Dehydrated fruits and vegetables using low temperature drying technologies and their application in functional beverages: a review
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
Although people's perception of fruits and vegetables is green, healthy and nutritious, the consumption of fruits and vegetables for most people around world still does not meet the WHO’s recommendations for a healthy diet. Functional foods and beverages containing functional ingredients with health-improving properties, are gaining increasing popularity among consumers and the food industry. Either hydrous or dried fruit and vegetables formulated into beverages can promote the daily intake and is a splendid delivery approach for nutrients and bioactive compounds to human body. Drying is the good method to preserve fruit and vegetables characterized by high moisture content and perishability. However conventional drying processes are strongly associated with high temperature, which is detrimental to their nutritional and sensory qualities. This work aims to review low temperature drying technologies for fruits and vegetables to better maintain the qualities and expand their application in functional beverages.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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