Effectiveness of Ozonated Water for Preserving Quality and Extending Storability of Star Ruby Grapefruit
Bibliographic record
Abstract
The aim of this study was to explore the impact of aqueous ozone technology on maintaining grapefruit flavor and freshness by minimizing the occurrence of postharvest deterioration. During the 2018 and 2019 seasons, Star Ruby grapefruit fruits were treated with 0.3 and 0.6 ppm aqueous ozone for 5 and 10 min after harvest at water temperatures of 5 °C and 15 °C, respectively. The fruits were stored for 40 days at 8 ± 1 °C with 85–90% relative humidity. The results revealed that all the ozonated water treatments reduced physiological weight loss, disease infection, and decay, as well as providing long-term protection to the fruits throughout storage. The best treatment for preserving the postharvest quality was 0.6 ppm ozonated water at 5 °C for 5 min, which successfully delayed ripening while concurrently preserving the TSS/acid ratios, total phenolics, and antioxidant activity. Overall, aqueous ozone treatment is a promising example of a treatment that is beginning to be utilized on a commercial scale. In accordance with the findings of this study, it can be deduced that aqueous ozone can be used to maintain fruit quality, reduce postharvest diseases, and extend storage life.
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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) | 0.001 | 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".