Recent developments in high efficient freeze-drying of fruits and vegetables assisted by microwave: A review
Bibliographic record
Abstract
Microwave heating has been applied in the drying of high-value solids as it affords a number of advantages, including shorter drying time and better product quality. Freeze-drying at cryogenic temperature and extremely low pressure provides the advantage of high product quality, but at very high capital and operating costs due partly to very long drying time. Freeze-drying coupled with a microwave heat source speeds up the drying rate and yields good quality products provided the operating unit is designed and operated to achieve the potential for an absence of hot spot developments. This review is a survey of recent developments in the modeling and experimental results on microwave-assisted freeze-drying (MFD) over the past decade. Owing to the high costs involved, so far all applications are limited to small-scale operations for the drying of high-value foods such as fruits and vegetables. In order to promote industrial-scale applications for a broader range of products further research and development efforts are needed to offset the current limitations of the process. The needs and opportunities for future research and developments are outlined.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".