Effect of Pectinolytic Enzyme Pretreatment on the Clarification of Cranberry Juice by Ultrafiltration
Bibliographic record
Abstract
Cranberries, mainly processed as juice, have garnered interest over the past decade due to their high content of phytochemical compounds related to promising health benefits. To meet consumer expectations, a juice clarification step is usually incorporated to remove suspended solids. The use of pectinolytic enzyme and membrane processes are commonly applied to the production of clarified juices, but no studies have been done on cranberry juice. In this study, the effects of 60 (D60) and 120 min (D120) of depectinization by pectinolytic enzymes coupled to clarification by ultrafiltration (UF) (membrane molecular weight cut-off (MWCO) of 50, 100 and 500 kDa) was evaluated on the filtration performance, membrane fouling and cranberry juice composition. Compared to fresh juice, depectinization for 60 and 120 min reduced the UF duration by 16.7 and 20 min, respectively. The best filtration performance, in terms of permeate fluxes, was obtained with the 500 kDa MWCO UF membrane despite the highest total flux decline (41.5 to 57.6%). The fouling layer at the membrane surface was composed of polyphenols and anthocyanins. Compared to fresh juice, anthocyanin decreased (44% and 58% for D60 and D120, respectively) in depectinized juices whereas proanthocyanidin (PAC) content increased by 16%. In view of the industrial application, a 60 min depectinization coupled to clarification by a 500 kDa UF membrane could be viewed as a good compromise between the enhancement of filtration performance and the loss of polyphenols and their fouling at the membrane surface.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".