Effect of Revtech thermal processing on volatile organic compounds and chemical characteristics of split yellow pea ( <i>Pisum sativum</i> L.) flour
Bibliographic record
Abstract
Yellow pea (Pisumsativum L.) is an economically rich source of nutrients with health-promoting effects. However, the consumption of pea ingredients is minimal due to their off-flavor characteristics. The present study investigated the effect of Revtech heat treatment on the chemical profile and volatile compounds in split yellow pea flour. Revtech treatment (RT) was applied at 140°C with a residence time of 4 min in dry condition (RT 0%) and in the presence of 10% steam (RT 10%). Both thermal treatments resulted in a significant reduction (p < 0.05) in lipoxygenase activity and the concentration of key beany-related odors such as heptanal, (E)-2-heptenal, 1-octen-3-ol, octanal, and (E)-2-octenal. In addition, RT 10% resulted in a significant reduction in pentanal, 1-penten-3-ol, hexanal, and 1-hexanol compared to untreated flour. The content of known precursors of lipoxygenase such as linoleic and linolenic acids was found in higher concentrations in heat-treated flours, indicating the efficacy of Revtech technology in minimizing the degradation of polyunsaturated fatty acids. No significant changes in the amino acid composition or the 29 selected phenolic compounds in pea flours were observed with Revtech processing except for two compounds, caffeic acid and gallocatechin, which were found at higher concentrations in RT 0%. PRACTICAL APPLICATION: Thermal processing of split yellow pea flours at 140°C using Revtech technology successfully decreased the concentrations of volatile compounds responsible for beany off-flavor while improving the nutritional quality of studied yellow pea flours. These results provide valuable information to the food industry for developing novel pulse-based products with enhanced sensory characteristics.
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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".