Vacuum microwave dehydration decreases volatile concentration and soluble protein content of pea (<scp><i>Pisum sativum</i></scp> L.) protein
Bibliographic record
Abstract
Abstract BACKGROUND Peas are an inexpensive yet nutritious and sustainable source of protein. However, it is challenging to incorporate pea proteins into food formulations owing to their beany or green off‐flavours and their limited water solubility. RESULTS Vacuum microwave dehydration (VMD) of pea protein with an initial moisture content of 425% (dry basis, db) at 2 W g−1 specific microwave energy and 200 Torr vacuum level for 88 min led to an 83% reduction in total volatile compound concentration. VMD processing at high initial moisture contents facilitated the Maillard reaction, enhancing the extent of protein cross‐linking, leading to a marked decrease in soluble protein content, to 11 g kg−1. Reducing the initial moisture content to 56% db greatly retained protein solubility (112–113 g kg−1), but it only led to a minor reduction in total volatile compound concentration (2–11% reduction). A high microwave energy (20 W g−1)–short time (2 min) treatment at 200 Torr vacuum level was found optimal, reducing both volatile levels and soluble protein content by ~50%. CONCLUSION Evidently, it is difficult to employ VMD without reduction of pea protein solubility and corresponding changing in functionality. Yet, if optimized, VMD has the capability to decrease volatile concentrations while retaining protein solubility. Future sensory analysis should be conducted to determine whether the aforementioned reductions in total volatile compound concentration may have a notable effect on consumer palatability. © 2020 Society of Chemical Industry
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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 | 0.000 |
| 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".