Targeted Anthocyanin Enrichment of Cranberry Juice by Electrodialysis with Filtration Membranes: Impact of Filtration Membrane Physicochemical Properties and Predictive Statistical Models
Bibliographic record
Abstract
To optimize cranberry juice enrichment, correlation between physicochemical properties of filtration membranes (FM) and anthocyanin migration was investigated during electrodialysis with filtration membranes (EDFM) using redundancy (RDA) and multivariate regression (MRGA) analyses. Six polyether sulfone (PES) and polyvinylidene fluoride (PVDF) membranes with molecular weight cut-offs between 150 and 500 kDa, commercially available at large scale, were characterized in terms of nine physicochemical characteristics and used for EDFM. The highest migration of total anthocyanin was obtained with PVDF 250 kDa, with a global migration rate of 3.5 ± 0.4 g/m2·h. RDA showed that two FM properties (mesopore porosity and hydrophilic porosity) were significantly negatively correlated to the anthocyanin’s migration and explained 67.4% of their total variation in migration. Predictive MRGA models were also developed for each anthocyanin based on these significant FM properties. A combination of intermolecular interactions may lead to binding in a cooperative and synergistic mode and hinder the anthocyanin migration.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".