Evaluation of changes in the taste of cooked meat products during curing using an artificial taste sensor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it