Identification of relationship between pork colour and physicochemical traits in American Berkshire by canonical correlation analyses
Bibliographic record
Abstract
This study was carried out to predict the relationship between the colour and physicochemical traits in pork by using canonical correlation analysis. The variables of pork colour traits were lightness (L*), redness (a*) and yellowness (b*), whereas the variables of physicochemical traits were post-mortem pH24 h, water-holding capacity (WHC), collagen content, fat content, moisture content, protein content, drip loss, cooking loss and shear force. The canonical correlation coefficient (0.819) between the first pair of canonical variates, V1 and W1, was significant (P < 0.01). According to cross loadings, drip loss, cooking loss and fat content provided the relatively high positive correlations with the variates of colour traits (V1), while pH24 h, WHC and moisture content displayed negative relationships with the variates. Otherwise, L* and a* strongly contribute to the variates of physicochemical traits (W1). In addition, a redundancy index of 0.256 suggests that 25.6% of the variance in V1 is explained by W1. Therefore, in order to obtain the reddish pink colour in pork that is preferred by consumers depending on the relationships between the pork quality traits, we suggest that producer leads to the proper maintenance of post-mortem pH24 h, higher WHC, lower drip loss and cooking loss in pork.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".