Effect of variety and drying temperature on physicochemical quality, functional property, and sensory acceptability of dried onion powder
Bibliographic record
Abstract
In Ethiopia, onion is one of the most important vegetables/spices produced as a source of cash income and for flavoring foods. However, postharvest loss during storage remains a major challenge. In this study, the effects of variety (Bombay red, Qellafo and Sweet carolin) and drying temperature (Fresh, 50, 60, 70, 80, and 90°C) on physicochemical quality, functional property, and sensory acceptability of dried onion powder were determined. The results indicated that total color change of Bombay red was not affected by temperature, but Qellafo and Sweet carolin varieties showed an increase in color change as drying temperature increases. Bulk density, shrinkage ratio, and water hydration capacity increased with increasing temperature for all three varieties. The degradation of vitamin C, pyruvic acid, and desired sensory attributes increased with increasing oven drying temperature. All in all, Qellafo dried at 70°C for 5 hr was found to be desirable for production of dehydrated onion powder. The findings of this study will allow the identification of the best of the three commonly grown onion varieties in Ethiopia, and the preferred temperature for production of dried onion with minimal negative effect on physicochemical, functional, nutritional, and sensory properties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".