Determination of the presence of Escherichia coli in the peel and edible part of the banana and evaluation of its growth during post-harvest process and storage at controlled temperature
Bibliographic record
Abstract
To ensure innocuous food for consumers, agroindustries have implemented preventive practices and have developed research that supports their processes. The banana producing guild in Guatemala required to determine the presence of Escherichia coli in the peel and edible part of the fruit, and to determine the growth of this bacteria during postharvest and storage processes. The study was carried out in four banana packing plants in Guatemala with two objectives; the first one was to determine if E. coli is able to infiltrate the edible part of the banana during postharvest washing. e second objective was to determine if there is E. coli in the banana peel at stowage and in simulation of controlled temperature storage. For the first objective, two boxes of banana were randomly collected from a packing line, then were transferred to the laboratory where the conditions of the washing tank were replicated and E. coli was introduced intentionally. The most probable number method was used to analyze the maceration of the fruit. For the second objective, five boxes of bananas were randomly collected from three packing plants. Samplings were made to the peel of two bananas per box, at stowage, and then during storage at temperatures between 17 and 18° C, during the 4th and 18th day. The results indicated absence of E. coli in the edible part of the fruit and in the banana peel at stowage area, and at controlled storage temperature during the evaluation time.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".