Cold plasma pretreatment improves the quality and nutritional value of ultrasound-assisted convective drying: The case of goldenberry
Bibliographic record
Abstract
Nutrient damage and high energy consumption are the challenges of convective drying to achieve food security and economic stability. Wild berries have high nutritional value, but they are difficult to dry because of the waxed skin tissue. Such a cellular structure is highly resistant to mass transfer, which increases drying time and nutrient degradation. Although chemical pretreatments can facilitate a mass transfer, they reduce the amounts of soluble nutrients. As an alternative, we propose an innovative strategy with cold plasma pretreatment followed by ultrasound-assisted convective drying. Cold plasma pretreatment enabled reducing drying temperature from 60–90 °C to 50 °C, which improved the nutritional quality of the dried goldenberries. The application of ultrasound energy significantly reduced drying time. Compared to the untreated convective dried samples, the vitamin C retention, antioxidant activity, and total phenolic content increased by up to 175.07%, 84.32%, and 52.31%, respectively. This drying approach can significantly contribute to food security by improving product quality, nutritional value, shelf stability, and reducing greenhouse gas emissions.
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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".