Effects of Polysaccharide Supplementation on Lactic Acid Bacteria-Fermented Soy Protein Gel: Structural Characteristics, Allergenicity, and Epitope Analysis
Bibliographic record
Abstract
BACKGROUND: Soy allergy is an important nutritional and health issue that needs to be addressed. Lactic acid bacteria (LAB) fermentation is an effective approach to reduce soy protein allergy. Polysaccharides are commonly used in LAB-fermented products to enhance their textural properties. This study proposes a new strategy for developing hypoallergenic soy protein products. METHODS: We prepared a soy protein isolate (SPI) through fermentation with LAB (FSPI) and with five types of polysaccharides supplementation, namely polydextrose (PDX), inulin (IN), long-chain inulin (LCIN), soluble soy polysaccharides (SSPS), and β-glucan (BG). The texture and microstructure of different samples were analyzed. Antigenicity and IgE-binding capacity were determined using ELISA. Finally, peptide sequencing was used to identify the degradation degree and frequency of allergenic epitopes. RESULTS: Samples with added PDX (F-PDX) and IN (F-IN) exhibited lower hardness; smaller, irregular pores; and a honeycomb structure, whereas samples with SSPS (F-SSPS) and BG (F-BG) had higher hardness; large, regular pores; and strong sheet structures. The antigenicity and IgE-binding capacity of F-PDX and F-IN were lower both before and after 120 min of in vitro dynamic gastrointestinal digestion. The peptidomics results indicated that F-PDX and F-IN primarily facilitated the degradation of the glycinin G1 and G2 subunits, β-conglycinin α, and the β subunit. Moreover, it increased the frequency of destruction of allergenic epitopes, and further promoted the degradation of epitopes in the external α-helix structures of glycinin and β-conglycinin compared to FSPI. CONCLUSIONS: The addition of polysaccharides had a significant impact on the structure and allergenicity of the soy protein gel, especially PDX and IN.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".