The synergistic effect of ribose, carnosine, and ascorbic acid on the sensory and physico‐chemical characteristics of minced bison meat
Bibliographic record
Abstract
Ingredients such as ascorbic acid used to preserve redness of the raw meat, and carnosine and ribose used for flavor improvement have been incorporated into minced meats to increase consumer acceptance. The objective of this study was to investigate the possible synergistic effect of ascorbic acid, carnosine, and ribose on the sensory and physico-chemical characteristics of minced bison meat. Samples included control (Co) ±1% carnosine (C), 0.1% ascorbic acid (A), 2% ribose (R) (w/w), and combinations of RC, RA, RCA in the same concentrations as the single ingredient samples. A trained sensory panel (n = 7) measured the intensity of the aromas and flavors of salty, sour, beef, and liver of the bison patties. A consumer acceptance panel (n = 59) evaluated color, aroma, flavor, texture, and overall acceptability of the patties. Hunterlab colorimetry, shear force, cook loss, and drip loss percentage were measured on the cooked patties, and color and pH on the raw patties. The sample containing 2% ribose (R), 1% carnosine (C), and 0.1% ascorbic acid (A) in combination (RCA) showed a significantly higher consumer acceptance for aroma, which could possibly be attributed to the high beef aroma intensity measured by the descriptive analysis panel. RCA had the highest color acceptance which may be related to the high a* value for the cooked sample. RCA also had high overall acceptance corresponding to "like slightly." Raw and cooked color values, shear force, pH, cook loss and drip loss percentages, and aroma and flavor attribute intensities for RCA were not significantly different from the control sample. The synergistic effect of ribose, carnosine, and ascorbic acid may positively affect the aroma and color of minced bison meat leading to higher overall acceptability without compromising sensory and physico-chemical quality.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".