Influence of Microwave Osmotic Dehydration Pre-Treatment on the Second Stage of Air-Drying Kinetics of Apples
Bibliographic record
Abstract
The effect of microwave-osmotic dehydration pre-treatment under continuous flow medium spray (MWODS) conditions on the second stage air-drying kinetics of apple (Red Gala) cylinders was evaluated. MWODS pre-treatment was carried out using a response surface methodology involving 5-levels of sucrose concentration (33-66.8°B), temperature (33-66.8°C) and contact time (5-55 min). Drying time and coefficient of moisture diffusion (Dm) and coefficient of moisture infusion (Im) during rehydration were evaluated as responses and the results were compared with their air-dried (AD) (worst scenario) and freeze-dried (FD) (best scenario) counterparts without the osmotic treatments. The diffusion and infusion coefficients were based on the solution of Fick's diffusion model. Empirical models developed for all response variables were significant (P ? 0.001) and the lack of fit was not significant (P > 0.05). MWODS pre-treatments significantly influenced the Dm values and reduced the air-drying time of apples by 30-65 percent in comparison with untreated apple thereby providing opportunity for better energy savings. On the other hand, the values of Im during the rehydration process were highest for the freeze-dried samples followed by apples air-dried after MWODS treatment, and the least for the untreated air-dried samples.
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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".