Dried berry pomace as a source of high value-added bioproduct: drying kinetics and bioactive quality indices
Bibliographic record
Abstract
Drying kinetics and bioactive quality indices (total phenolics, flavonols, anthocyanins, and antioxidant activity) of fermented Merlot grape, cranberry, highbush blueberry, and wild lowbush blueberry pomace were evaluated. Thin layer drying experiments were performed at two loading densities (kg m−2) at 50, 60, and 70°C in a cabinet convection air dryer. Phenolics composition and antioxidant activity of both cabinet convection air dried pomace and freeze dried pomace were assessed. The effective moisture diffusivity (Deff) and activation energy (Ea) values were calculated for each pomace type subjected to cabinet convection air drying and experimental drying data from these experiments were modeled with the Newton/Lewis, Henderson-Pabis, and Page equations. This work showed that levels of phenolic compounds and antioxidant activity in pomace subjected to cabinet convection air drying at certain conditions were generally comparable to levels in freeze dried pomace. Results indicated that subjecting the berry pomace to shorter processing times upon cabinet convection air drying (half load density at 70°C) results in better bioactive quality retention. Thus this method could be used to generate dried berry pomace which could be a source of bioactive compounds with potential use as a value added product in the food industry and other industries.
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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".