Relationships between Intramuscular Fat, Meat Quality and Collagen Characteristics in Bovine Longissimus Thoracis
Notice bibliographique
Résumé
ObjectivesThe presence of intramuscular fat within beef muscle has been associated with decreased meat toughness and studies suggest that intramuscular connective tissue (ICT) and myofibrils contribute to cooked beef toughness. The objective of this study was to determine the variation in collagen characteristics among beef quality grades and their relationship with meat toughness.Materials and MethodsTwelve longissimus thoracis (LT, rib eye) muscles from each of the Canada quality grades (Prime, AAA, AA, and A equivalent to USDA Prime, Choice, Select and Standard) were obtained direct from abattoir within 3 d postmortem for a total of 48 LT. Three steaks were removed followed by 14 d of ageing for 1) color, pH, proximate analysis; 2) cooking loss and Warner-Bratzler shear force; and 3) drip loss. The remaining muscle was frozen at –20°C until thawed, cut into meat cubes, mixed to homogeneity and divided into three fractions. Fraction A was analyzed as whole muscle, Fraction B was used for isolation of ICT with alkali extraction and Fraction C was for the isolation of ICT with a salt buffer extraction. Fraction A was lyophilized and analyzed for proximate composition, total collagen concentration and collagen heat solubility, while ICT isolated by alkali and salt were compared for total collagen and Ehrlich chromagen (EC) concentration. Data were analyzed using Statistical Analysis Software (SAS Inst. Inc., Cary, NC). The effects of grade on beef quality and collagen characteristics were analyzed by one way analysis of variance with grade as the sole source of variation, while the isolation and comparison of ICT used grade and isolation methods and their interaction as sources of variation with significance at P < 0.05. Mean differences between sources of variation were determined by Student-Newman-Kuel multiple range test and Pearson correlations and partial least square regression estimated linear relationships between the measurements (P < 0.05).ResultsGrade had no effect on pH, cook time and drip loss. Mean shear force value was lower for LT from Canada Prime (37.5 kg) and AAA (33.6 kg) than LT from Canada A (45.0 kg) and AA (50.6 kg; P = 0.001). Canada Prime LT had lower cooking loss than other quality grades (P = 0.0204) but the highest intramuscular fat content (11.57%), followed by LT from Canada AAA (4.62%), AA (3.11%), and A (2.92%) (P < 0.0001). Strong correlations were found between fat and moisture (r = –0.84) and protein (r = –0.65). Prime LT showed a higher collagen content (2.49 mg/g fresh muscle) than LT from AA (2.05 mg/g fresh muscle), AAA (1.99 mg/g fresh muscle) and A (1.92 mg/g fresh muscle; P = 0.0057). Grade had no effect on collagen heat solubility (P = 0.4945); however, solubility increased with total collagen content (r = –0.47, P = 0.000745). Salt extraction yielded more ICT (14.9% of muscle) than alkali (8.53%) with Prime LT having the greatest proportion of perimysium (12.41%; P = 0.0057). Alkali-extracted ICT contained more collagen (2.94 mg/g fresh muscle) and EC concentration (0.38 mol/mol collagen) than salt-extracted ICT collagen (2.38 mg/g fresh muscle, EC concentration 0.29 mol/mol collagen) with no effect of grade.ConclusionResults showed that Prime and AAA LT with inherent increased intramuscular fat content produce a tenderer product than Canada AA and A LT despite increased collagen content.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».