Analysis of volatile profiles and taste characteristics in sous-vide cooked chicken breast based on HS-SPME-GC-MS and E-tongue
Bibliographic record
Abstract
Sous-vide (SV) cooking is a practical strategy to improve the texture of chicken breast products. Nevertheless, there has been minimal research on the impact of SV cooking conditions on the volatile profiles and taste characteristics of chicken breast. This study aimed to analyze the texture, volatile profiles and taste characteristics in SV cooked samples as affected by temperature-time combinations, by employing HS-SPME-GC-MS and E-tongue coupled with PCoA, PCA and PERMANOVA analysis. SV samples had lower cooking loss and shear force than traditional boiling (TB) samples. A total of 95 volatiles were identified in all raw and heated samples, and SV70 samples (SV cooking at 70 °C for 45 and 90 min) had the highest concentration of 1-octen-3-ol, 2-pentanone and total esters. TB and SV samples cooked exceeding 75 °C had more aldehydes than other samples. E-tongue results indicated increasing umami values of SV samples as temperature and time grew, and SV70 samples displayed the lowest bitterness and astringency values. PERMANOVA analysis showed that temperature rather than time caused the difference of taste characteristics among samples. Therefore, SV cooking at 70 °C can effectively improve the volatile profiles and taste attributes of chicken breasts.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".