Continuous and pulsed ultrasound pectin extraction from navel orange peels
Bibliographic record
Abstract
Pectin is a valuable product (up to 30 $kg−1) that makes-up 20–30% of an orange’s peel. The commercial extraction is lengthy (up to 6 h) and energy intensive as it requires heating aqueous solutions (60–100 °C). Ultrasound speeds up the extraction process reducing processing time by macroscopic and microscopic mixing by acoustic cavitation. We adopted an ultrasonic horn to deliver a rated power of 500 W at amplitudes of 20%, 40%, and 60% with and without pulsation to extract pectin from waste orange peels. These correspond to power densities of 0.08 W ml−1, 0.16 W ml−1 and 0.24 W ml−1, respectively. The extractions operated at a pH of either 2 or 3. The experimental data agree with the fitted values from the statistical model (R2=95.5%). The model confirms our predictions that yield increases with amplitude/power density and decreasing pH. The highest yield was (11%) at a pH of 2 and with continuous ultrasonic irradiation at a power density of 0.24 W ml−1. There is only a 1.3% difference between this datum and pulse ultrasound mode (1 s on/1 s off) at the same conditions — a Student’s t test confirmed that there was no significant difference in yield between continuous and pulse mode. However, pulsing is more efficient in that it consumes less than half the energy of continuous operation (80 kJ vs. 190 kJ).
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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".