Evaluation of changes in the taste of cooked meat products during curing using an artificial taste sensor
Bibliographic record
Abstract
The purpose of this study was to assess an evaluation method using an artificial taste sensor, in comparison with chemical analysis and sensory evaluation of the taste of meat during curing. Samples of Canadian pork were treated with salt, nitrite and phosphate. Curing time ranged from 0 to 168 h. In the sensory evaluation, there were no significant differences in the all characteristic items at 72-h cured sample compared to the 0-h sample. Some of the characteristic items for the 168-h sample (umami, overall taste, richness and overall palatability) showed significant difference (P < 0.05) compared to the 0-h sample. Taste sensor analysis indicated that the sensor outputs of bitterness and saltiness were significantly correlated with curing time (R = 0.98 and 0.97, respectively), and total free amino acids (R = 0.91 and 0.96, respectively). The sensor output of bitterness was significantly correlated (R = 0.96) with the sum of amino acids corresponding to bitter taste. The increase in the chemical components contributing to bitterness and/or saltiness was indicated as the cause of the characteristic taste. Taste sensor analysis may be applicable as a qualitative method for evaluating taste characteristics generated during the curing of manufactured cooked meat products.
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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 | 0.001 |
| 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 | 0.000 |
| 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".