Effects of boning method and postmortem aging on meat quality characteristics of pork loin
Bibliographic record
Abstract
This work investigated the effects of boning method and postmortem aging on pork loin color, shearing value and sensory attributes. Two experiments were assigned. In Experiment I, 30 Chinese native black pigs were slaughtered and their carcasses were divided into three groups: (i) hot-boning: carcasses were fabricated within 45 min postmortem just after dressing; (ii) cold boning at 24 h: carcasses were fabricated after chilling at 0 degrees C for 24 h; (iii) cold boning at 36 h: carcasses were fabricated after chilling at 0 degrees C for 36 h. In Experiment II, right sides of the second group in Experiment I were used and primal cuts were vacuum packed and aged for 1 day, 8 days and 16 days. Pork loins (Longissimus lumborum) were used for color measurement, shearing test, and sensory evaluation. Among three boning methods, cold-boning at 36 h postmortem had the advantages of giving muscles a better color, the lowest cooking loss and cooked shearing value, and the highest sensory tenderness, juiciness, flavor and overall liking. Postmortem aging could improve pork quality characteristics, but it is not the fact that the longer aging time is, the better pork quality would be. Eight days may be enough to obtain an acceptable sensory attribute. These results are meaningful for pork processing and pork consumption.
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) | 0.000 | 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".