Assessment of the effect of lactic acid fermentation on the gastroduodenal digestibility and immunoglobulin E binding capacity of soy proteins <i>via</i> an <i>in vitro</i> dynamic gastrointestinal digestion model
Bibliographic record
Abstract
Fermentation by lactic acid bacteria is helpful in reducing soy protein immunoreactivity. However, how lactic acid fermentation influences the gastroduodenal digestibility and immunoglobulin E (IgE) binding capacity of soy proteins remains unclear. In this study, the protein digestion of a fermented soybean protein isolate (FSPI) was investigated and compared with that of a soybean protein isolate (SPI). The effect on their respective IgE binding capacities at the gastric and duodenal phases was also explored by using a novel in vitro dynamic gastrointestinal digestion model (Bionic Rat Model II+). Medium pH was measured, microstructural analysis was performed, peptide distribution and free amino acid content were determined, and SDS-PAGE analysis was performed to assess the differences between SPI and FSPI. The results showed that FSPI had lower pH (3.76), larger protein aggregates (>60 μm), and higher low-molecular-weight peptides than SPI. During the first 30 min of gastric and duodenal digestion, the extent of hydrolysis of FSPI was higher than that of SPI, and the gastric transition time of the former was longer than that of the latter. Conversely, differences tended to be narrower in the next 30-180 min of gastric and duodenal digestion. As a result, the IgE binding capacity of FSPI was significantly lower than that of SPI at 30 min of gastric and duodenal digestion. Therefore, fermentation by lactic acid bacteria affected the digestibility rate of soy proteins, especially at the initial phases of gastric and duodenal digestion, thereby reducing the exposure of intact epitopes in the duodenum. This study helped to elucidate how lactic acid fermentation affected the digestive behavior of soy proteins and its implication in IgE immunoreactivity reduction.
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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) | 0.001 | 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".