Intensification of atmospheric freeze drying for thin food slices with impinging jet
Bibliographic record
Abstract
Atmospheric freeze drying obviates the complexities and costs of maintaining a high vacuum for freeze drying. One of the main drawbacks of atmospheric freeze drying is the low sublimation rate, which is restricted by drying temperatures possible at ambient pressure to prevent food products from softening during dehydration. There is a strong need to intensify the process to reduce the total drying time. This study evaluated the feasibility of using impinging jets to enhance the mass transfer characteristics of the atmospheric freeze drying process. Atmospheric freeze drying experiments on thin lamb slices between −3 to −7 °C showed that the impinging jet configuration has a significant effect in improving the rate of mass transfer flux compared to the conventional cross-flow configuration. This was consistent across different thicknesses of the lamb slices, which would have presented different degrees of internal resistance to mass transfer. Given the amount of non-frozen water in the lamb slices at the drying temperature range evaluated, a scheduled switch from cold air atmospheric freeze drying to a mild hot air drying condition was explored. This strategy enhanced the removal of the remaining water content in the lamb slices, mainly the non-frozen water.
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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 | 0.001 |
| 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".