Effects of ethanol combined with ascorbic acid and packaging on the inhibition of browning and microbial growth in fresh‐cut Chinese yam
Bibliographic record
Abstract
The objective of this study was to investigate the effects of aqueous solutions of ethanol (25%, v/v) and ascorbic acid (AA, 1%, m/v) alone and in combination, along with modified atmosphere packaging (MAP) or vacuum packaging (VP), on the physicochemical properties and microbial quality of fresh-cut Chinese yam slices during 4°C storage. The data showed that ethanol (25%, v/v) combined with AA (1%, m/v) and MAP treatment resulted in the lowest changes for headspace gas composition, color, electrical conductivity, overall visual quality, aerobic plate count population, and mold and yeast population in fresh-cut Chinese yam over 21 days of 4°C cold storage, which was more effective at both inhibiting microbial growth and delaying browning than ethanol or AA alone or a commonly used sanitizer, sodium hypochlorite. The experiment on packaging demonstrated that samples treated with VP and 25% ethanol with 1% (m/v) AA dip can only preserve the sensory qualities of fresh-cut Chinese yam slices up to 7 days at 4°C, but 25% (v/v) ethanol in conjunction with 1% (m/v) AA packed with MAP had antibrowning potential and maintained the quality of fresh-cut Chinese yam slices up to 14 days when stored at 4°C.
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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 | 0.000 | 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 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".