A MODIFIED MEAT JUICE CONTAINER (EZ‐DRIPLOSS) PROCEDURE FOR A MORE RELIABLE ASSESSMENT OF DRIP LOSS AND RELATED QUALITY CHANGES IN PORK MEAT
Bibliographic record
Abstract
ABSTRACT The objective of this study was to assess the efficiency of a modified sample handling technique within the “meat juice container (EZ‐DripLoss)” method for drip loss (DL) assessment. To this end, samples were stored for 48 h and dabbed prior to recording sample weight loss after storage instead of being stored for 24 h and nondabbed as recommended in the conventional EZ‐DripLoss method. Ninety‐six pork m. longissimus samples were used. DL value for dabbed samples was higher (P < 0.0001) at both storage time compared with nondabbed samples. Highest correlations were observed between DL and electrical conductivity (r = 0.76) and the lowest with pHu (r = −0.37) in dabbed samples at 48‐h storage time. Highest correlations between DL and subjective color and light reflectance value (r = −0.59 and 0.67, respectively) were found in dabbed samples after 24‐h storage. Longer storage time and sample dabbing improved the reliability of the EZ‐DripLoss methodology for the DL assessment and overall pork quality evaluation. PRACTICAL APPLICATIONS The EZ‐DripLoss method is included in the routine measurements for pork quality evaluation in several plants in North America. However, the official methodology is questionable due to the short storage time and inaccurate sample handling. Based on the higher correlations with other important quality traits and the more accurate distribution of ioins through the quality classes according to the drip loss value, the modified EZ‐DripLoss method proposed in this paper will allow the users to implement an effective drip loss assessment and overall pork quality prediction both at the cutting plant and at the laboratory.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".