Soy protein processing impacts the ice recrystallization inhibition activity of protein hydrolysates
Bibliographic record
Abstract
Abstract Soy protein hydrolysates have demonstrated moderate ice recrystallization inhibition (IRI) activity, but the properties of the unhydrolyzed protein contributing to this activity are not well known. The objective of this research was to identify the main protein processing factors important to the varying IRI activities observed. Three possible modification reactions were applied to soy protein isolate (SPI): the Maillard reaction that can occur in soy flour before protein isolation, heat denaturation of the fully defatted protein, and heat denaturation of the protein with the presence of residual lipid, mainly polar phospholipids. These modified proteins were hydrolyzed by Alcalase protease (for 2 min) to produce hydrolysates, which were characterized by HPLC and FTIR to investigate how molecular weight (MW) and changes in secondary structure relate to IRI activity. A multilinear regression with the parameters of modification type, surface hydrophobicity, secondary structure profile, and average MW of the hydrolysates was used to investigate their roles in reducing ice crystal size. It was discovered that polar lipids present in the soy hydrolysate samples and MW were the only significant factors ( p < 0.05) for IRI activity of SPI hydrolysates which had an ice crystal size reduction from 22.0% to 36.7%. This study demonstrates for the first time a protein hydrolysate‐phospholipid interaction can produce IRI active molecules.
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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 | 0.001 |
| 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".