Pulsed vacuum drying of wolfberry: Effects of infrared radiation heating and electronic panel contact heating methods on drying kinetics, color profile, and volatile compounds
Bibliographic record
Abstract
The drying kinetics and quality attributes of wolfberry were investigated under pulsed vacuum drying based on two different heating ways of far-infrared radiation (PVD-FIR) and electronic panel contact (PVD-EPC) heating. They were operated at different drying values of heating panel temperatures (60, 65, and 70°C) with 15 and 2 min as the constant vacuum pressure and atmospheric pressure duration, respectively. Drying time for wolfberry dried by PVD-FIR was lower by 17–19% compared with that by PVD-EPC at the same drying temperature. The effective moisture diffusivity (Deff) determined by Weibull distribution model ranged from 3.72 × 10−10 to 6.59 × 10−10 m2/s and 3.34 × 10−10 to 6.88 × 10−10 m2/s for PVD-FIR and PVD-EPC, respectively. The drying activation energy was 54.30 and 68.59 kJ/mol for the samples dried by PVD-FIR and PVD-EPC, respectively. The color parameters L*, a*, and b* of wolfberry dried by PVD-FIR were higher than those dried by PVD-EPC. The product dried by PVD-FIR contained more vivid luster compared to that dried by PVD-EPC. The contents of aldehydes, esters, phenols, and the heterocyclic compound in PVD-FIR sample were higher than those in PVD-EPC samples. Additionally, the alcohols, ketones, and acid contents in PVD-FIR sample were lower than those in PVD-EPC sample. In summary, PVD-FIR is more suitable for wolfberry drying as it enhances drying rate and product’s quality compared with PVD-EPC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".