Enhancing Pea Flour Characteristics Through Radio Frequency Heating: Effects on Composition, Flavour, Anti‐Nutrients and Functionality
Bibliographic record
Abstract
ABSTRACT Peas and other legume crops are sources of sustainable plant‐based proteins. Dry pea seeds can be processed into whole seed flour or dehulled milled flour and used as food ingredients. However, the widespread consumption of plant‐based ingredients from peas is limited by their poor sensory properties, limitations in their functionality, nutritional properties and presence of anti‐nutritional factors. Many of these negative attributes could potentially be overcome by using novel processing technologies. In this study, radio frequency (RF) pre‐treatment of whole dry yellow peas was carried out at two temperatures (85°C and 115°C) and their effects on the macromolecular composition, amino acid profiles, anti‐nutritional content, techno‐functional properties and flavour chemistry were analysed. Significant reduction in the amounts of 12 out of 18 amino acids were observed in RF‐treated samples compared to untreated control, while the other six amino acids showed a slight increase after RF treatment. Upon RF treatment, total phenolic content increased, while saponin content, trypsin inhibitor activity and lipoxygenase enzyme activity showed a reduction. Volatolomics studies revealed the generation of a number of new pyrazine class of volatile flavour compounds in situ , only in the RF treatment at the higher temperature of 115°C. Techno‐functional analysis of dehulled milled flours prepared from RF‐treated seeds showed an increase in water holding capacity, while the oil absorption capacity, foaming and emulsion properties decreased compared to untreated control. The significant findings of this investigation include the identification of a set of pyrazine compounds that enhance the flavour profile of RF‐treated seeds and an improvement in water holding capacity of the flour that can increase its utilization in meat‐analogue applications.
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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".