Effects of broiler chilling methods on the occurrence of pale, soft, exudative (PSE) meat and comparison of detection methods for PSE meat using traditional and Nix colorimeters
Bibliographic record
Abstract
The aims of this study were to i) estimate the occurrence of pale, soft, and exudative (PSE) meat in modern commercial Ontario broiler flocks, ii) determine the effects of the chilling method (water vs. air) on PSE meat, and iii) investigate a new inexpensive colorimeter (10% of the price of traditional color meters), the Nix Color Sensor, as an objective color measurement of chicken meat. Between June 2019 to March 2020, a total of 17 different broiler flocks were processed. The color of 1,700 boneless skinless Pectoralis major muscles was randomly measured (100/flock), where 255 samples were also measured for pH, water-holding capacity (WHC), cooking loss, and penetration force. In addition, a traditional Minolta colorimeter was used to measure random 95 samples from a single water-chilled flock and subsequently compared the values obtained with the Nix Color Sensor. Strong correlations of L* values (rho = 0.75; P < 0.001), a* values (rho = 0.72; P < 0.001), and b* values (rho = 0.80; P < 0.001) were observed. When an L* value of 43 was used as the cut-off for the Nix, 12.5% of fillets were classified as PSE meat. Statistical differences (P < 0.05) were observed between the air and water-chill methods for L*, pH, and WHC. However, there were no significant differences observed between the 2 methods for cooking loss and penetration force values. The study indicated that PSE meat is still a challenge in Ontario broilers, and that the L*, pH, and WHC of breast meat (all indicate meat quality) are affected by the chilling method. In addition, the Nix was found to be an affordable, objective, and convenient sensor for measuring chicken meat color.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".