Effect of pulse type and concentration of yellow pea and black Beluga lentil purees on their physical characteristics and on their foaming properties with or without citric acid addition
Bibliographic record
Abstract
The aim of this study was to determine the effect of the type of pulse and concentration of yellow pea and black Beluga lentil purees on their physical characteristics and on their foaming properties with or without citric acid addition. Pulse purees were produced with 5.0, 7.5, 10.0 and 12.5 wt% pulses. Textural properties, flow behavior, and particle size of pulse purees were measured. Purees were whipped with or without 0.2 % of citric acid (C.A) to investigate the pH effect on their foaming properties. Overrun was measured at different times during the whipping process to establish the overrun kinetic. The foam was also characterized by oscillatory shear measurements at the end of the whipping process. Textural parameters and consistency index of purees increase with puree concentration and these parameters are generally higher for pea purees. Lentil purees have a higher protein content and generally produce foams with higher overruns. The overrun is higher when the foam is made from the 7.5 wt% pulse purees and it decreases for purees at 10.0 and 12.5 wt%. Particles of 10.0 and 12.5 wt% pulse purees may contribute to increasing the stability of the foam after 24h storage at 4 °C. The addition of C.A. increases the overrun of all foams but negatively impacts foam stability. Puree concentration has proven to be a lever to modulate pulse puree properties. An optimal concentration would allow having enough surface-active molecules to form the foam and enough particles to stabilize the film around the air bubbles.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".