Effects of Hot Water, Submergence Time and Storage Duration on Quality of Dragon Fruit (Hylocereus polyrhizus)
Bibliographic record
Abstract
This study was conducted to determine the effects of hot water temperature, time of submergence and storageduration on dragon fruit (Hylocereus polyrhizus). Fresh harvested dragon fruits were treated with hot watertemperatures at 35, 45 and 60 ?C and time of submergence for 15, 30 and 60 minutes for 0, 5, 10 and 15 days ofstorage. The result showed that the hot water temperature significantly affected (p<0.05) the percentage ofweight loss, titratable acidity (TA) and firmness of dragon fruit. The highest percentage of weight loss (12.80 %)was the fruit treated with hot water at 60°C and submergence for 60 minutes whereas the lowest percentage ofweight loss (5.05 %) was the fruit treated with hot water at 35 °C for 60 minutes. Dragon fruit treated with hotwater at 35 °C has high percentage of TA, 2.44 %. Fruit which was submergence in hot water at 35 °C showedthe highest fruit firmness. The result also showed that the interaction between the three factors significantlyaffected (p<0.05) the percentage of weight loss, TA and pH of the fruits. TA decreased and pH increased for thefruit treated with hot water at 35 °C and submergence for 60 minutes. The interaction between hot watertemperature at 35 °C and time of submergence for 60 minutes effectively reduced the weight loss and acidity ofthe dragon fruit (p<0.05). The shelf life and quality of dragon fruits can be extended using proper submergencetime of hot water treatment.
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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".