Proanthocyanidins and volatile aroma of cranberry juice are modulated by its microbiota and processing environment
Bibliographic record
Abstract
The quality and sensory attributes of juices are influenced by their natural microbiota and the microorganisms found on filtration membranes. This study aimed to assess the influence of natural microbiota and specific contaminants, including Candida krusei, Rhodotorula mucilaginosa, Debaryomyces prosopidis, Ralstonia insidiosa, and Lactiplantibacillus paraplantarum, isolated from cranberry juice and its associated industrial filtration membranes, on the characteristics of cranberry juice. Their growth kinetics and impacts on total phenols, total anthocyanins, total proanthocyanins, total organic acids, pH, titratable acidity, and volatile compounds were assessed. During the 42 h fermentation period, Candida krusei and Ralstonia insidiosa exhibited significant growth, increasing by 1-log and 3-log, respectively. The natural microbiota led to a 7% and 6% reduction in anthocyanins and proanthocyanidins, while Candida krusei and Rhodotorula mucilaginosa caused losses of 10% and 7% in proanthocyanidins, respectively. Organic acid content remained stable, except for an 8% decrease caused by Ralstonia insidiosa. Volatile compounds underwent significant increases, particularly in green (703%), winey (100%), mushroom (306%), and fusel (2678%) notes. These findings underscore the rapid impact of microorganisms from natural microbiota and filtration membranes on cranberry juice characteristics, highlighting the importance for beverage industries to prioritize customer safety and satisfaction.
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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 | 0.001 | 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".