Protein isolates derived from pea seeds pre-treated with radio frequency: Changes in global proteomes and impacts on nutritional and functional properties
Bibliographic record
Abstract
Thermal treatments are used to modify the functionality and sensory properties of proteins. Specifically, radio frequency (RF) heating is fast, scalable, penetrates deeply, and is suitable for treating low-moisture materials such as dry seeds. In this study, RF heating was applied as a pre-treatment to whole dry yellow pea seeds, and its impact was assessed on pea protein isolates produced from the seed. Global quantitative proteomics revealed a reduction in the abundance of proteins such as albumins, lipoxygenase, peroxidase, and non-specific lipid transfer protein, with a corresponding increase in globulins and lectins. Circular dichroism spectroscopy analysis indicated alterations in protein secondary structures. These RF-induced changes drastically affected the functional and nutritional properties of the protein isolates. At RF treatment temperature of 84±3.5°C, fat absorption capacity increased from 570±19% in the control treatment to 721±19% ( p <0.05), emulsion stability index increased from 19.5±0.1 min to 21.2±0.6 min ( p <0.05), foaming capacity increased from 168±1.5% to 174±2.6% ( p <0.05) and foam expansion increased from 869±2% to 892±2.1% ( p <0.05). These changes were accompanied by a downgrading of nutritional properties due to the depletion of sulfur amino acid-containing albumins. • The impact of RF seed pre-treatment on pea protein isolates was studied • LowRF and HighRF treatments induced protein unfolding and further aggregation at higher heat treatment • Fat absorption, emulsion and foaming capacity were improved after RF treatment at 84°C • RF treatment decreased albumins and sulfur-containing amino acids, lowering nutritional • quality • RF treatment at 114°C decreased solubility and extraction efficiency
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".