Effects of sugar addition on adipolysis and volatile profiles of dry cured sausage
Bibliographic record
Abstract
As sugar play a primary role in food flavor and biochemical reaction during the industrial manufacture of dry cured meat products , we aim to evaluate the function of sugar and how the quality changed with the addition of sugar on dry cured sausage after three months of preservation, and the corresponding effect on acidity value and the total acid value. The roles of sugars’ effects on FA hydrolysis and flavor formation are required to be assessed. We selected the addition of sugar with the gradient contents from 3% to 15%, we found that the total acid, acidity value, peroxide value , FA and organic acid were significantly affected. Mono unsaturated fatty acids (MUFA) was the main content of FAs, following by saturated FA and polyunsaturated FA. C 18:1n9c was the major FA, following by C 16:0 and C 18:0 . Six kinds of organic acids in dry-cured sausages containing different concentrations of sugar were analyzed by HPLC and the total organic acid reached a maximum of 12.15% by after adding 6% sugar. Acetic acid was 8.06% and lactic acid was 2.56%. This study concludes that sugar is the main factor in flavor formation of dry cured sausage and affects the sausage quality for three months in storage.
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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) | 0.000 | 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 | 0.001 |
| 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".