Natural additives as a source of antioxidants improve lipid oxidation, antioxidant activity, and shelf-life of beef
Bibliographic record
Abstract
Forty young bulls were finished in feedlot with diets that contained different dosages of a combination of several natural additives (NA), i.e., clove essential oil, cashew and castor oil, and rumenprotected eugenol, vanillin, and thymol. The animals were randomized in five diets containing four different inclusion levels of NA and a control diet (n = 8 animals per treatment): basal diet without NA (CON); NA15 = 1500 mg/day; NA30 = 3000 mg/day; NA45 = 4500 mg/day, and NA60 = basal diet with 6000 mg/day of the natural additives blend. Colour, antioxidant activity (DPPH, ABTS and FRAP assays), lipid oxidation, and visual acceptability were evaluated through display (until 14 d in vacuum or film packages). Both factors (diet and display) affected all parameters evaluated. The highest dosage, NA60, was able to improve the antioxidant potential, decreasing beef oxidation to produce a higher visual acceptability. The results of this research provide evidence that NA included in the diet of beef cattle can improve overall meat quality and extend shelf-life, thus, providing higher visual acceptability through the colour perception of consumers.
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.
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".