Grape Drying: Current Status and Future Trends
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it