Deacidification of concentrated cranberry juice by electrodialysis with bipolar membranes: A feasibility and comprehensive study
Bibliographic record
Abstract
Electrodialysis with bipolar membranes (EDBM) recently shown promising outcomes as a novel green technology to deacidify cranberry juice without altering its polyphenolic compounds, responsible for many health benefits. The performance of EDBM depends on many factors, including the concentration of the treated solution. While previous research only deacidified cranberry juice by EDBM with concentration of 8°B, the present study aimed to assess the physicochemical attributes of more concentrated juices and the effect of using specific ones (5°B, 15°B, 25°B and 35°B) during EDBM on deacidification and electrodialytic parameters evolution. Optimal juice characteristics for EDBM use (viscosity, conductivity) were exhibited at 25°B. The 4 selected juice concentrations were successfully deacidified by 39.0 ± 2.5 % but with significantly different process times (from 46 ± 2 min to 471 ± 16 min). Extended treatment duration led to a slight loss in anthocyanins and proanthocyanidins, likely due to increased polyphenols-membranes interactions, which contributed to system resistance growth. However, the reduction in citric (32 ± 3 %) and malic (61 ± 4 %) acids remained consistent, regardless of the juice concentration. This work confirmed the feasibility of producing a concentrated and deacidified cranberry juice by EDBM, that can represent a great potential to the functional food market.
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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 | 0.001 |
| 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".