<i>IN VITRO</i> STUDIES TO CONTROL THE GROWTH OF MICROORGANISMS OF SPOILAGE AND SAFETY CONCERN IN HIGH‐MOISTURE, HIGH‐pH BAKERY PRODUCTS
Bibliographic record
Abstract
ABSTRACT Initial agar plate studies were done to determine the effects of various levels (0 to 2,000 ppm) of potassium sorbate (KS) and sorbic hydroxamic acid (SHA) over a wide pH range (5 to 9) on the growth of microorganisms of spoilage and safety concern in high‐moisture, high‐pH bakery products. While growth of most microorganisms was inhibited for > 28 days on agar plates containing ∼1,000 ppm of KS at pH 5 and incubated at 30C, growth of all microorganisms occurred in plates at pH 7 and 9, regardless of the concentration of KS. SHA was equally effective at pH 5, however, it proved to be a more effective inhibitor against most microorganisms at higher pH (9). Subsequent agar plate studies were done with water‐ethanol (WE) and mastic oil‐ethanol (ME) emitters. While WE emitters failed to control the growth of all microorganisms under investigation, ME emitters controlled the growth of most microorganisms, with the exception of Listeria monocytogenes, for ∼12 to 28 days on agar plates packaged in high‐gas‐barrier Cryovac or metallized bags, respectively. Inhibition was not simply due to the levels of ethanol, which ranged from ∼1.2 to 2.8% v/v, but rather, the mastic volatiles in the package headspace. This study has demonstrated the potential of SHA and ME emitters to control the growth of several microorganisms of spoilage and safety concern in high‐moisture, high‐pH bakery products. However, the type of packaging material influenced the antimicrobial efficacy of this vapor‐phase inhibitor.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".