Characterization of aromatic properties of old-style cheese starters
Bibliographic record
Abstract
Old-style cheese starters were evaluated to determine their ability to produce cheese aroma compounds. Detailed analyses of the aroma-producing potential of 13 old-style starter cultures were undertaken. The proteolytic profile of the starters was established by an accelerated ripening study using a model cheese slurry and compared with those of a commercial aromatic starter and commercial Cheddar cheeses. To evaluate the aromatic potential of the starter cultures, quantification of free amino acids liberated and volatile compounds after 15 d of ripening at 30 degrees C as well as sensory analysis were carried out. Results showed that proteolysis patterns of all 13 starter cultures in the curd model were comparable to those of commercial Cheddar cheeses. All tested cultures demonstrated the ability to produce high amounts of amino acids recognized as precursors of aroma compounds. Several differences were observed between the starters and commercial Cheddar cheeses regarding some amino acids such as glutamate, leucine, phenylalanine, proline, and ornithine, reflecting the various enzymatic systems present in the starters. Starters Bt (control) and ULAAC-E exhibited various significant differences regarding their free amino acid profiles, as confirmed by sensory analysis. In addition, identification of volatile compounds confirmed the presence of several key molecules related to aroma, such as 3-methylbutanal and diacetyl. Besides the aroma-producing aspect, 2 starters (ULAAC-A and ULAAC-H) seem to possess an important ability to generate large amounts of gamma-aminobutyric acid, which contributed up to 15% of the total amino acids present in the model curd after 15 d ripening. gamma-Aminobutyric acid is an amine well-known for its antihypertensive and calming effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".