The influence of chilling rate and fat cover on beef quality
Bibliographic record
Abstract
The economic advantages of rapid carcass chilling include reduced cooling time, increased carcass processing rate, and decreased shrink and drip losses; however, it may also reduce subsequent meat tenderness, especially in lean carcasses. The present study examined the effects of blast chilling and electrical stimulation on quality of longissimus lumborum (LL) and semimembranosus muscles from carcasses with a wide range in backfat thickness (0 to 69 mm). Despite promoting a wide range in postmortem pH and temperature decline, backfat thickness had a limited effect on meat quality. There was a decrease in the proportion of carcasses with very high (>35 N cm–2) shear values and a tendency (P = 0.12) for the average shear force to decrease in the LL as backfat depth increased. Unexpectedly, leaner carcasses had less shrink loss than fatter carcasses under blast chill conditions. While blast chilling resulted in slightly darker, tougher meat, electrical stimulation was able to reduce these effects. Neither early postmortem pH nor temperature at 10 h was clearly associated with a pattern in shear force, although high pH10 was associated with tough steaks. Blast chilling in combination with electrical stimulation can be recommended to industry as a means to reduce chilling times and shrink losses while producing meat quality that is as good as, or slightly superior to conventionally cooled carcasses. Key words: Beef quality, blast chilling, electrical stimulation, fat thickness
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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 | 0.001 |
| 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) | 0.002 | 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".