Interactions and complex formation between pea proteins (Pisum sativum L.) and betanin from red beet (Beta vulgaris L.) extract – effect of foam fractionation
Bibliographic record
Abstract
Pea proteins show promise as vegan-friendly food ingredients, where their functional properties can be affected by extraction and purification processes. Red beets are a source of betanin, a natural colorant that is susceptible to degradation, limiting its application. This study examined the interactions between pea protein flour (PPF) proteins and betanin in red beet extract (RBE) and assessed whether foam fractionation could improve the functional properties and thermal stability of PPF-RBE complexes. Spectrophotometric, thermal, and morphological analyses indicated that both non-foamed and foamed proteins formed complexes with RBE. The secondary and tertiary structures of PPF proteins were likely altered and their flexibility was increased due to unfolding during foam fractionation and subsequent pH adjustment. Circular dichroism and Fourier transform infrared spectra indicated that non-foamed PPF proteins had an α-helical structure, which was more pronounced in the foamed PPF proteins. After interacting with RBE, the foamed PPF-RBE complex exhibited β-sheet structuring that was more pronounced than the non-foamed complex, resulting in improved functional properties such as soluble protein content, foaming, and emulsifying abilities. This increase in β-sheet structure might have also improved the thermal stability of foamed PPF-RBE complex. Scanning electron microscopy showed larger particles in the non-foamed and foamed PPF-RBE complexes than in the respective protein controls. Foamed proteins, which may have had more phenolic hydroxyl groups, also had the highest antioxidant activity. Consequently, foam-fractionated PPF-RBE complexes appear to be promising ingredients for commercial products with improved functional properties.
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".