Improving the techno‐functionality of air‐classified pea flour fine fraction by enzymatic proteolysis, followed by Maillard‐induced conjugation with available carbohydrates in the flour
Bibliographic record
Abstract
Abstract Background and Objectives Air‐classified pea protein‐enriched flour (PPEF) was hydrolyzed with trypsin and papain and heat conjugated with the carbohydrate fraction of the flour via the Maillard reaction to form protein–carbohydrate conjugates. The surface and functional properties of the conjugates were assessed thereafter. Findings The surface charge of the conjugates (trypsin and papain conjugates) (−27.5 to −32.2 mV and −33.1 to −37.5 mV) was higher than that of PPEF (−24.2 ± 0.5 mV and −27.3 ± 1.1 mV) at pH 7 and 10. Low degrees of trypsin hydrolysis, followed by conjugation resulted in enhanced foam capacity (109.2% ± 2.0%), and heated trypsin controls had the highest foam stability (76.9%) at pH 4. The emulsion activity index of the hydrolyzed protein–carbohydrate conjugates (hydrolyzed proteins conjugated with starch) was higher at pH 4 (15.9–22.2 min) as compared to pH 7 (16.5–21.3 min). Trypsin conjugates showed the highest emulsion stability (ES) at pH 4 (96.4%–98%), whereas the ES at pH 7 and 10 was almost comparable. Both oil‐holding (OHC: 1.9–3.9 g/g) and water‐holding capacities (WHC: 1.7–2.4 g/g) were significantly improved. Conclusions Maillard conjugated proteins have enhanced functional properties at pH 4, 7, and 10 as compared to PPEF. Most of the conjugates show high functionality at pH 4. It was also observed that only heating the low‐cost, raw materials produced conjugates with enhanced functional properties. Significance and Novelty Enhanced functional properties at acidic pH are indicative of their use in beverage emulsions like fruit juices and processed drinks. High WHC and OHC show their potential use in the food industry as an alternative to animal proteins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".