Effect of Lactobacillus plantarum Fermentation on the Surface and Functional Properties of Pea Protein-Enriched Flour
Bibliographic record
Abstract
fermentation on the functional and physicochemical properties of pea protein-enriched flour (PPF) was investigated. Over the course of the fermentation the extent of hydrolysis increased continuously until reaching a maximum degree of hydrolysis of 13.5% after 11 h. The resulting fermented flour was then adjusted to either pH=4 or 7 prior to measuring the surface and functional attributes as a function of fermentation time. At pH=4 surface charge, as measured by zeta potential, initially increased from +14 to +27 mV after 1 h of fermentation, and then decreased to +10 mV after 11 h; whereas at pH=7 the charge gradually increased from -37 to -27 mV over the entire fermentation time. Surface hydrophobicity significantly increased at pH=4 as a function of fermentation time, whereas at pH=7 fermentation induced only a slight decrease in PPF surface hydrophobicity. Foam capacity was highest at pH=4 using PPF fermented for 5 h whereas foam stability was low at both pH values for all samples. Emulsifying activity sharply decreased after 5 h of fermentation at pH=4. Emulsion stability improved at pH=7 after 5 h of fermentation as compared to the control. Oil-holding capacity improved from 1.8 g/g at time 0 to 3.5 g/g by the end of 11 h of fermentation, whereas water hydration capacity decreased after 5 h, then increased after 9 h of fermentation. These results indicate that the fermentation of PPF can modify its properties, which can lead towards its utilization as a functional food ingredient.
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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".