COLOR DEVELOPMENT DURING NATURAL FERMENTATION AND CHEMICAL ACIDIFICATION OF SALAMI‐TYPE PRODUCTS
Bibliographic record
Abstract
ABSTRACT The impact of using natural fermentation with lactic acid bacteria (LAB), chemical acidification with liquid lactic acid, and encapsulated citric, gluconic or lactic acid were evaluated in raw and cooked salami‐type products. Liquid lactic acid resulted in an immediate pH drop and significant increase in L*(lightness) and a*(redness) values, as well as clumping of the ground meat particles that exhibited moisture release due to excessive protein denaturation. Overnight LAB fermentation also resulted in increased L*and a*values, but unlike direct acid addition, did not cause moisture loss or clumping. Spectra data, collected after both treatments reached pH 4.6, revealed pretty similar curve shape, with higher values for the fermented product at 420–550 nm and 610–670 nm. The encapsulated acids, designed to release acid at about 62C, did not affect the color of the raw meat batters. Cooking of all treatments resulted in higher L*and a*values, both by about 50%. L*and a*values were pretty similar for all the acidified and non‐acidified control. The only exception was a no‐nitrite control, which showed a significantly (P < 0.05) lower a*value. PRACTICAL APPLICATIONS The use of different acidification methods to lower the pH of meat products (bacteria fermentation, direct acid addition, encapsulated acid) does affect the color of the product. When going through the process, care should be given to the rate of acidification. A slow acid production (fermentation) or acid release when the meat proteins have started to go through heat coagulation (by using acid encapsulated in hydrogenated vegetable oil) are recommended. Direct acid addition results in immediate lightening of the product as well as crumbling of the ground meat particles, which later negatively affect color and texture of the product.
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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".