Acceleration of pH Variation in Cloudy Apple Juice Using Electrodialysis with Bipolar Membranes
Bibliographic record
Abstract
The purpose of this study was to accelerate pH variation in cloudy apple juice using electrodialysis (ED). The testing was conducted using two ED configurations. The bipolar and cationic membrane configuration showed that reducing the spacing from 8 to 0.75 mm had little effect on treatment time, whereas stacking eight bipolar membranes reduced acidification time by 30%, although the treatment still took too long (21 min). Furthermore, it was not possible to acidify apple juice to a pH of 2.0 to completely inhibit enzymatic browning. The bipolar and anionic membrane configuration helped to accelerate the acidification step by a factor of 3, increasing the yield from 3.3 to 10 L of juice/m(2) membrane/min. Moreover, treatment time was inversely proportional to the size of the membrane stack. The speed at which the pH of acidified juice returned to its initial value was, however, 4 times slower than the speed of acidification, giving a yield of 2.5 L of juice/m(2) membrane/min. By accelerating the acidification step, ED treatment with bipolar and anionic membranes results in more effective polyphenol oxidase activity and more rapid control of juice browning at pH 2.0. Also, the treatment has very little effect on the chemical composition and organoleptic quality of apple juice.
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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) | 0.000 | 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".