Vascular rinsing and chilling carcasses improves meat quality and food safety: a review
Bibliographic record
Abstract
technology (RCT) entails rinsing the vasculature using a chilled isotonic solution (3°C; 98.5% water and a blend of dextrose, maltose, and sodium phosphates) to rinse out the residual blood from the carcass. Infusion of pre-chilled solutions into intact animal carcasses immediately upon exsanguination is advantageous in terms of lowering the internal muscle temperature and accelerating chilling. This technology is primarily used for purposes of effective blood removal, favorable pH decline, and efficient carcass chilling, all of which improve meat quality and safety. Although RCT solution contains some substrates, the pre-rigor muscle is still physiologically active at the time of early postmortem and vascular rinsing. Consequently, these substrates are fully metabolized by the muscle, leaving no detectable residues in meat. The technology has been commercially approved and in continuous use since 2000 in the United States and since 1997 in Australia. As of January 2022, 23 plants have implemented RCT among the 5 countries (Australia, US, Canada, New Zealand, and Japan) that have evaluated and approved RCT. All plants are operating under sound Sanitation Standard Operation Procedures (SSOP) and a sound Hazard Analysis Critical Control Point (HACCP) program. No food safety issues have been reported associated with the use of this technology. RCT has been adapted by the meat industry to improve product safety and meat quality while improving economic performance. Therefore, this review summarizes highlights of how RCT technically works on a variety of animal types (beef, bison, pork, and lamb).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".