Grape Drying: Current Status and Future Trends
Bibliographic record
Abstract
With high moisture and sugar content, fresh grapes respire and transpire actively after harvest, which contribute to quality loss. Drying can process grapes into raisins for longer shelf-life as well as dehydrated grapes, which can be used for wines or juice production. The pre-treatments, drying method and drying conditions, can significantly influence the quality of final products. In this chapter, firstly, different pre-treatments as a necessary operation previous to the drying of grapes into raisins is introduced. These pre-treatments include chemical pre-treatment, physical pre-treatment, and blanching. In addition, the quality and drying characteristics of different pre-treatments is summarized too. Secondly, the current status of different technologies for grape drying and their effects on drying kinetics and quality attributes of seedless grapes are described to highlight the advantages and disadvantages of each drying method. These drying methods include the traditional open sun drying, shade drying, hot-air drying, freezing drying, microwave drying, as well as the vacuum impulsed drying. Thirdly, influences of drying on bioactive substances (flavonoids, phenolics, anthocyanin, and resveratrol) and antioxidant capacity of grape by-products including seed, skin, stem, and stalk are also examined. Finally, the future research trends of grape and its by-product drying are indentified and discussed.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".