Development of Modified LA broth for Selectively Detecting Lactic Acid Bacteria from Foods Containing Various Microorganisms
Bibliographic record
Abstract
While lactic acid bacteria are used in the production of fermented foods, they are also known to contaminate foods and cause spoilage, characterized by a sour taste and odor. Despite the availability of numerous commercially available media for lactic acid bacteria detection, their selectivity is low, allowing for the growth of microorganisms other than lactic acid bacteria. In this study, we investigated modifications to LA broth, an effective broth for detecting lactic acid bacteria, with the aim of expanding its use by selectively detecting lactic acid bacteria. First, we investigated the concentration of organic acid salts added to the broth using the bacterial strains. Next, the selectivity of the modified LA broth supplemented with organic acid salts for lactic acid bacteria was evaluated using commercially available foods and swab samples from plants. It was observed that the addition of sodium acetate and potassium sorbate to the LA broth created conditions for the detection of lactic acid bacteria, without interference from any other microorganisms. Based on these results, the modified LA broth was considered useful for the selective detection of lactic acid bacteria.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".