Impact of Autolytic, Proteolytic, and Nisin-Producing Adjunct Cultures on Biochemical and Textural Properties of Cheddar Cheese
Bibliographic record
Abstract
The effect of incorporating a highly autolytic strain (Lactobacillus delbrueckii subsp. bulgaricus UL12) a proteolytic strain (Lactobacillus casei subsp. casei L2A), or a nisin Z-producing strain (Lactococcus lactis, subsp. lactis biovar diacetylactis UL719) into Cheddar cheese starter culture (Lactococcus lactis KB and Lactococcus cremoris KB) on physicochemical and rheological properties of the resultant cheeses was examined. Cheeses were ripened at 7 degrees C and analyzed over a 6-mo period for viable lactococcal and lactobacilli counts, pH, titratable acidity (TA), lipolysis, proteolysis, and textural characteristics. The combination of the nisin-producing strain and autolytic adjuncts significantly increased the production of water-soluble nitrogen, free amino acids, and free fatty acids. The effect of Lc. diacetylactis UL719 alone or of Lb. casei L2A on water-soluble nitrogen and free amino acid contents were also significant, whereas their effect on free fatty acids was not. Viable counts of Lb. bulgaricus UL12 were significantly reduced in the presence of Lc. diacetylactis UL719. Lactobacilli-containing cheeses showed significantly lower values for hardness, fracturability, and springiness. It could be concluded that the addition of Lb. bulgaricus UL12 together with a nisin-producing strain produces a greater increase in cheese proteolysis and an improvement in Cheddar cheese texture.
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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 | 0.001 | 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 | 0.001 |
| 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".