Improving beef hamburger quality and fatty acid profiles through dietary manipulation and exploitation of fat depot heterogeneity
Bibliographic record
Abstract
BACKGROUND: Hamburger is the most consumed beef product in North America, but lacks in nutritional appeal due to its high fat content and high proportion of saturated fatty acids (SFA). Objectives of the present study were to improve the FA profiles of hamburgers made with perirenal fat (PRF) and subcutaneous fat (SCF) when feeding steers different diets along with examining differences in sensory attributes and oxidative stability. Diets included a control diet containing 70:30 red clover silage: barley based concentrate, a diet containing sunflower-seed (SS) substituted for barley, and diets containing SS with 15% wheat dried distillers' grain with solubles (DDGS-15) or 30% DDGS (DDGS-30). Hamburgers were made from triceps brachii and either PRF or SCF (80:20 w/w). RESULTS: Perirenal fat versus SCF hamburgers FA had 14.3% more (P <0.05) 18:0, 11.8% less cis (c)9-18:1 (P <0.05), and 1.82% more total trans (t)-18:1 mainly in the form of t11-18:1. During sensory evaluation, PRF versus SCF hamburgers had greater (P <0.05) mouth coating, but the difference was less than one panel unit. Examining effects of steer diet within PRF hamburgers, feeding the SS compared to the control diet increased (P <0.05) t-18:1 by 2.89% mainly in the form of t11-18:1, feeding DGGS-15 diet led to no further changes (P >0.05), but feeding DDGS-30 diet reduced the proportions of (P <0.05) of t-18:1 chiefly t11-18:1. Feeding SS and DDGS diets had small but significant (P <0.05) effects on hamburger sensory attributes and oxidative stability. CONCLUSIONS: Feeding high-forage diets including SS and 15% DDGS, and taking advantage of the FA heterogeneity between fat depots offers an opportunity to differentially enhance beef hamburgers with 18:2n-6 biohydrogenation products (i.e., t11-18:1) with potential human health benefits without compromising their sensory attributes and oxidative stability during retail display.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".