Effect of iron bioavailability on muscle fibre types and on quality and oxidative stability of grain-fed veal
Bibliographic record
Abstract
The bioavailability of dietary iron can be reduced by using a chelator in the ration. In order to characterise the effects of supplementing veal calf rations with three doses of EDTA (0, 5 or 15 mg EDTA mg-1 Fe in the ration) for 8 or 18 wk, histochemical characteristics of the longissimus thoracis (LT) muscle and meat colour, composition, quality and stability were evaluated. Meat pH, colour, drip loss, cooking loss and shear forces were measured after a keeping period of 0, 7 and 14 d at 4°C, while lipid stability (thiobarbituric acid reactive substances) was measured on thawed ground meat stored for 1, 3 and 5 d at 4°C. EDTA treatments had no effect on dissectible tissue composition of the 9–11th rib section (P > 0.05) or on the chemical composition of the LT muscle (P > 0.05). EDTA dose had a quadratic effect (P < 0.05) on the proportion of slow-twitch, oxidative (SO) fibres and a negative, linear effect (P < 0.01) on the proportion of fast-twitch, oxidoglycolytic (FOG) fibres in the LT. The chelator was effective in enhancing paleness of veal, particularly with long-term administration of the highest dose (P < 0.05). This interaction, however, had a negative effect on shear force and also on oxidative stability of the meat during storage (P < 0.05). Key words: Veal quality, EDTA, fibre type, oxidative stability, TBARS
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".