skeletal
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Red Meat
Limits:
Animals
Language:
En
Journal:
Meat Sci
Journal subject:
CIENCIAS DA NUTRICAO
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: