Osmotic Dehydration of Apple Cylinders: II. Continuous Medium Flow Heating Conditions
Bibliographic record
Abstract
Mass transfer of apple cylinders during osmotic dehydration was quantitatively investigated under continuous medium flow conditions. The influences of the main process variables (solution concentration, operation temperature, contact time, and solution flow rate) were determined. A second-order polynomial regression model was used to relate weight reduction (WR), moisture loss (ML), solids gain (SG), and mass diffusivity (D m and D s ) to process variables. The conventional diffusion model using a solution of Fick's unsteady state law involving a finite cylinder was applied for moisture diffusivity and solute diffusivity determination. Diffusion coefficients were in the range of 10−9–10−10 m2/s, and moisture diffusivity increased with temperature and flow rate, increased with solution concentration (> 50°Brix), and decreased with increasing solution concentration (< 50°Brix), but solids diffusivity increased with temperature and concentration and decreased with increasing flow rate. A continuous-flow osmotic dehydration (CFOD) contactor was developed to be a more efficient process in terms of osmotic dehydration efficiency: time to reach certain weight reduction (T w ) and moisture loss (T m ) were shorter than that of conventional osmotic (COD) dehydration processes. Effectiveness evaluation functions used in this study could be widely applied to osmotic dehydration system evaluation.
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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".