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Record W3013908496 · doi:10.221751/rmc2018.038

Correlation of Beef Longissimus Thoracis Quality and Composition with Semimembranosus Quality and Composition

2018· article· en· W3013908496 on OpenAlex
B. M. Bohrer, Liping Wang, Shiqi Huang, S. Chalupa-Krebzdak, S. M. Vasquez Mejia

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueMeat and Muscle Biology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsLongissimus ThoracisLongissimusFood scienceChemistryHomogeneousMathematicsAnimal scienceComposition (language)TendernessBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.874
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.311
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it