Effects of combined wet- and dry-aging techniques on the physicochemical and sensory attributes of beef ribeye steaks from grain-fed crossbred Zebu steers
Bibliographic record
Abstract
The effects of dry- and wet-aging combinations on the sensory and physicochemical attributes of beef ribeye steaks were investigated. Paired beef ribs (n = 16) from eight grain-fed crossbred Zebu steers (n = 16) were divided into unaged, 28 d wet, 28 d dry, 14 d wet + 14 d dry, and 14 d dry + 14 d wet. Aging was conducted in a chamber at 2 °C with 73% relative humidity and without airflow. Dry-aged and combined-aged products had greater percentages of total loss compared with wet-aged products during the aging and fabrication, resulting in lower total saleable product (P < 0.01). All aging treatments presented a brighter and more vivid red color than unaged samples (P < 0.05). Regarding shear force, aged samples presented lower (P < 0.05) values when compared with unaged samples, but no significant differences were observed among aging treatments (P > 0.05). In addition, all aged samples presented higher proportion of tender steaks (>87%; P < 0.01). In this study, trained panelists were unable to identify differences among aging treatments for any of the palatability attributes evaluated (P > 0.05). The combination of both aging techniques did not offer any advantage, and the wet-aging process alone appears to be the most efficient strategy for the Brazilian food service to maximize palatability characteristics of beef.
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".