Effect of high‐intensity ultrasound on the physicochemical properties and nanostructure of citrus pectin
Bibliographic record
Abstract
BACKGROUND: Modified pectin has been found to have various biological activities. The preparation of modified pectin is generally accomplished by either chemical or enzymatic depolymerisation processes, but both methods have several disadvantages. Ultrasound treatment is simple and requires shorter times and lower temperatures than conventional techniques used for processing plant materials. In recent years the application of ultrasound to modify polysaccharides has received increasing attention. The objective of this study was to use ultrasound to modify citrus pectin. RESULTS: The average molecular weight of citrus pectin decreased under different ultrasonic conditions. The average molecular weight decreased from 464 to 296 kDa after 30 min of sonication. The degree of methylation of citrus pectin changed slightly and its monosaccharide component remained unchanged when high-intensity ultrasound was applied. The reduced (Gal+Ara)/Rha ratio after ultrasonication suggested degradation in the neutral sugar side chains of citrus pectin. Atomic force microscopy results confirmed the degradation of citrus pectin chains by ultrasound at nanolevel. CONCLUSION: Ultrasound is an effective way to pretreat or modify pectin. The degradation of citrus pectin is due to the cavitational effects of ultrasound. Thus ultrasound may be useful in establishing environmentally friendly extraction and modification technologies for pectin.
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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) | 0.001 | 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".