Low‐Temperature Blanching as a Tool to Modulate the Structure of Pectin in Blueberry Purees
Bibliographic record
Abstract
Blueberry composition was characterized for 6 cultivars. It contains a good amount of dietary fiber (10% to 20%) and pectin (4% to 7%) whose degree of methylation (DM) is sensitive to food processing. A low temperature blanching (LTB: 60 °C/1 h) was applied on blueberry purees to decrease pectin DM, in order to modulate puree properties and functionalities (that is, viscosity and stability), and to enhance pectin affinity toward other components within food matrices. Fiber content, viscosity, pectin solubility, DM, and monosaccharide composition were determined for both pasteurized, and LTB+pasteurized blueberry purees. The results showed that neither the amount of fiber, nor the viscosity were affected by LTB, indicating that this treatment did not result in any significant pectin depolymerization and degradation. LTB caused a decrease both in pectin DM from 58-67% to 45-47% and in the amount of water-soluble pectin fraction, the latter remaining the major fraction of total pectin at 52% to 57%. A LTB is a simple and mild process to produce blueberry purees with mostly soluble and low-methylated pectin in order to extend functionality and opportunities for interactions with other food ingredients.
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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 | 0.001 |
| 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".