The effect of washing on physicochemical changes in packaged, sliced green peppers
Bibliographic record
Abstract
Abstract Whole green bell peppers (Capsicum annuum L.) were obtained through a local fresh-cut company. They were washed in a 100 ppm chlorine solution, dried and then sliced into 0.5-cm wide strips. A bulk sample of slices was divided into four treatments: (1) no wash, (2) one wash in fresh distilled water, (3) two sequential washes in fresh distilled water, and (4) three sequential washes in fresh distilled water. Retention of firmness of the pepper slices during storage at 7 °C improved with the number of washes after cutting. The modification of package atmosphere was less in all wash treatments than in the ‘no wash’ control. The sliced tissues of ‘no wash’ controls had higher levels of acetaldehyde and ethyl acetate. Analysis of the wash water revealed that sequential washes with water removed incremental amounts of acetaldehyde and soluble phenols from the cut surfaces of the tissue. These results show that washing has a dramatic effect on physicochemical measures of quality in green pepper slices, and it is likely that this effect is mediated by the removal of stress-related compounds produced during the cutting operation. While acetaldehyde and total phenolics were the two stress-related compounds measured in the wash water, it may be that other compounds removed in the wash water could have contributed to the beneficial effects on quality reported in this study.
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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 | 0.001 |
| 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".