Application of yellow mustard gum in preparation of egg-free mayonnaise
Bibliographic record
Abstract
Abstract Yellow mustard gum (YMG) is a natural hydrocolloid extracted from the viscous fraction of the seed coat, a by-product of mustard seeds, and possesses superior emulsifying capacity in emulsions. However, there is no application of YMG for commercial products. The demand for vegan mayonnaise (VM) has increased in recent years and the challenge is to maintain the original consistency and taste while replacing eggs from conventional mayonnaise. To obtain high-quality VM and to investigate the potential of YMG as a food additive, YMG was applied to VM at 0%, 0.2%, 0.4%, 0.6%, 0.8%, and 1.0% (w/w) in this study. The physical properties (index of stability, rheological properties and particle size characterisation), storage stability (visual stability, microstructure observation and cold tolerance) and texture of YMG-formulated VM were measured and compared with commercial products to assess the quality and market prospects. The results showed that the VM obtained higher stability, more non-Newtonian flow behaviour, and a smaller droplet size with higher YMG content. After 90 days of storage, no separation occurred in the YMG-added VM. YMG improves the shelf-life of VM, and mayonnaise formulated with YMG has comparative or better texture properties compared with commercial counterparts. Overall, YMG-added VM could be more appealing to consumers favouring plant-based foods and have the potential to substitute for traditional products. YMG is beneficial to vegan product development with clean-label requirements.
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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".