Cryogenic Pretreatment Enhances Drying Rates in Whole Berries
Bibliographic record
Abstract
The impact of cryogenic pretreatments on drying performance was studied in blueberries, seabuckthorn fruits and green grapes. The fruits were immersed in liquid nitrogen in 2 min freezing/thawing cycles (one to five). Untreated samples were used as the control. Drying experiments were carried out on treated and non-treated berries at 50 °C and 1 m/s (hot-air-drying), 50 °C and 25″ Hg vacuum (vacuum-drying), 30 mTorr total pressure and 25 °C shelf temperature (freeze-drying). The weight loss evolution of the foodstuffs was measured as a function of time. Microscopic (SEM and optical) determinations of the epicarp were performed. A visual inspection was performed and color changes and volume reductions were assessed before and after dehydration. The thickness of the berries' epicarp decreased between 20 and 50% (depending on the fruit) after 3-5 immersions in liquid N2. The drying kinetics was accelerated significantly for the three tested drying processes (i.e., drying time decreased from 48 to 16 h for blueberry freeze-drying). The best quality of dried berries was observed for pretreated blueberries after freeze-drying, keeping their volume, shape and color after the process. This work shows that "tailor-made" dried berry products with desired properties can be achieved and drying performance can be improved by the application of ultra-low temperature pretreatments.
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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".