Effect of Xanthan and Arabic Gums on Foaming Properties of Pumpkin (Cucurbita pepo) Seed Protein Isolate
Bibliographic record
Abstract
<p>Understanding how foaming properties of proteins are affected by factors such as pH, salt concentration and temperature is essential in predicting their performance and utilisation. In this study, the effects of pH and salt concentration were studied on the foaming properties of pumpkin seed protein isolate (PSPI) and PSPI- xanthan (XG)/Arabic (GA) gum blends. The foaming properties of the PSPI-GA/XG blends were also compared with egg white. Foam stability (FS) was significantly affected by pH with PSPI: GA (25:4) and PSPI: XG (25:1) having a significantly higher stability at pH 2 with the lowest foam stability at pH 4. Sodium chloride (0.2-1.0 M) did not significantly affect foaming properties although PSPI: GA (25:4) had the highest FC (89.33 ± 3.24%) and FS (76.83 ± 1.53 min) at 0.2 M sodium chloride concentration. The foaming capacity (FC) of PSPI: GA (25:4) blend (128.00 ± 0.91%) was significantly higher (<em>p </em><em>&lt; </em>0<em>.</em>05) than that of egg white (74.00 ± 1.33%) but its FS was significantly lower. It was further revealed that the FC of egg white (74.00 ± 1.33%) was comparable to the PSPI:XG (25:1) blend (74.00 ± 1.46%) but the FS for egg white (480.00 ± 2.67 min) was significantly higher (<em>p </em><em>&lt; </em>0<em>.</em>05) than the FS (116.21 ± 0.86 min) of PSPI:XG (25:1). The foaming properties of PSPI and PSPI-xanthan (XG)/Arabic (GA) blends were significantly affected by pH. Optimum foaming properties, PSPI:XG (25:1) and PSPI:GA (25:4) were observed at pH 2 and heat treatment temperature of 80 ºC.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".