Impact of glycan content on emulsifying and emulsion-stabilizing properties of Maillard-type soy protein isolate-maltodextrin conjugates
Bibliographic record
Abstract
Soy protein isolate (SPI) generally does not possess strong emulsifying activity and faces challenges in functioning as an effective food emulsifier. Maillard-type conjugates of SPI and maltodextrin (MD) have exhibited enhanced emulsifying and emulsion-stabilizing properties. While the impact of glycation on protein functionality has been established, the specific role of varying glycan content in modulating emulsifying and stabilizing properties remains an area requiring further investigation. Therefore, this study prepared four Maillard-type SPI-MD conjugates with varying glycan contents (expressed as grams of maltodextrin per 100 g of protein: 12.44 ± 0.60 g/100 g for M0, 8.10 ± 0.19 g/100 g for M1, 4.88 ± 0.25 g/100 g for M2, and 2.21 ± 0.09 g/100 g for M3) using β-amylase. Turbidimetric analysis showed a positive correlation between emulsifying activity and MD content, with all conjugates outperforming native SPI (15.52 ± 0.17), ranging from 16.54 ± 0.18 (M3) to 22.78 ± 1.18 (M0). Furthermore, rheological and droplet size analysis further showed M0 had the highest emulsifying capacity and stability compared to all other conjugates and native SPI. These improvements are likely attributable to increased water solubility, surface hydrophobicity, zeta potential, and flexible tertiary structures in the SPI-MD conjugates. This study provides fundamental insights into how glycan content influences the structure and functional properties of glycated SPI, offering valuable guidance for the rational design of food-grade emulsifiers.
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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) | 0.000 | 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".