FERMENTATION AND CHEMICAL ACIDIFICATION OF SALAMI‐TYPE PRODUCTS – EFFECT ON YIELD, TEXTURE AND MICROSTRUCTURE
Bibliographic record
Abstract
ABSTRACT The effects of using lactic acid bacteria (LAB), liquid lactic acid and encapsulated acids (lactic, citric and glucono‐delta‐lactone [GDL]) to reduce the pH of salami‐type products were investigated. Liquid lactic acid caused an immediate pH drop, crumbling of meat particles and some moisture separation. This type of precooking protein denaturation resulted in the lowest springiness value and showed a microstructure with gaps among the cooked meat particles. The encapsulated acids did not produce such problems, and their effects on cook loss and hardness values varied. The nonacidified control (pH 5.60) provided the highest hardness value and low cook loss, because of less disruption to protein gelation during cooking. The slow acid release, during the overnight LAB fermentation, resulted in some binding prior to cooking, but a higher cook loss compared to the encapsulated acids.
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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 | 0.000 |
| 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".