Hot Acid Extraction, Characterisation and Scavenging Activity of Pectin from Hylocereus polyrhizus
Bibliographic record
Abstract
Gelatin is extensively added to the food products for quality improvements of food entities. The role of gelatin as food thickener, texturizer, stabilizer, ingredient and as an animal based source has restricted its liberal use. However, the usage of this animal-based food quality improver has become less popular due to religious constraints and health restrictions. In fact, it is now direly needed to replace animal-based gelatin by plant-based. Pectin, the basic building material of cell walls in the terrestrial plant has great potential to be gelatin replacer as it can work as a gelling agent, thickener and also a stabilizer. Dragon fruit contains pectin which has high-value functional food as well as healthenhancing properties to substitute gelatin’s function in foods production. The current study aims to extract pectin from dragon fruit peels by using hot acid extraction. The optimum conditions for extraction were found to be at 75ºC and pH 3.5 based on the highest percentage of pectin yield (33%). The FTIR result proved that dragon fruit peel contained pectin, which can be used as gelatin replacer are free from any religious and health-wise prohibitions. Pectin extracted was characterized in terms of moisture (14.03 ± 1.925) and ash content (8.73 ± 1.218). The extracted pectin of dragon fruit peel acts as the best gelatin replacer compared to commercial pectin and gelatins from the market. The prepared fruit peels also exhibit high DPPH scavenging activity (57.94%) with methanol extract (2mg/ml).
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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 | 0.001 | 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".