ADDITION OF SORBITOL WITH KMnO<sub>4</sub> IMPROVES BROCCOLI QUALITY RETENTION IN MODIFIED ATMOSPHERE PACKAGES
Why this work is in the frame
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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 CO 2 to accumulate above the recommended 5–10%. Six treatments within MAP were investigated: (1) control − 0‐g sorbitol + 0 g‐KMnO 4 ; (2) 0‐g sorbitol + 20.0‐g KMnO 4 ; (3) 2.5‐g sorbitol + 20.0‐g KMnO 4 ; (4) 5.0‐g sorbitol + 20.0‐g KMnO 4 ; (5) 10.0‐g sorbitol + 20.0‐g KMnO 4 ; and (6) 20.0‐g sorbitol + 20.0‐g KMnO 4 . 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 KMnO 4 after 29 days of storage at 0–1C (0.21 versus 0.00–0.02 μ L/L with KMnO 4 and/or sorbitol), while ethanol content was greater in both control bags and those with only KMnO 4 (0.12–0.13 versus 0.00–0.06 μ L/L with sorbitol). Chlorophyll fluorescence (Φ PSII ) 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 KMnO 4 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it