Correlation of Beef Longissimus Thoracis Quality and Composition with Semimembranosus Quality and Composition
Bibliographic record
Abstract
ObjectivesPrevious research has investigated the quality of individual muscles and separate cuts within a beef carcass. However, few studies have examined the relationship between the quality of different beef muscles. Understanding this relationship could determine the necessity of certain meat quality analyses to assess the value of beef carcasses. Thus, the objective of this study was to examine the correlation between the quality and composition of beef semimembranosus (SM) and longissimus thoracis (LT) muscles.Materials and MethodsAt 4 d post-mortem, beef inside round (IMPS #168) and rib (IMPS#107) cuts from the right side of steer carcasses (n = 63) were collected from a commercial processing facility and delivered to the University of Guelph Meat Science Laboratory. At 5 d post-mortem, pH and objective color (L*, a*, b*, chroma, hue; measured with a Minolta CR-400) were collected for the SM muscle within the round section. At 6 d post-mortem, pH and objective color were collected for the LT muscle. Duplicate 5 to 6 g homogeneous samples from each the SM and LT samples were analyzed for moisture content by forced-air convection oven drying at 100°C for at least 24 h (Method 950.46, AOAC, 2000). Lipid content of the dried duplicate samples were determined by soxhlet extraction with petroleum ether, followed by at least 24 h of oven drying at 100°C. PROC CORR of SAS 9.4 was used to calculate the Pearson correlation coefficients for all parameters. Correlation coefficients were regarded as weak at r < |0.35|, moderate at |0.36| ≤ r ≤ |0.67|, and strong at r ≥ |0.68|. PROC REG of SAS was used to create linear regression models between parameters that had meaningful relationships. PROC GPLOT of SAS (SAS Inst. Inc., Cary, NC) was used to create scatter plots to allow better visualization of the correlations between meaningful parameters.ResultsThere was a weak and statistically insignificant correlation between LT and SM pH values (r = 0.20, P = 0.11); as well as, a weak correlation between LT and SM hue (r = 0.24, P = 0.06). There was a slightly stronger, positive linear correlation between LT and SM L* (lightness) values (r = 0.34, P = 0.01). Moisture content of LT and SM had a significant, moderately correlated linear relationship (r = 0.66, P < 0.0001). Similar to moisture, the correlation between LT and SM lipid content was moderately correlated (r = 0.67, P < 0.0001). All other LT and SM quality parameters were very weakly correlated (r = -0.06 to 0.01), thus no further statistical analyses were performed.ConclusionResults from this study showed that in general meat quality attributes from beef longissimus thoracis and semimembranosus muscles were weakly correlated with one another, and composition was only moderately correlated. Thus, analyses measuring individual beef muscle quality are required to achieve more accurate results and more meaningful assumptions of eating experience. Future studies could examine the relationship of the remaining beef primal cuts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".