Microwavevacuum and osmotic drying of cranberries
Bibliographic record
Abstract
CRANBERRIESModem food industry dictates strict conditions on energy use and application, preventing unnecessary energy dissipation.Energy demanding processes such as distillation and drying have to be optimised to the highest extent, while retaining or improving the final product quality.Pretreatments to drying can be used in order to optimize drying, and sorne of cranberry pretreatments such as chemical, mechanical and osmotic dehydration were optimized.Chemical pretreatment consisted of dipping cranberries into solution of ethyl oleate and sodium hydroxide at different temperatures, and process times.Mechanical pretreatment was cutting of berries into halves or quarters.Tested parameters for osmotic dehydration were the duration of process, osmotic agent type and its concentration.Once the appropriate pretreatment was selected, cranberries were subjected to hybrid drying under subatmospheric pressure and using microwaves as an energy source.Evaluated process parameters were microwave power level, microwave power mode, and the operating pressure of process.This drying method showed good potential, but in order to verify the results obtained, it was compared to microwave/convective drying.Slight advantages of the miçrowave/vacuum process over the microwave/convective process were apparent in almost all product quality parameters, as weIl as in process efficiency.
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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) | 0.002 | 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".