Developing thermally stable beverage emulsions using mildly fractionated pea proteins
Bibliographic record
Abstract
Abstract Oil‐in‐water emulsions are widely used as the base flavoring or clouding agents in various beverages. Pulse proteins can play a major role as a natural emulsifier in beverages. However, the presence of insoluble components greatly minimizes their potential application in beverage emulsions. In this work, pea protein concentrate was mildly fractionated by aqueous centrifugation to recover a soluble fraction with 71% protein yield, which was then used to develop 5% oil‐in‐water emulsions using a high‐pressure homogenizer. Emulsion stability was tested by heat treatment (90°C, 30 min) in the presence of NaCl (0–1 M) at pH 7.0 and 2.0. Stability increased upon the addition of salt at pH 7, while at pH 2, proteins and droplets aggregated. Heat treatment led to extensive aggregation at both pH values, which was further worsened by salt. To prevent thermal destabilization, the proteins were heat‐treated at 75°C for 30 min for partial denaturation before emulsification under hot conditions. The heat‐treated protein‐stabilized emulsions at pH 7 had superior thermal stability at all salt concentrations without aggregation. However, a similar improvement was not observed at pH 2. Pre‐heating the soluble protein exposed the hydrophobic patches, leading to better adsorption on the droplet surface, which did not show additional aggregation upon further heating the emulsions at pH 7. Interestingly, heat‐treated protein‐stabilized emulsions showed a 44% drop in lipid digestibility compared to the original emulsions. The proposed approach could be a valuable addition to the utilization of pea proteins in beverage emulsions that could withstand heat treatment during food processing.
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 | 0.001 |
| 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".