Extraction of pectin from Elephant Apple and Pomelo fruit peels: Valorization of fruit waste towards circular economy
Bibliographic record
Abstract
Citrus fruit peels are used as the primary source for pectin production, effectively transforming fruit waste into a valuable resource and making a significant contribution to fostering a circular economy. This study aimed to extract pectin from locally available citrus fruits such as Elephant Apple, inner peel (IP), and outer peel (OP) of Pomelo. Pectin was extracted using acid hydrolysis and sodium-hexa-meta-phosphate extraction, and its physiochemical properties, including moisture, ash, methoxyl content, equivalent weight, anhydrouronic acid (AUA), and degree of esterification, were analyzed. The sodium-hexa-meta-phosphate method yielded higher average pectin percentages (6.73±3.4%) than the acid hydrolysis technique (3.65±1.36%), with Pomelo IP showing the highest yield (10.84±0.19%), followed by Elephant Apple (6.37±0.10%) and Pomelo OP (2.97±0.11%). The pectin powder from Pomelo IP using the sodium-hexa-meta-phosphate method exhibited low moisture (7.36±0.07%) and ash (3.07±0.06%), high esterification (75.56±0.09%), and satisfactory methoxyl (7.48±0.02%), AUA (56.18±0.05%), and equivalent weight (920.67±3.51mg/mol). The extracted Pomelo peel pectin exhibited favorable physicochemical properties, indicating strong potential for stable storage and efficient gel formation, making Pomelo IP a promising source for pectin extraction.
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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".