ADDITION OF SORBITOL WITH KMnO<sub>4</sub> IMPROVES BROCCOLI QUALITY RETENTION IN MODIFIED ATMOSPHERE PACKAGES
Bibliographic record
Abstract
ABSTRACT The objective of this study was to determine if the addition of sorbitol (a water absorbent) to packages containing potassium permanganate (KMnO 4 ) on an inert carrier (a volatile adsorber) could be used to enhance the removal of volatiles associated with off‐odors in broccoli (Brassica oleracea L., Italica group) during storage in modified atmosphere packaging (MAP). Broccoli heads were held in PD‐961EZ bags (five per bag), to allow CO2 to accumulate above the recommended 5–10%. Six treatments within MAP were investigated: (1) control − 0‐g sorbitol + 0 g‐KMnO4; (2) 0‐g sorbitol + 20.0‐g KMnO4; (3) 2.5‐g sorbitol + 20.0‐g KMnO4; (4) 5.0‐g sorbitol + 20.0‐g KMnO4; (5) 10.0‐g sorbitol + 20.0‐g KMnO4; and (6) 20.0‐g sorbitol + 20.0‐g KMnO4. Broccoli heads in MAP with sorbitol had better appearance, firmness and odor ratings after 29 days of storage at 0–1C, compared to the controls. Furthermore, the odor rating was higher (less off‐odor) as the amount of sorbitol was increased (1.9 control versus 3.9 for 20‐g sorbitol). Slight increases in weight loss (≤1.3%) of the broccoli were also noted with the addition of sorbitol, although not near an amount that would affect marketability. Acetaldehyde concentrations were higher in the control bags with no sorbitol or KMnO4 after 29 days of storage at 0–1C (0.21 versus 0.00–0.02 μL/L with KMnO4 and/or sorbitol), while ethanol content was greater in both control bags and those with only KMnO4 (0.12–0.13 versus 0.00–0.06 μL/L with sorbitol). Chlorophyll fluorescence (ΦPSII) began to decrease more rapidly in the control broccoli, as the ethanol and acetaldehyde began to accumulate. Overall, the use of sorbitol (≥2.5 g) with KMnO4 in MAP could enhance the removal of volatiles that are responsible for off‐odors and off‐flavors in broccoli, and thus maintain the quality and marketability longer.
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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".