Optimizing wild blueberry juice production: Sensory, physicochemical, and phytochemical characteristics
Bibliographic record
Abstract
ABSTRACT The goal of this study is to help small-scale producers improve the quality of wild blueberry juice by identifying practical, optimized processing conditions that enhance its sensory and nutritional attributes. Valued for their health benefits, wild blueberries are typically frozen due to their short harvest season and later processed into juice. Using an experimental design approach, we evaluated the effects of thawing method, extraction technique, enzymatic treatment, and pasteurization time on juice quality. Sensory analysis identified 14 descriptors, including desirable notes such as blueberry aroma and citrus and undesirable notes such as an earthy flavor, which is linked to volatile compounds like 3-octan-1-ol and 2-(E)-decenal. Pectinase treatment increased juice yield by up to 91 % and anthocyanin content by up to 4,287 mg/L, though it also intensified astringency and sourness. °Brix values ranged from 10.6 to 14.0, and turbidity varied with processing. Principal component analysis revealed distinct sensory profiles between experimental and commercial juices. The optimal conditions, oven thawing at 40 °C for eight minutes, traditional extraction, pectinase addition, and pasteurization at 90 °C for one minute, offer a practical framework for producing high-quality 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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".