Inhibition of Yeast in Commercial Pickle Brines
Bibliographic record
Abstract
This study investigated the inhibition of yeasts in brines from fermented cucumber pickles using 2, 4-hexadienoic (sorbic), hexanoic and (E)-3-hexenoic acids. Native yeast population and chemical composition of commercial brines were analyzed and the minimum inhibitory concentrations of inhibitors on yeast growth were established. Commercial brines were treated with 100-350 ppm of 2, 4-hexadienoic (sorbic), hexanoic and (E)-3-hexenoic acids individually and at 2.5 to 10% salt (sodium chloride) concentrations. Yeast populations in the treated brines were monitored for 30 days of incubation. Hexanoic and (E)-3-hexenoic acids at 350 ppm caused reduction in yeast populations by about 4 and 2 log CFU/ml, respectively, within 24 hours of treatment. However, when brines were treated with 2, 4-hexadienoic acid at salt concentrations of 7.5 to 10%, there were no significant differences noted in yeast inhibition between the three acids. Hexanoic and (E)-3-hexenoic acids at 200 ppm caused longer lasting inhibitory effects (30 days) on yeasts than the traditionally used 2, 4-hexadienoic acid (10 days) in fermentation brine. Thus, the hexanoic and (E)-3-hexenoic acids are potential alternatives to 2, 4-hexadienoic acid for controlling yeasts during storage of spent cucumber fermentation brines.
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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 | 0.000 | 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".