Invited review: Starter lactic acid bacteria survival in cheese: New perspectives on cheese microbiology
Bibliographic record
Abstract
The importance of starter cultures to cheese manufacture and ripening is well known. Starters are inoculated into cheese milk at a level of ∼10 6 cfu/mL either from a bulk culture or using commercial direct-to-vat cultures. Before ripening, starters grow in the milk to reach populations of 10 7 to 10 9 cfu/g of curd depending on processing variables such as cook temperature, inclusion of washing steps, degree of partitioning with curds and whey, and importantly salt addition rate. Inherent strain-related properties also determine final populations in the curd following manufacture and include temperature sensitivity, salt sensitivity, presence of prophage, autolytic and permeabilization properties (which are influenced by processing steps), presence and type of cell envelope proteinase, and metabolic activity. Ripening of important industrial cheese varieties such as Cheddar, Dutch, Swiss, and Italian-type cheese varieties is characterized by extended storage under temperature-controlled conditions enabling characteristic flavor and texture development to occur. Over ripening, microbiological, biochemical and enzymatic changes occur with a decline in starter viability, release of intracellular enzymes, hydrolysis of proteins, carbohydrates and lipids, and formation of a range of volatile and nonvolatile flavor components. Recent reports suggest that starter strains may be present during the later stages of ripening and therefore their potential role needs to be reconsidered. This review will focus on our current understanding of starter viability and vitality during cheese ripening and will also review the area of starter permeabilization, autolysis, and enzyme release.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".