Physicochemical properties, structural characteristics, and rheological behavior of mango peel pectin with different degrees of esterification
Bibliographic record
Abstract
• Purification reduced protein, ash, and impurities in mango peel pectin (MP). • De-esterified mango peel pectins (DEMPs) was prepared by alkaline method. • De-esterification enhanced solubility, water-holding capacity, dispersibility and surface texture of DEMPs. • Ca2+ improved elasticity and gelation properties of DEMPs. Raw mango peel pectin (RMP) was extracted from mango peel with a yield of 12%. The RMP was subsequently purified to obtain mango peel pectin (MP) and de-esterified to obtain de-esterified mango peel pectins (DEMPs) with degrees of esterification (66.26% - 33.67%). The moisture content (4.48% - 5.36%), ash content (3.31% - 4.57%), protein content (4.14% - 9.51%), total sugar content (35.74% - 36.84%), solubility, water-holding capacity (WHC) and colour of RMP, MP, and DEMPs were systematically analyzed. The content of galacturonic acid in all DEMPs samples was higher than 65% and above the commercial grade. Notably, the highly de-esterified sample (DEMP 4 ) demonstrated the lowest molecular weight (342.37 KDa), the smallest particle size (1278.67 nm ± 27.65 nm) and the highest zeta potential (-36.09 mV), indicating its strongest dispersibility and stability in solutions. Scanning electron microscopy showed that the DEMP 4 was highly porous, which exhibited superior solubility and WHC. Rheological analysis showed that although DEMP 4 had the lowest consistency coefficient (0.133 Pa·s n ), the addition of Ca 2+ enhanced its elasticity and gelation properties. These results demonstrated that de-esterification significantly improved the physicochemical properties, structural morphology, and rheological behavior of MP, and provided a theoretical basis for the development and utilization of mango peel 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".